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

With large-scale integration of renewable generation and distributed energy resources,
modern power systems are confronted with new operational challenges, such as growing …

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 …

Robust reinforcement learning (RL) is to find a policy that optimizes the worst-case
performance over an uncertainty set of MDPs. In this paper, we focus on model-free robust …

Since reinforcement learning algorithms are notoriously data-intensive, the task of sampling
observations from the environment is usually split across multiple agents. However …

In safe reinforcement learning (SRL) problems, an agent explores the environment to
maximize an expected total reward and meanwhile avoids violation of certain constraints on …

SARSA is an on-policy algorithm to learn a Markov decision process policy in reinforcement
learning. We investigate the SARSA algorithm with linear function approximation under the …

Actor-critic (AC) methods have exhibited great empirical success compared with other
reinforcement learning algorithms, where the actor uses the policy gradient to improve the …

We investigate the sample efficiency of reinforcement learning in a $\gamma $-discounted
infinite-horizon Markov decision process (MDP) with state space S and action space A …

Q-learning, which seeks to learn the optimal Q-function of a Markov decision process (MDP)
in a model-free fashion, lies at the heart of reinforcement learning. When it comes to the …