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Evolutionary game theory and multi-agent reinforcement learning

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  • DeepMind and University of Liverpool

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In this paper we survey the basics of Reinforcement Learning and (Evolutionary) Game Theory, applied to the field of Multi-Agent Systems. This paper contains three parts. We start with an overview on the fundamentals of Reinforcement Learning. Next we summarize the most important aspects of Evolutionary Game Theory. Finally, we discuss the state-of-the-art of Multi-Agent Reinforcement Learning and the mathematical connection with Evolutionary Game Theory.
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Evolutionary Game Theory and Multi-Agent Reinforcement
Learning
Karl Tuyls11 and Ann Now´e2
1Theoretical Computer Science Group, Hasselt University, Diepenbeek, Belgium
E-mail: karl.tuyls@uhasselt.be or k.tuyls@cs.unimaas.nl
2Computational Modeling Lab, Vrije Universiteit Brussel, Brussels, Belgium
E-mail: asnowe@info.vub.ac.be
Abstract
In this paper we survey the basics of Reinforcement Learning and (Evolutionary) Game Theory,
applied to the field of Multi-Agent Systems. This paper contains three parts. We start with an
overview on the fundamentals of Reinforcement Learning. Next we summarize the most important
aspects of Evolutionary Game Theory. Finally, we discuss the state-of-the-art of Multi-Agent
Reinforcement Learning and the mathematical connection with Evolutionary Game Theory.
1 Introduction
In this paper we describe the basics of Reinforcement Learning and Evolutionary Game Theory,
applied to the field of Multi-Agent Systems. The uncertainty inherent to the Multi-Agent
environment implies that an agent needs to learn from, and adapt to, this environment to be
successful. Indeed, it is impossible to foresee all situations an agent can encounter beforehand.
Therefore, learning and adaptiveness become crucial for the successful application of Multi-agent
systems to contemporary technological challenges as for instance routing in telecom, e-commerce,
robocup, etc. Reinforcement Learning (RL) is already an established and profound theoretical
framework for learning in stand-alone or single-agent systems. Yet, extending RL to multi-agent
systems (MAS) does not guarantee the same theoretical grounding. As long as the environment
an agent is experiencing is Markov2, and the agent can experiment enough, RL guarantees
convergence to the optimal strategy. In a MAS however, the reinforcement an agent receives,
may depend on the actions taken by the other agents present in the system. Hence, the Markov
property no longer holds. And as such, guarantees of convergence do no longer hold.
In the light of the above problem it is important to fully understand the dynamics of
reinforcement learning and the effect of exploration in MAS. For this aim we review Evolutionary
Game Theory (EGT) as a solid basis for understanding learning and constructing new learning
algorithms. The Replicator Equations will appear to be an interesting model to study learning
in various settings. This model consists of a system of differential equations describing how a
population (or a probability distribution) of strategies evolves over time, and plays a central role
in biological and economical models.
In Section 2 we summarize the fundamentals of Reinforcement Learning. More precisely, we
discuss policy and value iteration methods, RL as a stochastic approximation technique and some
1Note that as of October 1st 2005, the first author will move to the University of Maastricht, Institute
for Knowledge and Agent Technology (IKAT), The Netherlands. His corresponding adress will change
into k.tuyls@cs.unimaas.nl
2The Markov property states that only the present state is relevant for the future behavior of the learning
process. Knowledge of the history of the process does not add any new information.
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convergence issues. We also discuss distributed RL in this section. Next we discuss basic concepts
of traditional and evolutionary game theory in Section 3. We provide definitions and examples of
the most basic concepts as Nash equilibrium, Pareto optimality, Evolutionary Stable Strategies
and the Replicator Equations. We also discuss the relationship between EGT and RL. Section 4
is dedicated to Multi-Agent Reinforcement Learning. We discus some possible approaches, their
advantages and limitations. More precisely, we will describe the joint action space approach,
independent learners, informed agents and an EGT approach. Finally, we conclude in Section 5.
2 Fundamentals of Reinforcement Learning
Reinforcement learning (RL) finds its roots in animal learning. It is well known that we can
teach an animal to respond in a desired way by rewarding and punishing it appropriately. For
example we can train a dog to detect drugs in people’s luggage at customs by rewarding it each
time it responds correctly and punishing it otherwise. Based on this external feedback signal the
dog adapts to the desired behavior. More general, the objective of a reinforcement learner is to
discover a policy, i.e. a mapping from situations to actions, so as to maximize the reinforcement
it receives. The reinforcement is a scalar value which is usually negative to express a punishment,
and positive to indicate a reward. Unlike supervised learning techniques, reinforcement learning
methods do not assume the presence of a teacher who is able to judge the action taken in a
particular situation. Instead the learner finds out what the best actions are by trying them out
and by evaluating the consequences of the actions by itself. For many problems the consequences
of an action become not immediately apparent after performing the action, but only after a
number of other actions have been taken. In other words the selected action may not only affect
the immediate reward/punishment the learner receives, but also the reinforcement it might get in
subsequent situations, i.e. the delayed rewards and punishments. Originally, RL was considered
to be single agent learning. All events the agent has no control over, are considered to be part
of the environment. In this section we consider the single agent setting, in Section 4 we discuss
different approaches to multi-agent RL.
2.1 RL and it’s relationship to Dynamic Programming
From a mathematical point of view RL is closely related to Dynamic Programming (DP). DP is
a well known method to solve Markovian Decision Problems (MDP) [Ber76]. An MDP is a multi-
stage decision problem for which an optimal policy must be found, i.e. a policy that optimizes
the expected long term reward. Usually the expected discounted cumulative return is considered.
However other measures do exist [Bel62]. The Markovian property assures that the learner can
behave optimal observing it’s current state only, i.e. there is no need to keep track of the history,
so the learner does not need to know how it got there. The DP techniques are usually classified
into two approaches: the policy iteration approach and the value iteration approach. The same
classification is used in RL, and each RL approach can be viewed as an asynchronous, model free
approach of it’s DP counter part. Before we present the two RL classes, we briefly introduce the
DP counter parts. DP is a model based approach, so it assumes that a model of the environment
is available. In general the environment is stochastic, and it’s response is described by a transition
matrix. This matrix gives the probability with which the next state st+1 will be reached and a
reward rt+1 will be received, given the current state is st, and the action taken is at. This is
represented by
Pa
ss=P{st+1 =s|st=s, at=a}(1)
which are called the transition probabilities. Now, given any state and action sand a, together
with any next state s, the expected value of the next reward is,
Ra
ss=E{rt+1|st=s, at=a, st+1 =s}(2)
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Important here to note is that we assume that the Markov property is valid. This allows us
to determine the optimal action based on the observation of the current state only. Below we
introduce the two approaches in DP: policy iteration and value iteration, and introduce their RL
counter parts.
2.1.1 Policy iteration in DP
The policy iteration approach considers a current policy π, and tries to locally improve the policy
based on the state-values that correspond to the current policy π. More formally
Vπ(s) = Eπ{rt+1 +γrt+2 +γ2rt+3 +...|st=s}(3)
=Eπ{rt+1 +γV π(st+1 )|st=s}(4)
It is well known that the V
π(s) , with πthe optimal policy, are the solutions of the Bellman
optimality equation given below:
V(s) = maxaX
s
Pa
ss[Ra
ss+γV (s)] (5)
The Vπ(s) can be calculated using successive approximation as follows:
Vk+1(s) = Eπ{rt+1 +γVk+1(st+1)|st=s}(6)
=X
a
π(s, a)X
s
Pa
ss[Ra
ss+γVk(s)] (7)
To locally improve the policy in a given state s, the best action ais looked for based on the
current state values Vk(s). So πis improved in state s, by updating π(s) into the action that
maximises the right hand side of equation 7, yielding an updated policy π. The policy iteration
algorithm is given below:
Algorithm 1 Policy Iteration
Choose a policy πarbitrarily
- loop
-π:= π
- Compute the value function Vof policy π:
- solve the linear equations:
-Vπ(s) = PsPπ(s)
ss[Rπ(s)
ss+γV π(s)]
- Now improve the policy at each state:
(I) π(s) = argmaxaPsPa
ss[Ra
ss+γV π(s)]
- until π=π
2.1.2 Policy iteration in RL
Because for a reinforcement learning approach we usually assume that the model is not available,
we cannot improve the policy locally using equation (I) from Algorithm 1. Instead Policy iteration
RL techniques build up their own internal evaluator or critic. Based on this internal critic the
appropriateness of an action for a state is evaluated. An architecture realising this type of learning
is given in Figure 1.
It was first proposed in [Bar83] and generalised in [And87]. Since then it has been adopted by
many others. The scheme in Figure 1 contains two units, the evaluation unit and the action unit.
The former is the internal evaluator, while the latter is responsible for determining the actions
which look most promising according to the internal evaluation. The evaluation unit yields an
estimate of the current Vπ(s) values. In [Bar83] this component is called the adaptive critic
element. Based on the external reinforcement signal rt, and its own prediction, the evaluation
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Evaluation unit action unit
Internal
reinforcement
r
processr
External
reinforcement
a
s
Process’ state
Figure 1 An architecture for policy iteration reinforcement learning.
unit adjusts its prediction on-line as follows:
V(st) = V(st) + ζ(rt+γV (st+1 )V(st)) (8)
Where ζis a positive constant determining the rate of change. This updating rule is the so called
temporal difference, TD(0), method of [Sut88]. As stated above, the goal of the evaluation unit
is to transform the environmental reinforcement signal rinto a more informative internal signal
r. To generate the internal reinforcement, differences in the predictions between two successive
states are used. If the process moves from a state with a prediction of lower reinforcement into a
state with a prediction of higher reinforcement, the internal reinforcement signal will reward the
action that caused the move. In [Bar83] it is proposed to use the following internal reinforcement
signal:
r(t) = r(t) + γV (st+1 )V(st) (9)
Given the current state, the action unit produces the action that will be applied next. Many
different approaches exist for implementing this action unit. If the action unit contains a mapping
from states to actions, the action that will be applied to the system can be generated by a two
step process. In the first step the most promising action is generated, this is that action to which
the state is mapped. This action is then modified by means of a stochastic modifier S. This second
step is necessary to allow exploration of alternative actions. Actions that are ”close” to the action
that was generated in the first step, are more likely to be the outcome of this second step. This
approach is often used if the action set is a continuum. If an action that was applied to the system
turned out to be better than expected, i.e. the internal reinforcement signal is positive, then the
mapping will be shifted” towards this action. If the action set is discrete, a table representation
can be used. Then the table maps states to probabilities of actions to be selected for a particular
state, and the probabilities are updated directly. Below we discuss a simple mechanism to update
these probabilities.
2.1.3 Learning Automata
Learning Automata have their origins in the research labs of the former USSR. More precisely it
started with the work of Tsetlin in the 1960s [Tse62, Tse73].
In those early days, Learning Automata were deterministic and based on complete knowledge
of the environment. Later developments came up with uncertainties in the system and the
environment and lead to the stochastic automaton. More precisely, the stochastic automaton
tries to provide a solution for the reinforcement learning problem without having any information
on the optimal action initially. It starts with equal probabilities on all actions and during the
5
learning process these probabilities are updated based on responses from the environment. We
consider LA to be a method for solving RL problems in a policy iterative fashion.
The term Learning Automaton was introduced for the first time in the work of Narendra
and Thathacher in 1974 [Nar74]. Since then there has been a lot of development in the field
and a number of survey papers and books on this topic have been published: to cite a few
[Tha02, Nar89, Nar74].
In Figure 2 a Learning Automaton is illustrated in its most general form.
Figure 2 The feedback connection of the Learning Automaton - Environment pair.
The automaton tries to determine an optimal action out of a set of possible actions to perform.
Let us now first zoom in to the environment part of Figure 2. This part is illustrated in Figure
3. The environment responds to the input action αby producing an output β. The output also
belongs to a set of possible outcomes, i.e. {0,1}, which is probabilistically related to the set of
inputs through the environment vector c.
Figure 3 Zooming in to the environment of the Learning Automaton
The environment is represented by a triple {α, c, β}, where αrepresents a finite action set, β
represents the response set of the environment, and cis a vector of penalty probabilities, where
each component cicorresponds to an action αi.
The response βfrom the environment can take on 2 values β1or β2. Often they are chosen
to be 0 and 1, where 1 is associated with a penalty response (a failure) and 0 with a reward (a
success).
Now, the penalty probabilities cican be defined as
ci=P(β(n) = 1|α(n) = αi) (10)
Consequently, ciis the probability that action αiwill result in a penalty response. If these
probabilities are constant, the environment is called stationary.
Several models are recognized by the response set of the environment. Models in which the
response βcan only take 2 values are called P-models. Models which allow a finite number of
values in the fixed interval [0,1] are called Q-models. When βis a continuous random variable in
the fixed interval [0,1], the model is called S-model.
Now we considered the environment of the LA model of Figure 2, we will now zoom in at the
automaton itself of Figure 2. More precisely, Figure 4 illustrates this.
The automaton is represented by a set of states φ={φ1, ..., φs}. As opposed to the environ-
ment, βbecomes the input and αthe output. This implicitly defines a function F:φ×βφ,
mapping the current state and input into the next state, and a function H:φ×βα, mapping
the current state and current input into the current output. In this text we will use pas the
6
Figure 4 Zooming in to the automaton part of the Learning Automaton
probability vector over the possible actions of the automaton which corresponds to the function
H.
Summarizing, this brings us to the definition of a Learning Automaton. More precisely, it is
defined by a quintuple {α, β , F, p, T }for which αis the action or output set {α1, α2, . . . αr}of
the automaton , βis a random variable in the interval [0,1], Fis the state transition function,
pis the action probability vector of the automaton or agent and Tdenotes an update scheme.
The output αof the automaton is actually the input to the environment. The input βof the
automaton is the output of the environment, which is modeled through penalty probabilities ci
with ci=P[β|αi], i = 1 . . . r over the actions.
The automaton can be either stochastic or deterministic, the former’s output function Hbeing
composed of probabilities based on the environment’s response, whilst the latter having a fixed
mapping function between the internal state and the function to be performed.
Further sub-division of classification occurs when considering the transition or updating
function Fwhich determines the next state of the automaton given its current state and the
response from the environment. If this is fixed then the automaton is a fixed structure deterministic
or a fixed structure stochastic automaton.
However if the updating function is variable, allowing for the transition function to be modified
so that choosing the operations or actions changes after each iteration, then the automaton is
avariable structure deterministic or a variable structure stochastic automaton. In this paper we
are mainly concerned with the variable structure stochastic automata, which have the potential
of greater flexibility and therefore performance. Such an automaton A at timestep tis defined as:
A(t) = {α, β , p, T (α, β, p)}
where we have an action set αwith ractions, an environment response set βand a probability
set pcontaining rprobabilities, each being the probability of performing every action possible
in the current internal automaton state. The function Tis the reinforcement algorithm which
modifies the action probability vector pwith respect to the performed action and the received
response. The new probability vector can therefore be written as:
p(t+ 1) = T{α, β, p(t)}
with tthe timestep.
Next we summarize the different update schemes.
The most important update schemes are linear reward-penalty,linear reward-inaction and
linear reward-ǫ-penalty. The philosophy of those schemes is essentially to increase the probability
of an action when it results in a success and to decrease it when the response is a failure. The
general update algorithm is given by:
pi(t+ 1) pi(t) + a(1 β(t))(1 pi(t)) (t)pi(t) (11)
if αiis the action taken at time t
pj(t+ 1) pj(t)a(1 β(t))pj(t) + (t)[(r1)1pj(t)] (12)
if αj6=αi
The constants aand bin ]0,1[ are the reward and penalty parameters respectively. When a=b
the algorithm is referred to as linear reward-penalty (LRP), when b= 0 it is referred to as linear
7
reward-inaction (LRI) and when bis small compared to ait is called linear reward-ǫ-penalty
(LRǫP ).
If the penalty probabilities ciof the environment are constant, the probability p(n+ 1) is
fully determined by p(n) and hence p(n)n>0is a discrete-time homogeneous Markov process.
Convergence results for the different schemes are obtained under the assumptions of constant
penalty probabilities, see [Nar89]. LA belong to the Policy Iteration (PI) approach since action
probabilities are updated directly. In the Value Iteration (VI) approach the quality of an action
is determined, and the action with the highest quality is part of the optimal policy.
2.1.4 Value iteration in DP
The value iteration approach turns the Bellman equation (equation 5) into an update rule.
Because the V(s) are the unknowns, an estimate of these values is used at the right hand
side, these estimates Vk(s) are iteratively updated using the update rule:
Vk+1(s) = maxaE{rt+1 +γVk(st+1 )|st=s, at=a}(13)
=maxaX
s
Pa
ss[Ra
ss+γVk(s)] (14)
Since for all states kVk(s) is a contraction mapping, the Vk(s) values converge in the limit to
the optimal values V(s). In practice the updating is stopped, when the changes become very
small, and the corresponding optimal policy doesn’t changes any more. Since value iteration in
DP assumes a transition model of the system is available, the optimal policy πcan be obtained
using the equation below:
π(s) = argmaxaX
s
Pa
ss[Ra
ss+γV (s)] (15)
2.1.5 Value iteration in RL
The best known counter part of value iteration of DP in RL is Q-learning [Wat92]. Since RL is
model-free the optimal policy can not be retrieved from equation 15. Therefore, Q-learning stores
explicitly the Quality of each action for each state. These values are called Q-values, denoted by
Q(s, a). The relationship between the V(s) values and the Q(s, a) values is:
V(s) = maxaQ(s, a) (16)
The Q(s, a) are equal to the expected return of taking action ain state s, and from then on
behaving according to the optimal policy π, i.e.
Q(s, a) = X
s
Pa
ssE[Ra
ss+γV (s)] (17)
and therefore we also have that
Q(s, a) = X
s
Pa
ssE[Ra
ss+γmaxaQ(s, a)] (18)
The same way as in value iteration of DP, the Q-values are iteratively updated. But since in
RL we don’t have a model of the environment, we don’t know the Pa
ss, nor the E[Ra
ss], therefore
stochastic approximation is used, yielding the well known Q-learning updating rule:
Q(s, a)Q(s, a) + α(r+γmax
aQ(s, a)Q(s, a)) (19)
where αis a learning factor.
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2.2 Some convergence issues
Not all RL techniques come with a proof of convergence. Especially the policy iteration approaches
often lack such a proof. The Learning Automata, introduced above as an example of one stage
policy iteration, do have a proof of convergence. A single LA that uses the reward-inaction
updating scheme is guaranteed to converge [Nar89], the same is true for a set of independent LA
(see Section 4).
The value iteration approach, Q-learning is also proved to converge if applied in a Markovian
environment, and provided some very reasonable assumptions apply such as appropriate settings
for α(see Section 2.2.2). The Markovian property is really crucial, as soon as this not longer
holds, the guarantee for convergence is lost. This however does not mean that RL can not be
applied in non-Markovian environments but care has to be taken.
2.2.1 Partially Observable Markov Decision Processes
RL has been successfully applied to Partially Observable MDP’s (POMDP’s). Here the states
are only partially observable such that the learner can not distinguish sufficiently between the
states to let the Markovian property hold. This might e.g. be because a continuous state problem
is translated into a discrete problem, and the discretization is too coarse. A denser discretization
can restore the Markovian property, but leads to an increased size of the state space. Another
reason to not have the Markovian property is because an agent might miss a particular component
of the state description, due to the lack of a sensor that can measure that component. E.g. if
temperature is a critical component to have a Markovian view on the environment, and the agent
cannot measure this, the environment looks non-Markovian to the agent. Or if the agent only
has a local view of the environment, like a mobile robot, then it is possible that two different
locations result in the same sensor input to the agent. A technique that is often used to tackle
the non-Markovianism is to guide the exploration. If the agent can actively take part in it’s
exploration, the exploration can be steered such that e.g. two states that are indistinguishable,
become distinguishable because they result in different consequences when the same action is
applied, by doing so a limited history is introduced. Since we do not believe that this kind of
guided exploration is the way to go to solve the non-Markovian environments where MAS operate
in, we do not go in to more detail in this issue here. For more details see [Per02, Cas94, Kae96].
2.2.2 The convergence of Q-learning
Amongst others, Tsitsiklis [Tsi93] has proved that under very reasonable assumptions Q-learning
is guaranteed to converge to the optimal policy. The proof of Tsitsiklis is very interesting because
it considers Q-learning as a stochastic approximation technique, and can be adapted to prove
all kinds of variants to Q-learning. See e.g. the distributed version in the next section. We will
not discuss the proof in detail here, but it is worthwhile to have a look at the assumptions that
have an impact on the setting of the learning process. A first assumption says that the learning
parameter in the Q-learning updating rule (i.e. αin Section 2.1.5) must be decreased in time
such that, P
t=0 α(s,a)(t) = and P
t=0 α2
(s,a)(t)<. This allows that Q-values are updated in
an asynchronous way.
Another assumption states that it is no problem that past experience is used to guide the
exploration, so the agent can actively take part in the exploration.
A last interesting assumption is that the agent can use outdated information as long as old
information is eventually discarded, i.e. in the updating rule of Q-learning, the update of the
Qk+1(s, a) values can be done based on Qki(s, a) values, with ibetween 0 and k. It becomes
clear that Q-learning can be considered as a stochastic approximation method by rewriting the
Q-learning update rule as follows:
Qk+1(s, a) = Qk(s, a) + α(s,a)(k)[Qk(s, a)(Qk)Qk(s, a) + Wk(s, a)] (20)
9
With Qk(s, a)(Qk) = E[r(s, a) + γPsPa
ssmaxaQk(s, a)] and (Qk) is the vector containing all
Qk(s, a) values. α(s,a)(k) = 0 if Qk+1 (s, a) is not updated at time step k+ 1, otherwise α(s,a)(k)
]0,1] obeying the restrictions stated above.
And
Wk(s, a) = (rk(s, a) + γmaxaQk(s, a)) E[r(s, a) + γX
s
Pa
ssmaxaQk(s, a)] (21)
Qis a contraction mapping, meaning that it is monotonously converging, and Wis proved to
behave as white noise [Tsi93].
In the next section we briefly introduce a first MAS setting of RL, however we prefer to refer
to it as a simple distributed approach.
2.3 Distributed RL
Since the proof of Tsitsiklis allows that Q-values are updated asynchronously and based on
outdated information, it is rather straightforward to come up with a parallel or distributed
version of Q-learning.
Assume we subdivide the state space in different regions. In each region an agent gets the
responsibility of updating the corresponding Q-values by exploring his region. Agents can explore
their own region, and make updates in their copy of the table of the Q-values to the Q-values
that belong to their own region. As long as they make transitions in their own region, they can
apply the usual updating rule. If they make however a transition to another region, with Q-values
that belong to the responsibility of another agent, they should not directly communicate to that
other agent and ask for the particular Q-value, but use the information they have in their own
copy table, i.e. use out-dated information, and steer the exploration so as to get back to their
own region. Since out-date information needs to be updated from time to time, the agents should
communicate from time to time and distribute the Q-values of which they have the responsibility.
Since we do not put the Markovian property in to danger by this approach, the prove of Tsitsiklis
can be applied, and convergence is still assured. While this approach can be considered as a MAS,
we prefer to refer to it as a distributed or parallel version of Q-learning. The approach has been
successfully applied to the problem of Call Admission Control in telecommunications [Ste97]3.
3 Evolutionary Game Theory
3.1 Introduction
Originally, Game Theory was launched by John von Neumann and Oskar Morgenstern in 1944
in their book Theory of Games and Economic Behavior [Neu44]4.
Game theory is an economical theory that models interactions between rational agents as
games of two or more players that can choose from a set of strategies and the corresponding
preferences. It is the mathematical study of interactive decision making in the sense that the
agents involved in the decisions take into account their own choices and those of others. Choices
are determined by 1) stable preferences concerning the outcomes of their possible decisions, and
2) agents act strategically, in other words, they take into account the relation between their own
3In this paper we focus on genuine model-free RL. However many RL techniques incorporate domain
knowledge. If this knowledge is available then it is a good idea to use it one way or the other. The nice
thing about RL, is that if the domain knowledge seems not to be perfect or even totally incorrect, still the
RL techniques will converge, at the end, to the optimal policy. The incorporation of domain knowledge
can e.g. be done by initialing the Q-values based on the knowledge, or by hypothetical moves, so updates
are based on the model.
4We do not intend to ignore previous game theoretic results as for instance the theorem of Zermelo which
asserts that chess is strictly determined. We only state that this is the first book under the name Game
Theory assembling many different results.
10
choices and the decisions of other agents. Different economical situations lead to different rational
strategies for the players involved.
When John Nash discovered the theory of games at Princeton, in the late 1940’s and early
1950’s, the impact was enormous. The impact of the developments in Game Theory expressed
itself especially in the field of economics, where its concepts played an important role in for
instance the study of international trade, bargaining, the economics of information and the
organization of corporations. But also in other disciplines such as social and natural sciences
the importance of Game Theory became clear, examples are: studies of legislative institutions,
voting behavior, warfare, international conflicts, and evolutionary biology.
However, von Neumann and Morgenstern had only managed to define an equilibrium concept
for 2-person zero-sum games. Zero-sum games correspond to situations of pure competition,
whatever one player wins, must be lost by another. John Nash addressed the case of competition
with mutual gain by defining best-reply functions and using Kakutani’s fixed point-theorem5. The
main results of his work were the development of the Nash Equilibrium and the Nash Bargaining
Solution concept.
Despite the great usefulness of the Nash equilibrium concept, the assumptions traditional
game theory make, like hyper rational players that correctly anticipate the other players in
an equilibrium, made game theory stagnate for quite some time [Wei96, Gin00, Sam97]. A lot
of refinements of Nash equilibria came along (for instance trembling hand perfection), which
made it hard to choose the appropriate equilibrium in a particular situation. Almost any Nash
equilibrium could be justified in terms of some particular refinement. This made clear that the
static Nash concept did not reflect the (dynamic) real world where people do not make decisions
under hyper-rationality assumptions.
This is where evolutionary game theory originated. More precisely, John Maynard Smith
adopted the idea of evolution from biology [May73, May82]. He applied Game Theory (GT) to
Biology, which made him relax some of the premises of GT. Under these biological circumstances,
it becomes impossible to judge what choices are the most rational ones. The question now becomes
how a player can learn to optimize its behavior and maximize its return. This learning process is
the core of evolution in Biology.
These new ideas led Smith and Price to the concept of Evolutionary Stable Strategies (ESS),
a special case of the Nash condition. In contrast to GT, EGT is descriptive and starts from
more realistic views of the game and its players. Here the game is no longer played exactly once
by rational players who know all the details of the game, such as each others preferences over
outcomes. Instead EGT assumes that the game is played repeatedly by players randomly drawn
from large populations, uninformed of the preferences of the opponent players.
Evolutionary Game Theory offers a solid basis for rational decision making in an uncertain
world, it describes how individuals make decisions and interact in complex environments in the
real world. Modeling learning agents in the context of Multi-agent Systems requires insight in
the type and form of interactions with the environment and other agents in the system. Usually,
these agents are modeled similar to the different players in a standard game theoretical model.
In other words, these agents assume complete knowledge of the environment, have the ability to
correctly anticipate the opposing player (hyper-rationality) and know that the optimal strategy in
the environment is always the same (static Nash equilibrium). The intuition that in the real world
people are not completely knowledgeable and hyper-rational players and that an equilibrium can
change dynamically led to the development of evolutionary game theory.
Before introducing the most elementary concepts from (Evolutionary) Game Theory we
summarize some well known examples of strategic interaction in the next section.
5Kakutani’s fixpoint theorem goes as follows. Consider Xa nonempty set and Fa point-to-set map from
Xto subsets of X. Now, if Fis continuous, Xis compact and convex, and for each xin X,F(x) is
nonempty and convex, Fhas a fixed point. Applying this theorem (and thus checking its conditions) to
the best response function proves the existence of a Nash equilibrium.
11
3.2 Examples of Strategic interaction
3.2.1 The Prisoner’s Dilemma
As the first example of a strategic game we consider the prisoner’s dilemma game [Gin00, Wei96].
In this game, 2 prisoners, who committed a crime together, have a choice to either collaborate
with the police (to defect) or work together and deny everything (to cooperate). If the first
criminal (row player) defects and the second one cooperates, the first one gets off the hook
(expressed by a maximum reward of 5) and the second one gets the most severe punishment
(reward 0). If they both defect, they get the second most severe punishment one can get (expressed
by a payoff of 1). If both cooperate, they both get a small punishment (reward 3).
The rewards are summarized in payoff Tables 1 and 2. The first table has to be read from a row
perspective and the second one from a column perspective. For instance if the row player chooses
to Defect (D), the payoff has to be read from the first row. It then depends on the strategy of
the column player what payoff the row player will receive.
A= D 1 5
C 0 3
Table 1 Matrix (A) defines the payoff for the row player for the Prisoner’s dilemma. Strategy Dis
Defect and strategy Cis Cooperate.
B=
D C
1 0
5 3
Table 2 Matrix (B) defines the payoff for the column player for the Prisoner’s dilemma. Strategy Dis
Defect and strategy Cis Cooperate.
3.2.2 The Battle of the Sexes
The second example of a strategic game we consider is the battle of the sexes game [Gin00, Wei96].
In this game, a married couple loves each other so much they want to do everything together.
One evening the husband wants to see a football game and the wife wants to go to the opera. If
they both choose their own preference and do their activities separately they receive the lowest
payoff. This situation is described by the payoff matrices of Tables 3 and 4.
A= F 2 0
O 0 1
Table 3 Matrix (A) defines the payoff for the row player for the Battle of the sexes. Strategy Fis
choosing Football and strategy Ois choosing the Opera.
B=
F O
1 0
0 2
Table 4 Matrix (B) defines the payoff for the column player for Battle of the sexes. Strategy Fis
choosing Football and strategy Ois choosing the Opera.
3.2.3 Matching Pennies
The third example of a strategic game we consider is the matching pennies game. [Gin00, Wei96].
12
In this game two children hold both a pennie and independently choose which side of the coin
to show (Head or Tails). The first child wins if both coins show the same side, otherwise child
2 wins. This is an example of a zero-sum game as can be seen from the payoff Tables 5 and 6.
Whatever is lost by one player, must be won by the other player.
A= H 1 -1
T -1 1
Table 5 Matrix (A) defines the payoff for the row player for the matching pennies game. Strategy His
playing Head and strategy Tis playing Tail.
B=
H T
-1 1
1 -1
Table 6 Matrix (B) defines the payoff for the column player for the matching pennies game. Strategy
His playing Head and strategy Tis playing Tail.
3.3 Elementary concepts
In this section we review the key concepts of GT and EGT and its mutual relationships. We start
by defining strategic games and concepts as Nash equilibrium, Pareto optimality and evolutionary
stable strategies. Then we discuss the relationships between these concepts and provide some
examples.
3.3.1 Strategic games
An n-player normal form games models a conflict situation involving gains and losses between n
players. In such a game nplayers interact with each other by all choosing an action (or strategy)
to play. All players choose their strategy at the same time without being informed about the
choices of the others. For reasons of simplicity, we limit the pure strategy set of the players to
2 strategies. A strategy is defined as a probability distribution over all possible actions. In the
2-pure strategies case, we have: s1= (1,0) and s2= (0,1). A mixed strategy smis then defined
by sm= (x1, x2) with x1, x26= 0 and x1+x2= 1.
Defining a game more formally we restrict ourselves to the 2-player 2-action game. Nevertheless,
an extension to n-players n-actions games is straightforward, but examples in the n-player case
do not show the same illustrative strength as in the 2-player case. A game G= (S1, S2, P1, P2)
is defined by the payoff functions P1,P2and their strategy sets S1for the first player and S2
for the second player. In the 2-player 2-strategies case, the payoff functions P1:S1×S2→ ℜ and
P2:S1×S2→ ℜ are defined by the payoff matrices, Afor the first player and Bfor the second
player, see Table 7. The payoff tables A, B define the instantaneous rewards. Element aij is the
reward the row-player (player 1) receives for choosing pure strategy sifrom set S1when the
column-player (player 2) chooses the pure strategy sjfrom set S2. Element bij is the reward for
the column-player for choosing the pure strategy sjfrom set S2when the row-player chooses pure
strategy sifrom set S1.
The family of 2 ×2 games is usually classified in three subclasses, as follows [Red01],
A = a11 a12
a21 a22 B = b11 b12
b21 b22
Table 7 The left matrix (A) defines the payoff for the row player, the right matrix (B) defines the payoff
for the column player
13
Subclass 1: if (a11 a21 )(a12 a22)>0 or (b11 b12 )(b21 b22)>0, at least one of the 2
players has a dominant strategy, therefore there is just 1 strict equilibrium.
Subclass 2: if (a11 a21)(a12 a22)<0,(b11 b12)(b21 b22)<0, and (a11 a21 )(b11 b12)>
0, there are 2 pure equilibria and 1 mixed equilibrium.
Subclass 3: if (a11 a21)(a12 a22)<0,(b11 b12)(b21 b22)<0, and (a11 a21 )(b11 b12)<
0, there is just 1 mixed equilibrium.
The first subclass includes those type of games where each player has a dominant strategy6, as for
instance the prisoner’s dilemma. However it includes a larger collection of games since only one
of the players needs to have a dominant strategy. In the second subclass none of the players has a
dominated strategy (e.g. battle of the sexes). But both players receive the highest payoff by both
playing their first or second strategy. This is expressed in the condition (a11 a21)(b11 b12 )>0.
The third subclass only differs from the second in the fact that the players do not receive their
highest payoff by both playing the first or the second strategy (e.g. matching pennies game). This
is expressed by the condition (a11 a21)(b11 b12 )<0.
3.3.2 Nash equilibrium
In traditional game theory it is assumed that the players are hyperrational, meaning that every
player will choose the action that is best for him, given his beliefs about the other players’
actions. A basic definition of a Nash equilibrium is stated as follows. If there is a set of strategies
for a game with the property that no player can increase its payoff by changing his strategy
while the other players keep their strategies unchanged, then that set of strategies and the
corresponding payoffs constitute a Nash equilibrium.
Formally, a Nash equilibrium is defined as follows. When 2 players play the strategy profile s=
(si, sj) belonging to the product set S1×S2then sis a Nash equilibrium if P1(si, sj)P1(sx, sj)
x∈ {1, ..., n}and P2(si, sj)P2(si, sx)x∈ {1, ..., m}7.
3.3.3 Pareto optimality
Intuitively a Pareto optimal solution of a game can be defined as follows: a combination of actions
of agents in a game is Pareto optimal if there is no other solution for which all players do at least
as well and at least one agent is strictly better off.
More formally we have: a strategy combination s= (s1, ..., sn) for nagents in a game is Pareto
optimal if there does not exist another strategy combination s= (s1, ..., sn) for which each player
receives at least the same payoff Piand at least one player jreceives a strictly higher payoff than
Pj,i.e. Pi(s)Pj(s)Iandj:Pi(s)< Pj(s).
Another related concept is that of Pareto Dominance: An outcome of a game is Pareto
dominated if some other outcome would make at least one player better off without hurting
any other player. That is, some other outcome is weakly preferred by all players and strictly
preferred by at least one player. If an outcome is not Pareto dominated by any other, than it is
Pareto optimal.
3.3.4 Evolutionary Stable Strategies
The core equilibrium concept of Evolutionary Game Theory is that of an Evolutionary Stable
Strategy (ESS). The idea of an evolutionarily stable strategy was introduced by John Maynard
Smith and Price in 1973 [May73]. Imagine a population of agents playing the same strategy.
Assume that this population is invaded by a different strategy, which is initially played by a
6A strategy is dominant if it is always better than any other strategy, regardless of what the opponent
may do.
7For a definition in terms of best reply or best response functions we refer the reader to [Wei96].
14
small number of the total population. If the reproductive success of the new strategy is smaller
than the original one, it will not overrule the original strategy and will eventually disappear. In
this case we say that the strategy is evolutionary stable against this new appearing strategy. More
generally, we say a strategy is an Evolutionary Stable strategy if it is robust against evolutionary
pressure from any appearing mutant strategy.
Formally an ESS is defined as follows. Suppose that a large population of agents is programmed
to play the (mixed) strategy s, and suppose that this population is invaded by a small number
of agents playing strategy s. The population share of agents playing this mutant strategy is
ǫ]0,1[. When an individual is playing the game against a random chosen agent, chances that
he is playing against a mutant are ǫand against a non-mutant are 1 ǫ. The expected payoff for
the first player, being a non mutant is:
P(s, (1 ǫ)s+ǫs) = (1 ǫ)P(s, s) + ǫp(s, s)
and being a mutant is,
P(s,(1 ǫ)s+ǫs)
Now we can state that a strategy sis an ESS if s6=sthere exists some δ]0,1[ such that
ǫ: 0 < ǫ < δ,
P(s, (1 ǫ)s+ǫs)> P (s,(1 ǫ)s+ǫs)
holds. The condition ǫ: 0 < ǫ < δ expresses that the share of mutants needs to be sufficiently
small.
3.3.5 The relation between Nash equilibria and ESS
This section explains how the core equilibria concepts from classical and evolutionary game
theory relate to one another. The set of Evolutionary Stable Strategies for a particular game are
contained in the set of Nash Equilibria for that same game,
{ESS} ⊂ {NE}
The condition for an ESS is more strict than the Nash condition. Intuitively this can be understood
as follows: as defined above a Nash equilibrium is a best reply against the strategies of the other
players. Now if a strategy s1is an ESS then it is also a best reply against itself, and as such
optimal. If it was not optimal against itself there would have been a strategy s2that would lead
to a higher payoff against s1than s1itself.
P(s2s1)> P (s1, s1)
So, if the population share ǫof mutant strategies s2is small enough then s1is not evolutionary
stable because,
P(s2,(1 ǫ)s1+ǫs2)> P (s1,(1 ǫ)s1+ǫs2)
Another important property for an ESS is the following. If s1is ESS and s2is an alternative
best reply to s1, then s1has to be a better reply to s2than s2to itself. This can easily be seen
as follows, because s1is ESS, we have for all s2
P(s1,(1 ǫ)s1+ǫs2)> P (s2,(1 ǫ)s1+ǫs2)
i.e.
P(s2, s2) = P(s1, s2)
If s2does as well against itself as s1does, then s2earns at least as much against (1 ǫ)s1+ǫs2
as s1and then s1is no longer evolutionary stable. To summarize we now have the following 2
properties for an ESS s1,
1. P(s2, s1)P(s1, s1)s2
2. P(s2, s1) = P(s1, s1) =P(s2, s2)< P (s1, s2)s26=s1
15
3.3.6 Examples
In this section we provide an example for each class of game described in Section 3.3.1) and
illustrate the Nash equilibrium concept and Evolutionary Stable Strategy concept as well as
Pareto optimality.
For the first subclass we consider the prisoner’s dilemma game. The strategic setup of this
game has been explained in Section 3.2. The payoffs of the game are repeated in table 8. As one
can see both players have one dominant strategy, more precisely defect.
A = 1 5
0 3 B = 1 0
5 3
Table 8 Prisoner’s dilemma: The left matrix (A) defines the payoff for the row player, the right one
(B) for the column player.
For both players, defecting is the dominant strategy and therefore always the best reply toward
any strategy of the opponent. So the Nash equilibrium in this game is for both players to defect.
Let’s now determine whether this equilibrium is also an evolutionary stable strategy. Suppose
ǫ[0,1] is the number of cooperators in the population. The expected payoff of a cooperator is
3ǫ+ (1 0ǫ) and that of a defector is 5ǫ+ (1 1ǫ). Since for all ǫ,
5ǫ+ 1(1 ǫ)>3ǫ+ 0(1 ǫ)
defect is an ESS. So the number of defectors will always increase and the population will eventually
only consist of defectors. In Section 3.4 this dynamical process will be illustrated by the replicator
equations.
This equilibrium which is both Nash and ESS, is not a Pareto optimal solution. This can be eas-
ily seen if we look at the payoff tables. The combination (defect, defect) yields a payoff of (1,1),
which is a smaller payoff for both players than the combination (cooperate, cooperate) which
yields a payoff of (3,3). Moreover the combination (cooperate, cooperate) is a Pareto optimal
solution. However, if we apply the definition of Pareto optimality, then also (def ect, cooperate)
and (cooperate, def ect) are Pareto optimal. But both these Pareto optimal solutions do not
Pareto dominate the Nash equilibrium and therefore are not of interest to us. The combination
(cooperate, cooperate) is a Pareto optimal solution which Pareto dominates the Nash equilibrium.
For the second subclass we considered the battle of the sexes game [Gin00, Wei96]. In this
game there are 2 pure strategy Nash equilibria, i.e. (f ootball, f ootball) and (opera, opera), which
both are also evolutionary stable (as demonstrated in Section 3.4.4). There is also 1 mixed nash
equilibrium, i.e. where the row player (the husband) plays football with 2/3 probability and
opera with 1/3 probability and the column player (the wife) plays opera with 2/3 probability
and football with 1/3 probability. However, this equilibrium is not an evolutionary stable one.
A = 2 0
0 1 B = 1 0
0 2
Table 9 Battle of the sexes: The left matrix (A) defines the payoff for the row player, the right one (B)
for the column player.
The third class consists of the games with a unique mixed equilibrium ((1/2,1/2),(1/2,1/2)).
For this category we used the game defined by the matrices in Table 10, i.e. maching pennies
. This equilibrium is not an evolutionary stable one. Typical for this class of games is that the
interior trajectories define closed orbits around the equilibrium point.
16
A = 11
1 1 B = 1 1
11
Table 10 The left matrix (A) defines the payoff for the row player, the right one (B) for the column
player.
3.4 Population Dynamics
In this section we discuss the Replicator Dynamics in a single and a multi population setting.
We discuss the relation with concepts as Nash equilibrium and ESS and illustrate the described
ideas with some examples.
3.4.1 Single Population Replicator Dynamics
The basic concepts and techniques developed in EGT were initially formulated in the context of
evolutionary biology. [May82, Wei96, Sam97]. In this context, the strategies of all the players
are genetically encoded (called genotype). Each genotype refers to a particular behavior which
is used to calculate the payoff of the player. The payoff of each player’s genotype is determined
by the frequency of other player types in the environment.
One way in which EGT proceeds is by constructing a dynamic process in which the proportions
of various strategies in a population evolve. Examining the expected value of this process gives
an approximation which is called the RD. An abstraction of an evolutionary process usually
combines two basic elements: selection and mutation. Selection favors some varieties over
others, while mutation provides variety in the population. The replicator dynamics highlight the
role of selection, it describes how systems consisting of different strategies change over time. They
are formalized as a system of differential equations. Each replicator (or genotype) represents one
(pure) strategy si. This strategy is inherited by all the offspring of the replicator. The general
form of a replicator dynamic is the following:
dxi
dt = [(Ax)ix·Ax]xi(22)
In equation (22), xirepresents the density of strategy siin the population, Ais the payoff
matrix which describes the different payoff values each individual replicator receives when
interacting with other replicators in the population. The state of the population (x) can be
described as a probability vector x= (x1, x2, ..., xJ) which expresses the different densities of all
the different types of replicators in the population. Hence (Ax)iis the payoff which replicator si
receives in a population with state x, and x·Axdescribes the average payoff in the population.
The growth rate
dxi
dt
xiof the population share using strategy siequals the difference between the
strategy’s current payoff and the average payoff in the population. For further information we
refer the reader to [Wei96, Hof98].
3.4.2 Multi-Population Replicator Dynamics
So far the study of population dynamics was limited to a single population. However in many
situations interaction takes place between 2 or more individuals from different populations. In
this section we study this situation in the 2-player multi-population case for reasons of simplicity.
Games played by individuals of different populations are commonly called evolutionary
asymmetric games. Here we consider a game to be played between the members of two different
populations. As a result, we need two systems of differential equations: one for the row player
(R) and one for the column player (C). This setup corresponds to a RD for asymmetric games. If
A=Bt(the transpose of B), equation (22) would result again. Player Rhas a probability vector
pover its possible strategies and player Ca probability vector qover its strategies.
17
This translates into the following replicator equations for the two populations:
dpi
dt = [(Aq)ip·Aq]pi(23)
dqi
dt = [(Bp)iq·Bp]qi(24)
As can be seen in equation (23) and (24), the growth rate of the types in each population is
now determined by the composition of the other population. Note that, when calculating the
rate of change using these systems of differential equations, two different payoff matrices (Aand
B) are used for the two different players.
3.4.3 Relating Nash, ESS and the RD
As being a system of differential equations, the RD have some rest points or equilibria. An
interesting question is how these RD-equilibria relate to the concepts of Nash equilibria and ESS.
We briefly summarize some known results from the EGT literature [Wei96, Gin00, Osb94, Hof98,
Red01]. An important result is that every Nash equilibrium is an equilibrium of the RD. But the
opposite is not true. This can be easily understood as follows. Let us consider the vector space
or simplex of mixed strategies determined by all pure strategies. Formally the unit simplex is
defined by,
∆ = {x∈ ℜm
+:
m
X
i=1
xi= 1}
where xis a mixed strategy in m-dimensional space (there are mpure strategies), and xiis
the probability with which strategy siis played. Calculating the RD for the unit vectors of this
space (putting all the weight on a particular pure strategy), yields zero. This is simply due to the
properties of the simplex ∆, where the sum of all population shares remains equal to 1 and no
population share can ever turn negative. So, if all pure strategies are present in the population
at any time, then they always have been and always will be present, and if a pure strategy is
absent from the population at any time, then it always has been and always will be absent8. So,
this means that the pure strategies are rest points of the RD, but depending on the structure
of the game which is played these pure strategies do not need to be a Nash equilibrium. Hence
not every rest point of the RD is a Nash equilibrium. So the concept of dynamic equilibrium or
stationarity alone is not enough to have a better understanding of the RD.
For this reason the criterion of asymptotic stability came along, where you have some kind of local
test of dynamic robustness. Local in the sense of minimal perturbations. For a formal definition
of asymptotic stability, we refer to [Hir74]. Here we give an intuitive definition. An equilibrium
is asymptotic stable if the following two conditions hold:
Any solution path of the RD that starts sufficiently close to the equilibrium remains
arbitrarily close to it. This condition is called Liapunov stability.
Any solution path that starts close enough to the equilibrium, converges to the equilibrium.
Now, if an equilibrium of the RD is asymptotically stable (i.e. being robust to local perturbations)
then it is a Nash equilibrium. For a proof, the reader is referred to [Red01]. An interesting result
due to Sigmund ans Hofbauer [Hof98] is the following : If sis an ESS, then the population state
x=sis asymptotically stable in the sense of the RD. For a proof see [Hof98, Red01]. So, by this
result we have some kind of refinement of the asymptotic stable rest points of the RD and it
provides a way of selecting equilibria from the RD that show dynamic robustness.
8Of course a solution orbit can evolve toward the boundary of the simplex as time goes to infinity, and
thus in the limit, when the distance to the boundary goes to zero, a pure strategy can disappear from
the population of strategies. For a more formal explanation, we refer the reader to [Wei96]
18
3.4.4 Examples
In this section we continue with the examples of Section 3.2 and the classification of games of
Section 3.3.1. We start over with the Prisoner’s Dilemma game (PD). In Figure 5 we plotted the
direction field of the replicator equations applied to the PD. A Direction field is a very elegant
and excellent tool to understand and illustrate a system of differential equations. The direction
fields presented here consist of a grid of arrows tangential to the solution curves of the system.
Its a graphical illustration of the vector field indicating the direction of the movement at every
point of the grid in the state space. Filling in the parameters for each game in equations 23 and
24, allowed us to plot this field.
0
0.2
0.4
0.6
0.8
1
y
0.2 0.4 0.6 0.8 1
x
Figure 5 The direction field of the RD of the prisoner’s dilemma using payoff Table 8.
The x-axis represents the probability with which the first player will play defect and the y-axis
represents the probability with which the second player will play defect. So the Nash equilibrium
and the ESS lie at coordinates (1,1). As you can see from the field plot all the movement goes
toward this equilibrium.
Figure 6 illustrates the direction field diagram for the battle of the sexes game. As you may
recall from Section 3.3.6 this game has 2 pure Nash equilibria and 1 mixed Nash equilibrium.
These equilibria can be seen in the figure at coordinates (0,0),(1,1),(2/3,1/3). The 2 pure
equilibria are ESS as well. This is also easy to verify from the plot, more precisely, any small
perturbation away from the equilibrium is led back to the equilibrium by the dynamics.
The mixed equilibrium, which is Nash, is not an asymptotic stable strategy, which is obvious
from the plot. From Section 3.3.6, we can now also conclude that this equilibrium is not
evolutionary stable either.
0
0.2
0.4
0.6
0.8
1
y
0.2 0.4 0.6 0.8 1
x
Figure 6 The direction field of the RD of the Battle of the sexes game using payoff Table 9.
19
3.5 The role of EGT in MAS
In this section we discuss the most interesting properties that link the fields of EGT and MAS.
These properties make clear that there exists an important role for EGT in MAS.
Traditional Game theory is an economical theory that models interactions between rational
agents as games of two or more players that can choose from a set of strategies and the
corresponding preferences. It is the mathematical study of interactive decision making in the
sense that the agents involved in the decisions take into account their own choices and those of
others. Choices are determined by
1. stable preferences concerning the outcomes of their possible decisions,
2. agents act strategically, in other words, they take into account the relation between their own
choices and the decisions of other agents.
Typical for the traditional game theoretic approach is to assume perfectly rational players (or
hyperrationality) who try to find the most rational strategy to play. These players have a perfect
knowledge of the environment and the payoff tables and they try to maximize their individual
payoff. These assumptions made by classical game theory just do not apply to the real world and
Multi-Agent settings in particular.
In contrast, EGT is descriptive and starts from more realistic views of the game and its
players. A game is not played only once, but repeatedly with changing opponents. Moreover,
the players are not completely informed, sometimes misinterpret each others’ actions, and
are not completely rational but also biologically and sociologically conditioned. Under these
circumstances, it becomes impossible to judge what choices are the most rational ones. The
question now becomes how a player can learn to optimize its behavior and maximize its return.
For this learning process, mathematical models are developed, e.g. replicator equations.
Summarizing the above we can say that EGT treats agents’ objectives as a matter of fact,
not logic, with a presumption that these objectives must be compatible with an appropriately
evolutionary dynamic [Gin00]. Evolutionary models do not predict self-interested behaviour. It
describes how agents can make decisions in complex environments, in which they interact with
other agents. In such complex environments software agents must be able to learn from their
environment and adapt to its non-stationarity.
The basic properties of a Multi-Agent System correspond exactly with that of EGT. First of
all, a MAS is made up of interactions between two or more agents, who each try to accomplish a
certain, possibly conflicting, goal. No agent has the guarantee to be completely informed about
the other agents intentions or goals, nor has it the guarantee to be completely informed about
the complete state of the environment. Of great importance is that EGT offers us a solid basis to
understand dynamic iterative situations in the context of strategic games. A MAS has a typical
dynamical character, which makes it hard to model and brings along a lot of uncertainty. At this
stage EGT seems to offer us a helping hand in understanding this typical dynamical processes in
a MAS and modeling them in simple settings as iterative games of two or more players.
4 Multi-Agent Reinforcement Learning
Coordination is an important problem in multi-agent reinforcement learning research (MARL).
For cooperative multi-agent environments several algorithms and techniques exist. However they
are usually defined for one state problems or games, only few of them are suited for the multi-
stage case, moreover restrictions on the structure of the multi-stage case are imposed. Below we
give an overview of the most important one stage techniques, and the results of a comparative
study where we tested the techniques on a variety of settings, even for situations for which some
of the algorithms were originally not developed.
We shed light on some useful characteristics and strengths of each algorithm studied. For
learning in a multi-agent system, two extreme approaches can be recognized. On the one hand,
20
the presence of other agents, who are possibly influencing the effects a single agent experiences,
can be completely ignored. Thus a single agent is learning as if the other agents are not around.
On the other hand, the presence of other agents can be modeled explicitly. This results in a
joint action space approach which recently received quite a lot of attention [Cla98, Hu99, Lit94].
4.1 The Joint Action Space Approach
In the joint action space technique, learning happens in the product space of the set of states S,
and the collections of action sets A1, ...An(one set for every agent). The state transition function
T:S×A1×. . . ×AnP(S) maps a state and an action from each agent onto a probability
distribution on Sand each agent receives an associated reward, defined by the reward function Ri:
S×A1×. . . ×AnP(). This is the underlying model for the stochastic games, also referred
to as Markov games in [Hu99, Lit94].
The joint action space, is a safe technique in the sense that the influence of an agent on
every other agent can be modeled in such a manner that the Markov property still holds. This
combined with a unique solution concept such as the Stackelberg equilibrium, allows to bootstrap
as is usually done in RL techniques [Kon04]. However the joint action space approach violates
the basic principles of multi-agent systems : distributed control, asynchronous actions, incomplete
information, cost of communication etc.
4.2 Independent Reinforcement learners
Independent RL are trying to optimize their behavior without any form of communication
with the other agents, they only use the feedback they receive from the environment. These
independent RL might use traditional reinforcement learning algorithms, created for stationary,
one-agent settings. Since the feedback coming from the environment is in general dependent on
the combination of action taken by the agents, and not just the action of a single agent, the
Markovian property no longer holds, and the problem becomes non-stationary state dependent
[Nar89]. It is shown in [Nar89] that if the agents use a reward-inaction updating scheme they are
able to converge to a pure Nash equilibrium state. In case no pure NE exists, we need to extend
the RL algorithm as discussed in the last section of this paper.
In our study, we also consider optimistic independent agents, introduced in [Lau00]. Optimistic
independent agents will only do their update when the performance they receive from the new
experience is better than was expected until then.
4.3 Informed Reinforcement learners
Informed agents use communication through revelation schemes, in order to coordinate on the
optimal joint action, [Muk01]. Revelation comes in different 2-player schemes, ranging from not
allowing agents to communicate actions to each other (no revelation), to allowing agents in
turn to reveal their actions (alternate revelation), and to allowing agents to reveal their actions
simultaneously (simultaneous revelation). The agents, who are allowed to communicate, decide
for themselves whether they reveal their action or not.
4.4 Exploration-exploitation schemes for independent Reinforcement learners
The last group of techniques used for the comparison of Section 4.5 tries extended exploration-
exploitation schemes to push independent agents toward their part of the optimal joint-action.
Two algorithms have been considered in this study. The Frequency Maximum Q technique
described in [Kap02] adds an heuristic value to the Q-value of an action in the Boltzmann
exploration strategy. This FMQ heuristic takes into account how frequently an action produces its
maximum corresponding reward. In [Ver02] a new exploration technique is used for coordination
games. It is based on exploring, selfish reinforcement learning agents (ESRL), playing selfish for a
21
period of time and then excluding actions from their private action space, so that the joint action
space gets considerably smaller and the agents are able to converge to a NE of the remaining
subgame. By repeatedly excluding action, the agents are able to figure out the Pareto front and
decide on which combination of actions is preferable.
4.5 Comparison of techniques
The 2 player games we used as a test-bed were respectively 2 revelation games, the penalty game
and the climbing game. The revelation games were extracted from [Muk01]. The first one was
constructed in such a way that each agent has a preferred action no matter what the other agent
does. However the joint combination of these choices is not optimal. The second revelation game
has a Pareto optimal joint action however this is not a Nash equilibrium. The other 2 games
were originally used in [Cla98]. The challenge in the climbing game is to reach the optimal joint
action when it is surrounded by heavy penalties. The penalty game has the additional difficulty
of having two Pareto optimal solutions on which the agents should agree. A complete overview
of the results can be found in [Pee03]. We summarize the most important conclusions, flaws and
strong points here.
The independent learners produced the most remarkable result in one of the revelation games.
They were able to perform better than the revelation agents. This shows that providing more
information to an agent may result in a worse performance. However the other games showed
that acting independently is not always enough to guarantee convergence to the Pareto Optimal
NE. Independent learners are also very dependent on the settings of the learning rate and the
exploration temperature. The optimistic assumption is useful in driving the agents to the Pareto
Optimal solution, however this technique will not work in stochastic environments.
The revelation learners were originally not created for the climbing and penalty game, and
the penalties in these games turned out to be too difficult to overcome. The revelation learners
would probably behave better in an auction environment. The modeling of other agents might
give them an advantage and the concept of revelation might lead to interesting results (perhaps
extending the agents to give them the ability to lie).
The FMQ learners have a convergence rate of 100% in identical payoff games (i.e the games
the algorithm is designed for). For the other games convergence is not always assured, but if it
does converge the convergence time is very good. When we play games where the optimal group
utility differs from the agents’ personal utility, FMQ learners fail to reach the optimal solution.
Also at this stage, FMQ learners have problems with genuine stochastic games.
The ESRL learners reach the Pareto optimal solution in every game we played under the
assumption that they were allowed enough time to converge to a NE. A downside is that this
time is to be set in advance. The time they need to converge is usually longer than for FMQ
learners, because playing in periods and visiting all equilibria is a slow process. However, because
of the agents playing in periods of time and therefore allowing them to sample enough information
on joint actions, exclusion learners are able to work in stochastic games.
4.6 An Evolutionary Game Theoretic perspective on Multi-Agent Learning
Relating RL and EGT is not new. B¨orgers and Sarin were the first9to mathematically connect
RL with EGT [B¨or97]. Their work is located in the field of economics, where also other researchers
are very active on the link between RL and EGT, as for instance [Red01]. However this relation
has received far less attention in the field of AI, and more specifically in the field of Multi-Agent
Systems. B¨orgers and Sarin have made the formal connection between the Replicator Equations
and Cross Learning (a simple RL model) explicit in their work [B¨or97]. The evolutionary approach
to game theory attracts ever more attention of researchers from different fields, such as economics,
computer science and artificial intelligence [B¨or97, Gin00, Wei96, Sam97, Baz97, Now99a]. The
9As far as we know.
22
possible successful application of evolutionary game theoretic concepts and models in these
different fields becomes more and more apparent.
If the word evolution is used in the biological sense, then this means we are concerned with
environments in which behavior is genetically determined. Strategy selection then depends on the
reproductive success of their carriers, i.e. genes. Often, evolution is not intended to be understood
in a biological sense but rather as a learning process which we call cultural evolution [Bjo95].
Of course it is implicit and intuitive that there is an analogy between biological evolution and
learning. We can now look at this analogy at two different levels. First there is the individual
level. An individual decision maker usually has many ideas or strategies in his mind according to
which he can behave. Which one of these ideas dominates, and which ones are given less attention
depends on the experiences of the individual. We can regard such a set of ideas as a population of
possible behaviors. The changes which such a population undergoes in the individual’s mind can
be very similar to biological evolution. Secondly, it is possible that individual learning behavior
is different from biological evolution. An example is best response learning where individuals
adjust too rapidly to be similar to evolution. However, then it might be the case that at the
population level, consisting of different individuals, a process is operating analogous to biological
evolution. In this paper we describe the similarity between biological evolution and learning at
the individual level in a formal and experimental manner.
In this section we discuss or merely point out the results in making this relation between
Multi-Agent Reinforcement Learning and EGT explicit. B¨orgers and Sarin have shown how the
two fields are related in terms of dynamic behaviour, i.e. the relation between Cross learning
and the replicator dynamics. The replicator dynamics postulate gradual movement from worse
to better strategies. This is in contrast to classical Game Theory, which is a static theory and
does not prescribe the dynamics of adjustment to equilibrium. The main result of B¨orgers and
Sarin is that in an appropriately constructed continuous time limit, the Cross’ learning model
converges to the asymmetric, continuous time version of the replicator dynamics. The continuous
time limit is constructed in such a manner that each time interval sees many iterations of the
game, and that the adjustments that the players (or Cross learners) make between two iterations
of the game are very small. If the limit is constructed in this manner, the (stochastic) learning
process becomes in the limit deterministic. This limit process satisfies the system of differential
equations which characterizes the replicator dynamics. For more details see [B¨or97]. We illustrate
this result with the prisoners dilemma game. In Figure 7 we plotted the direction field of the
Replicator equations for the prisoner’s dilemma game and we also plotted the Cross learning
process for this same game.
0
0.2
0.4
0.6
0.8
1
y
0.2 0.4 0.6 0.8 1
x
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
Figure 7 Left: The direction field plot of the RD of the prisoner’s dilemma game. The x-axis represents
the probability with which the first player (or row player) plays defect, and the y-axis represents the
probability with which the second player (or column player) plays defect. The strong attractor and Nash
equilibrium of the game lies at coordinates (1,1) as one can see in the plot. Right: The paths induced
by the Cross learning process of the prisoner’s dilemma game. The arrows point out the direction of the
learning process. These probabilities are now learned by the Cross learning algorithm.
23
For both players we plotted the probability of choosing their first strategy (in this case defect).
The x-axis represents the probability with which the row player plays defect, and the y-axis
represents the probability with which the column player plays this same strategy. As you can see
the sample paths of the Cross learning process approximates the paths of the RD.
In previous work the authors have extended the results of B¨orgers and Sarin to popular
Reinforcement Learning (RL) models as Learning Automata (LA) and Q-learning. In [Tuy02]
the authors have shown that the Cross learning model is a Learning Automaton with a linear-
reward-inaction updating scheme. All details and experiments are available in [Tuy02].
Next, we continue with the mathematical relation between Q-learning and the Replicator
Dynamics. In [Tuy03b] we derived mathematically the dynamics of Boltzmann Q-learning.
We investigated here whether there is a possible relation with the evolutionary dynamics of
Evolutionary Game Theory. More precisely we constructed a continuous time limit of the Q-
learning model, where Q-values are interpreted as Boltzmann probabilities for the action selection,
in an analogous manner of B¨orgers and Sarin for Cross learning. We briefly summarize the findings
here. All details can be consulted in [Tuy03b]. The derivation has been restricted to a 2 player
situation for reasons of simplicity. Each agent(or player) has a probability vector over his action
set , more precisely x1, ..., xnover action set a1, ..., anfor the first player and y1, ..., ymover
b1, ..., bmfor the second player. Formally the Boltzmann distribution is described by,
xi(k) = eτ Qai(k)
Pn
j=1 eτ Qaj(k)
where xi(k) is the probability of playing strategy iat time step kand τis the temperature.
The temperature determines the degree of exploring different strategies. As the trade-off between
exploration-exploitation is very important in RL, it is important to set this parameter correctly.
Now suppose that we have payoff matrices Aand Bfor the 2 players. Calculating the time limit
results in, dxi
dt =xiατ((Ay)ix·Ay) + xiαX
j
xjln(xj
xi
) (25)
for the first player and analogously for the second player in,
dyi
dt =yiατ((Bx)iy·Bx) + yiαX
j
yjln(yj
yi
) (26)
Comparing (25) or (26) with the RD in (22), we see that the first term of (25) or (26) is exactly
the RD and thus takes care of the selection mechanism, see [Wei96]. The mutation mechanism
for Q-learning is therefore left in the second term, and can be rewritten as:
xiαX
j
xjln(xj)ln(xi) (27)
In equation (27) we recognize 2 entropy terms, one over the entire probability distribution x,
and one over strategy xi. Relating entropy and mutation is not new. It is a well known fact
[Schneid00, Sta99] that mutation increases entropy. In [Sta99], it is stated that the concepts
are familiar with thermodynamics in the following sense: the selection mechanism is analogous
to energy and mutation to entropy. So generally speaking, mutations tend to increase entropy.
Exploration can be considered as the mutation concept, as both concepts take care of providing
variety.
Equations 25 and 26 now express the dynamics of both Q-learners in terms of Boltzmann
probabilities, from which the RD emerge.
In [Tuy03c] we answered the question whether it is possible to first define the dynamic behavior
in terms of Evolutionary Game Theory (EGT) and then develop the appropriate RL-algorithm.
24
For these reasons we extended the RD of EGT. We call it the Extended Replicator Dynamics.
All details on this work can be found in [Tuy03c]. The main result is that the extended dynamics
guarantee an evolutionary stable outcome in all types of 1-stage games.
Finally, in [Hoe04] the authors have shown how the EGT approach can be used for Dispersion
Games [Gre02]. In this cooperative game, nagents must learn to choose from ktasks using local
rewards and full utility is only achieved if each agent chooses a distinct task. We visualized the
learning process of the MAS and showed typical phenomena of distributed learning in a MAS.
Moreover, we showed how the derived fine tuning of parameter settings from the RD can support
application of the COllective INtelligence (COIN) framework of Wolpert et al. [Wol98, Wol99]
using dispersion games. COIN is a proved engineering approach for learning of cooperative tasks
in MASs. Broadly speaking, COIN defines the conditions that an agent’s private utility function
has to meet to increase the probability that learning to optimize this function leads to increased
performance of the collective of agents. Thus, the challenge is to define suitable private utility
functions for the individual agents, given the performance of the collective. We showed that the
derived link between RD and RL predicts performance of the COIN framework and visualizes
the incentives provided in COIN toward cooperative behavior.
5 Final Remarks
In this survey paper we investigated Reinforcement Learning and Evolutionary Game Theory
in a Multi-Agent setting. We provided most of the fundamentals of RL and EGT and moreover
showed their remarkable similarities. We also discussed some of the excellent existing Multi-agent
RL algorithms today and gave a more detailed description of the Evolutionary Game Theoretic
approach of the authors. However, still a lot of work needs to be done and some problems are still
unresolved. Especially, overcoming problems of incomplete information and large state spaces,
in Multi-Agent Systems as for instance Sensor Webs, are still hard. More precisely, under these
conditions it becomes impossible to learn models over other agents, storing information on them
and using a lot of communication.
6 Acknowledgments
After writing this survey paper an acknowledgment is in place. We wish to thank our colleagues
of the Computational Modeling Lab from the Vrije Universiteit Brussel, Belgium. Most of all we
want to express our appreciation and gratitude to Katja Verbeeck and Maarten Peeters for their
important input and support in writing this article.
We also wish to express our gratitude to Dr. ir Pieter-Jan ’t Hoen of the CWI (center for
mathematics and computer science) in the Netherlands, especially for his input on the COIN
framework.
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... No agent has the surety to be fully informed about the other agents' objectives nor has it the surety to be fully informed about the overall state of the environment. 99,101 While focusing on the agent's collaboration, it perceives insight from other social structures in the animal kingdom that contain an assortment of individuals with their characters. It additionally draws strongly on Game Theory (GT). ...
... To overcome this challenge and formally study the multi-agent learning dynamics in strategic interactions, Evolutionary Game Theory (EGT) has been successfully employed. 101 Borgers and Sarin 103 were the first to mathematically associate agents learning with EGT, portrayed by how agents lack complete information, and both these fields are involved with dynamic environments with an extreme level of uncertainty. 101 The concepts of evolution in EGT prove appropriate to define the learning process in MAS instead of being realized in biological evolution meaning. ...
... 101 Borgers and Sarin 103 were the first to mathematically associate agents learning with EGT, portrayed by how agents lack complete information, and both these fields are involved with dynamic environments with an extreme level of uncertainty. 101 The concepts of evolution in EGT prove appropriate to define the learning process in MAS instead of being realized in biological evolution meaning. 104 This learning process of the agents is controlled by RD that helps them perform dynamically in the face of competition or real-world conditions. ...
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... Similarities between evolutionary game theory (not restricted to its application in epidemiology) and RL (not restricted to MAB) had been previously pointed out, mostly in the economic field (Börgers et al. 1997;Vega-Redondo 2003). Their relation has however received less attention from the RL community (Tuyls et al. 2005). In the field of epidemiology, Shi et al. 2019 considered RL and evolutionary game theory in a context of human decision-making regarding voluntary vaccination. ...
Thesis
Cette thèse porte sur la modélisation et l'optimisation de la maîtrise d'un agent pathogène se propageant dans une metapopulation d’animaux d’élevage via un réseau d'échanges commerciaux, en tenant compte des processus de décision concernant l'adoption de mesures de maîtrise.D'une part, concernant la prise de décision des éleveurs, un modèle stochastique intégrant la dynamique intra-troupeau de la maladie (composantes démographiques et réseau d’échanges) et la dynamique des décisions des éleveurs a été développé et exploré par simulations intensives et analyses de sensibilité. En particulier, un mécanisme de décision dynamique qui tient compte du comportement aléatoire des éleveurs, de leur apprentissage et de la dynamique d'imitation stratégique a été proposé. Le modèle a été formalisé pour une dynamique d'infection théorique (modèle SIR), et une mesure de contrôle spécifique (vaccination). Ce premier modèle a été étendu et adapté à une maladie réelle, la BVD (diarrhée virale bovine), où tant la transmission de l'agent pathogène que les échanges d'informations entre éleveurs peuvent passer par le réseau d'échanges, mais aussi par un voisinage géographique.D'autre part, dans une perspective plus générale, il a été supposé qu'un planificateur social central cherchait à allouer dynamiquement et de manière optimale une ressource limitée entre les différentes sous-populations d'un réseau de métapopulation donné, afin de réduire la propagation d’un agent pathogène. L'approche, basée sur des scores permettant de classer les sous-populations pour l'allocation des ressources, a été formalisée pour le modèle épidémiologique théorique considéré dans la première partie de la thèse, pour deux mesures différentes (vaccination et traitement). De nouveaux scores ont été obtenus par l'adaptation d'une approche d'optimisation gloutonne au cadre des métapopulations. Par le biais de simulations, les performances de ces nouveaux scores ont été comparées à celles de plusieurs heuristiques qui pourraient être appropriées lorsque le réseau de métapopulation correspond à un réseau de commerce d'animaux.
... 7) Evolutionary Game Theory: Combining evolutionary game theory concepts with RL opens many interesting possibilities. The benefit of evolutionary game theory over traditional game theory is the fact that it does not require agents to be hyper-rational players, and an equilibrium can change dynamically [219]. Evolutionary game theory has even been proposed as the preferable framework for studying multiagent learning formally [220]. ...
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Future AI applications require performance, reliability and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the device-edge-cloud continuum, and focus on AI for edge, that is, the AI methods used in resource orchestration. We claim that to support the constantly growing requirements of intelligent applications in the device-edge-cloud computing continuum, resource orchestration needs to embrace edge AI and emphasize local autonomy and intelligence. To justify the claim, we provide a general definition for continuum orchestration, and look at how current and emerging orchestration paradigms are suitable for the computing continuum. We describe certain major emerging research themes that may affect future orchestration, and provide an early vision of an orchestration paradigm that embraces those research themes. Finally, we survey current key edge AI methods and look at how they may contribute into fulfilling the vision of future continuum orchestration.
... In particular, reinforcement learning, one of the machine learning methods, aims at solving the problem that agents use learning strategies to maximize their returns during the process of interacting with the environment [6][7][8][9][10] . Due to its excellent intrinsic propriety, under which agents learn by "trial and error" to reach their maximum rewards, reinforcement learning has been widely applied to many other fields, such as epidemic prevention [11][12][13] , automatic control [ 14 , 15 ], decision-making theory in games [16][17][18][19][20] , and so on [21][22][23][24][25] . ...
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Recent studies show that different update rules are invariant regarding the evolutionary outcomes for a well-mixed population or homogeneous network. In this paper, we investigate how the Q-learning algorithm, one of the reinforcement learning methods, affects the evolutionary outcomes in square lattice. Especially, we consider the mixed strategy update rule, among which some agents adopt Q-learning method to update their strategies, the proportion of these agents (these agents are denoted as Artificial Intelligence (AI)) is controlled by a simple parameter ρ. The rest of other agents, the proportion is denoted by 1 − ρ, adopt the Fermi function to update their strategies. Through extensive numerical simulations, we found that the mixed strategy-update rule can facilitate cooperation compared with the pure Fermi-function-based update rule. Besides, if the proportion of AI is moderate, cooperators among the whole population exhibit conditional behavior and moody conditional behavior. However, if the whole population adopts the pure Fermi-function-based strategy update rule or the pure Q-learning-based strategy update rule, then cooperators among the whole population exhibit the hump-shaped conditional behavior. Our results provide a new insight to understand the evolution of cooperation from AI's view.
... Hou et al. [7] found that the intrinsic nature of parallelism of evolution in a population is ideal for MAS and thus presented an algorithm by integrating the evolution theory with reinforcement learning to analyze the knowledge transfer which happens simultaneously in a MAS system. Tuyls and Nowe [35] also investigated the relationship between MARL and evolutionary game theory, focusing on static tasks [36]. Prediction is another standard for convergence, Camerer and Hua [15] attempted to use experimental data to estimate the parameters and validate the model by predicting the behavior with the integration of reinforcement learning and fictitious play. ...
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Agents face challenges to achieve adaptability and stability when interacting with dynamic counterparts in a complex multiagent system (MAS). To strike a balance between these two goals, this paper proposes a learning algorithm for heterogeneous agents with bounded rationality. It integrates reinforcement learning as well as fictitious play to evaluate the historical information and adopt mechanisms in evolutionary game to adapt to uncertainty, which is referred to as experience weighted learning (EWL) in this paper. We have conducted multiagent simulations to test the performance of EWL in various games. The results demonstrate that the average payoff of EWL exceeds that of the baseline in all 4 games. In addition, we find that most of the EWL agents converge to pure strategy and become stable finally. Furthermore, we test the impact of 2 import parameters, respectively. The results show that the performance of EWL is quite stable and there is a potential to improve its performance by parameter optimization.
... Recently, multiagent reinforcement learning (MARL) has emerged as a framework for the study of social interactions (Kleiman-Weiner et al., 2016;Peysakhovich and Lerer, 2018;Tuyls and Noeé, 2005) by generalizing traditional models of social behaviour to more realistic scenarios in two important ways. First, it enables study of more complex environments than the ones covered by traditional game theory methods; the environments can be spatial, and temporally extended, grounding the abstract conceptions of actions and interactions in a rich world. ...
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Undesired bias afflicts both human and algorithmic decision making, and may be especially prevalent when information processing trade-offs incentivize the use of heuristics. One primary example is \textit{statistical discrimination} -- selecting social partners based not on their underlying attributes, but on readily perceptible characteristics that covary with their suitability for the task at hand. We present a theoretical model to examine how information processing influences statistical discrimination and test its predictions using multi-agent reinforcement learning with various agent architectures in a partner choice-based social dilemma. As predicted, statistical discrimination emerges in agent policies as a function of both the bias in the training population and of agent architecture. All agents showed substantial statistical discrimination, defaulting to using the readily available correlates instead of the outcome relevant features. We show that less discrimination emerges with agents that use recurrent neural networks, and when their training environment has less bias. However, all agent algorithms we tried still exhibited substantial bias after learning in biased training populations.
... First, for an independent learner, the individual Q-function of each agent is influenced by the other agents'actions. The analysis of independent learners focuses on repeated games with few agents and actions [13]- [18]. Second, to alleviate the non-stationarity problem, centralized learning is employed to estimate the global Q-function. ...
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Multi-agent reinforcement learning (MARL) has become a prevalent method for solving cooperative problems owing to its tractable implementation and task distribution. The goal of the MARL algorithms for fully cooperative scenarios is to obtain the optimal joint strategy that maximizes the expected common cumulative reward for all agents. However, to date, the analysis of MARL dynamics has focused on repeated games with few agents and actions. To this end, we propose a cooperative MARL algorithm based on the coordination degree (CMARL-CD) and analyze its dynamics in more general cases in which repeated games with more agents and actions are considered. Theoretical analysis shows that if the component action of every optimal joint action is unique, all optimal joint actions are asymptotically stable critical points. The CMARL-CD algorithm realizes coordination among agents without the need to estimate the global Q-value function. Each agent estimates the coordination degree of its own action, which represents the potential of being the optimal action. The efficacy of the CMARL-CD algorithm is studied through repeated games and stochastic games.
... This connection opened up all the tools of dynamical systems theory to the study of collective learning. The relationship between the two fields is as follows: one population with a frequency over phenotypes in the evolutionary setting corresponds to one agent with a frequency over actions in the learning setting [103]. The convergence to the replicator dynamics has also been shown for stateless Q-learning [86,104], following some mathematical limiting procedure. ...
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A dynamical systems perspective on multi-agent learning, based on the link between evolutionary game theory and reinforcement learning, provides an improved, qualitative understanding of the emerging collective learning dynamics. However, confusion exists with respect to how this dynamical systems account of multi-agent learning should be interpreted. In this article, I propose to embed the dynamical systems description of multi-agent learning into different abstraction levels of cognitive analysis. The purpose of this work is to make the connections between these levels explicit in order to gain improved insight into multi-agent learning. I demonstrate the usefulness of this framework with the general and widespread class of temporal-difference reinforcement learning. I find that its deterministic dynamical systems description follows a minimum free-energy principle and unifies a boundedly rational account of game theory with decision-making under uncertainty. I then propose an on-line sample-batch temporal-difference algorithm which is characterized by the combination of applying a memory-batch and separated state-action value estimation. I find that this algorithm serves as a micro-foundation of the deterministic learning equations by showing that its learning trajectories approach the ones of the deterministic learning equations under large batch sizes. Ultimately, this framework of embedding a dynamical systems description into different abstraction levels gives guidance on how to unleash the full potential of the dynamical systems approach to multi-agent learning.
... Game Theory can be defined as the study of mathematical models of conflict and cooperation between intelligent rational decision-makers. [1] It can be applied in different ambit of Artificial Intelligence, such as AI Pluribus in multiplayer poker [2,3], Multi-agent AI systems [4,5], Imitation and Reinforcement Learning [6,7] and so on. ...
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In many real-world games, such as traders repeatedly bargaining with customers, it is very hard for a single AI trader to make good deals with various customers in a few turns, since customers may adopt different strategies even the strategies they choose are quite simple. In this paper, we model this problem as fast adaptive learning in the finitely repeated games. We believe that past game history plays a vital role in such a learning procedure, and therefore we propose a novel framework (named, F3) to fuse the past and current game history with an Opponent Action Estimator (OAE) module that uses past game history to estimate the opponent's future behaviors. The experiments show that the agent trained by F3 can quickly defeat opponents who adopt unknown new strategies. The F3 trained agent obtains more rewards in a fixed number of turns than the agents that are trained by deep reinforcement learning. Further studies show that the OAE module in F3 contains meta-knowledge that can even be transferred across different games.
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Stochastic automata operating in an unknown random environment have been proposed earlier as models of learning. These automata update their action probabilities in accordance with the inputs received from the environment and can improve their own performance during operation. In this context they are referred to as learning automata. A survey of the available results in the area of learning automata has been attempted in this paper. Attention has been focused on the norms of behavior of learning automata, issues in the design of updating schemes, convergence of the action probabilities, and interaction of several automata. Utilization of learning automata in parameter optimization and hypothesis testing is discussed, and potential areas of application are suggested.
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This article introduces a class of incremental learning procedures specialized for prediction – that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between predicted and actual outcomes, the new methods assign credit by means of the difference between temporally successive predictions. Although such temporal-difference methods have been used in Samuel's checker player, Holland's bucket brigade, and the author's Adaptive Heuristic Critic, they have remained poorly understood. Here we prove their convergence and optimality for special cases and relate them to supervised-learning methods. For most real-world prediction problems, temporal-difference methods require less memory and less peak computation than conventional methods and they produce more accurate predictions. We argue that most problems to which supervised learning is currently applied are really prediction problems of the sort to which temporal-difference methods can be applied to advantage.
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Conflicts between animals of the same species usually are of ``limited war'' type, not causing serious injury. This is often explained as due to group or species selection for behaviour benefiting the species rather than individuals. Game theory and computer simulation analyses show, however, that a ``limited war'' strategy benefits individual animals as well as the species.
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