Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …

Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

We consider the fully decentralized multi-agent reinforcement learning (MARL) problem,
where the agents are connected via a time-varying and possibly sparse communication …

Reinforcement learning is a learning paradigm concerned with learning to control a system
so as to maximize a numerical performance measure that expresses a long-term objective …

Solving tasks in Reinforcement Learning is no easy feat. As the goal of the agent is to
maximize the accumulated reward, it often learns to exploit loopholes and misspecifications …

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 …

In many sequential decision-making problems one is interested in minimizing an expected
cumulative cost while taking into account risk, ie, increased awareness of events of small …

Temporal difference learning (TD) is a simple iterative algorithm used to estimate the value
function corresponding to a given policy in a Markov decision process. Although TD is one of …

This paper develops the first policy gradient method with global optimality guarantee and
complexity analysis for robust reinforcement learning under model mismatch. Robust …

Policy gradient methods with actor-critic schemes demonstrate tremendous empirical
successes, especially when the actors and critics are parameterized by neural networks …