Abstract Machine Learning (ML) has been enjoying an unprecedented surge in applications
that solve problems and enable automation in diverse domains. Primarily, this is due to 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 …

Batch reinforcement learning is a subfield of dynamic programming-based reinforcement
learning. Originally defined as the task of learning the best possible policy from a fixed set of …

We present several new algorithms for multiagent reinforcement learning. A common feature
of these algorithms is a parameterized, structured representation of a policy or value …

We present a batch reinforcement learning (RL) algorithm that provides probabilistic
guarantees about the quality of each policy that it proposes, and which has no hyper …

Reinforcement learning offers one of the most general framework to take traditional robotics
towards true autonomy and versatility. However, applying reinforcement learning to high …

Error Bounds for Approximate Policy Iteration RÚmi Munos, Page 1 Error Bounds for Approximate
Policy Iteration RÚmi Munos, Centre de MathÚmatiques AppliquÚes, Ecole Polytechnique …

Reinforcement Learning (RL) is an approach for solving complex multi-stage decision
problems that fall under the general framework of Markov Decision Problems (MDPs), with …

We show that several popular discounted reward natural actor-critics, including the popular
NAC-LSTD and eNAC algorithms, do not generate unbiased estimates of the natural policy …

The basic tools of machine learning appear in the inner loop of most reinforcement learning
algorithms, typically in the form of Monte Carlo methods or function approximation …