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 …

Many recent successful (deep) reinforcement learning algorithms make use of
regularization, generally based on entropy or Kullback-Leibler divergence. We propose a …

We establish a new connection between value and policy based reinforcement learning
(RL) based on a relationship between softmax temporal value consistency and policy …

In this paper, we utilize results from convex analysis and monotone operator theory to derive
additional properties of the softmax function that have not yet been covered in the existing …

When function approximation is used, solving the Bellman optimality equation with stability
guarantees has remained a major open problem in reinforcement learning for decades. The …

This paper presents a general mean-field game (GMFG) framework for simultaneous
learning and decision-making in stochastic games with a large population. It first establishes …

We propose a general framework for entropy-regularized average-reward reinforcement
learning in Markov decision processes (MDPs). Our approach is based on extending the …

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 …