Muhammad Burhan Hafez

University of Hamburg | UHH

Human infant learning happens during exploration of the environment, by interaction with objects, and by listening to and repeating utterances casually, which is analogous to unsupervised learning. Only occasionally, a learning infant would receive a matching verbal description of an action it is committing, which is similar to supervised learning....

Human infant learning happens during exploration of the environment, by interaction with objects, and by listening to and repeating utterances casually, which is analogous to unsupervised learning. Only occasionally, a learning infant would receive a matching verbal description of an action it is committing, which is similar to supervised learning....

Sound is one of the most informative and abundant modalities in the real world while being robust to sense without contacts by small and cheap sensors that can be placed on mobile devices. Although deep learning is capable of extracting information from multiple sensory inputs, there has been little use of sound for the control and learning of robo...

Recent advances in robot learning have enabled robots to become increasingly better at mastering a predefined set of tasks. On the other hand, as humans, we have the ability to learn a growing set of tasks over our lifetime. Continual robot learning is an emerging research direction with the goal of endowing robots with this ability. In order to le...

Recent advances in robot learning have enabled robots to become increasingly better at mastering a predefined set of tasks. On the other hand, as humans, we have the ability to learn a growing set of tasks over our lifetime. Continual robot learning is an emerging research direction with the goal of endowing robots with this ability. In order to le...

Using a model of the environment, reinforcement learning agents can plan their future moves and achieve super-human performance in board games like Chess, Shogi, and Go, while remaining relatively sample-efficient. As demonstrated by the MuZero Algorithm, the environment model can even be learned dynamically, generalizing the agent to many more tas...

Using a model of the environment, reinforcement learning agents can plan their future moves and achieve superhuman performance in board games like Chess, Shogi, and Go, while remaining relatively sample-efficient. As demonstrated by the MuZero Algorithm, the environment model can even be learned dynamically, generalizing the agent to many more task...

Combining model-based and model-free learning systems has been shown to improve the sample efficiency of learning to perform complex robotic tasks. However, dual-system approaches fail to consider the reliability of the learned model when it is applied to make multiple-step predictions, resulting in a compounding of prediction errors and performanc...

Combining model-based and model-free learning systems has been shown to improve the sample efficiency of learning to perform complex robotic tasks. However, dual-system approaches fail to consider the reliability of the learned model when it is applied to make multiple-step predictions, resulting in a compounding of prediction errors and performanc...

Combining model-based and model-free deep reinforcement learning has shown great promise for improving sample efficiency on complex control tasks while still retaining high performance. Incorporating imagination is a recent effort in this direction inspired by human mental simulation of motor behavior. We propose a learning-adaptive imagination app...

Combining model-based and model-free deep reinforcement learning has shown great promise for improving sample efficiency on complex control tasks while still retaining high performance. Incorporating imagination is a recent effort in this direction inspired by human mental simulation of motor behavior. We propose a learning-adaptive imagination app...

Abstract—Recent success in deep reinforcement learning for continuous control has been dominated by model-free approaches which, unlike model-based approaches, do not suffer from representational limitations in making assumptions about the world dynamics and model errors inevitable in complex do-mains. However, they require a lot of experiences com...

Recent success in deep reinforcement learning for continuous control has been dominated by model-free approaches which, unlike model-based approaches, do not suffer from representational limitations in making assumptions about the world dynamics and model errors inevitable in complex domains. However, they require a lot of experiences compared to m...

In this paper, we present a new intrinsically motivated actor-critic algorithm for learning continuous motor skills directly from raw visual input. Our neural architecture is composed of a critic and an actor network. Both networks receive the hidden representation of a deep convolutional autoencoder which is trained to reconstruct the visual input...

In this paper, we present a new intrinsically motivated actor-critic algorithm for learning continuous motor skills directly from raw visual input. Our neural architecture is composed of a critic and an actor network. Both networks receive the hidden representation of a deep convolutional autoencoder which is trained to reconstruct the visual input...

In this paper, we present a new visually guided exploration approach for autonomous learning of visuomotor skills. Our approach uses hierarchical Slow Feature Analysis for unsupervised learning of efficient state representation and an Intrinsically motivated Continuous Actor-Critic learner for neuro-optimal control. The system learns online an ense...

Guiding the action selection mechanism of an autonomous agent for learning control behaviors is a crucial issue in reinforcement learning. While classical approaches to reinforcement learning seem to be deeply dependent on external feedback, intrinsically motivated approaches are more natural and follow the principles of infant sensorimotor develop...

Many studies have been conducted for modeling the underlying non-linear relationship between pricing attributes and price of property to forecast the housing sales prices. In recent years, more advanced non-linear modeling techniques such as Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have emerged as effective techniques to p...

Improving the learning convergence of reinforcement learning (RL) in mobile robot navigation has been the interest of many recent works that have investigated different approaches to obtain knowledge from effectively and efficiently exploring the robot’s environment. In RL, this knowledge is of great importance for reducing the high number of inter...

Recent works involved in enhancing the learning convergence of reinforcement learning (RL) in mobile robot navigation have investigated methods to obtain knowledge from efficiently exploring the robot's environment. In RL, this knowledge is highly desirable to reduce the high number of interactions required for updating the value function and to ev...