# Elliot Catt's research while affiliated with Australian National University and other places

**What is this page?**

This page lists the scientific contributions of an author, who either does not have a ResearchGate profile, or has not yet added these contributions to their profile.

It was automatically created by ResearchGate to create a record of this author's body of work. We create such pages to advance our goal of creating and maintaining the most comprehensive scientific repository possible. In doing so, we process publicly available (personal) data relating to the author as a member of the scientific community.

If you're a ResearchGate member, you can follow this page to keep up with this author's work.

If you are this author, and you don't want us to display this page anymore, please let us know.

It was automatically created by ResearchGate to create a record of this author's body of work. We create such pages to advance our goal of creating and maintaining the most comprehensive scientific repository possible. In doing so, we process publicly available (personal) data relating to the author as a member of the scientific community.

If you're a ResearchGate member, you can follow this page to keep up with this author's work.

If you are this author, and you don't want us to display this page anymore, please let us know.

## Publications (8)

Reliable generalization lies at the heart of safe ML and AI. However, understanding when and how neural networks generalize remains one of the most important unsolved problems in the field. In this work, we conduct an extensive empirical study (2200 models, 16 tasks) to investigate whether insights from the theory of computation can predict the lim...

Reinforcement Learning formalises an embodied agent's interaction with the environment through observations, rewards and actions. But where do the actions come from? Actions are often considered to represent something external, such as the movement of a limb, a chess piece, or more generally, the output of an actuator. In this work we explore and f...

Reinforcement learners are agents that learn to pick actions that lead to high reward. Ideally, the value of a reinforcement learner’s policy approaches optimality–where the optimal informed policy is the one which maximizes reward. Unfortunately, we show that if an agent is guaranteed to be “asymptotically optimal” in any (stochastically computabl...

In this technical report we give an elementary introduction to Quantum Computing for non-physicists. In this introduction we describe in detail some of the foundational Quantum Algorithms including: the Deutsch-Jozsa Algorithm, Shor's Algorithm, Grocer Search, and Quantum Counting Algorithm and briefly the Harrow-Lloyd Algorithm. Additionally we gi...

Reinforcement Learning agents are expected to eventually perform well. Typically, this takes the form of a guarantee about the asymptotic behavior of an algorithm given some assumptions about the environment. We present an algorithm for a policy whose value approaches the optimal value with probability 1 in all computable probabilistic environments...

Reinforcement Learning agents are expected to eventually perform well. Typically, this takes the form of a guarantee about the asymptotic behavior of an algorithm given some assumptions about the environment. We present an algorithm for a policy whose value approaches the optimal value with probability 1 in all computable probabilistic environments...

The off-switch game is a game theoretic model of a highly intelligent robot interacting with a human. In the original paper by Hadfield-Menell et al. (2016), the analysis is not fully game-theoretic as the human is modelled as an irrational player, and the robot's best action is only calculated under unrealistic normality and soft-max assumptions....

## Citations

... As payload is encrypted, it cannot reveal any information. Shannon's [35] and Kolmogorov's [36] entropy values seem promising and can predict some sort of fixed pattern in the payload. Shannon's entropy basically measures the uncertainty of data, whereas Kolmogorov's entropy measures the uncertainty of compressed data. ...

... Kolmogorov complexity has also been considered in the context of reinforcement learning as a tool for complexityconstrained inference [9], [2], [15] based on Solomonoff's theory of inductive inference [19]. We differ by focusing instead on constraining the computational complexity of the obtained policy itself, assuming the underlying system to be known. ...

... Potential control methodologies for superintelligence have been classified into two broad categories, namely capability control and motivational controlbased methods [59]. Capability control methods attempt to limit any harm that the ASI system is able to do by placing it in a restricted environment [38,[60][61][62], adding shut-off mechanisms [63,64], or trip wires [38]. Motivational control methods attempt to design ASI to desire not to cause harm even in the absence of handicapping capability controllers. ...