The rapid changes in the finance industry due to the increasing amount of data have
revolutionized the techniques on data processing and data analysis and brought new …

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

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 …

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 …

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 …

We consider the dynamics of a linear stochastic approximation algorithm driven by
Markovian noise, and derive finite-time bounds on the moments of the error, ie, deviation of …

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 …

In this paper we introduce a simple approach for exploration in reinforcement learning (RL)
that allows us to develop theoretically justified algorithms in the tabular case but that is also …

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 …