This article surveys reinforcement learning approaches in social robotics. Reinforcement
learning is a framework for decision-making problems in which an agent interacts through …

Cooperative multi-agent systems (MAS) are ones in which several agents attempt, through
their interaction, to jointly solve tasks or to maximize utility. Due to the interactions among the …

A high school student can create deep Q-learning code to control her robot, without any
understanding of the meaning of'deep'or'Q', or why the code sometimes fails. This book is …

This article presents a general class of associative reinforcement learning algorithms for
connectionist networks containing stochastic units. These algorithms, called REINFORCE …

Planning algorithms are impacting technical disciplines and industries around the world,
including robotics, computer-aided design, manufacturing, computer graphics, aerospace …

Theoretical neuroscience provides a quantitative basis for describing what nervous systems
do, determining how they function, and uncovering the general principles by which they …

In the Markov decision process (MDP) formalization of reinforcement learning, a single
adaptive agent interacts with an environment defined by a probabilistic transition function. In …

To date, reinforcement learning has mostly been studied solving simple learning tasks.
Reinforcement learning methods that have been studied so far typically converge slowly …

INTRODUCTION TO THE THEORY OF NEURAL COMPUTATION Page 1 Page 2
INTRODUCTION TO THE THEORY OF NEURAL COMPUTATION Page 3 Page 4 …

This paper extends previous work with Dyna, a class of architectures for intelligent systems
based on approximating dynamic programming methods. Dyna architectures integrate trial …