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 …

Stochastic optimal control and games have a wide range of applications, from finance and
economics to social sciences, robotics, and energy management. Many real-world …

We consider infinite-horizon discounted Markov decision problems with finite state and
action spaces and study the convergence rates of the projected policy gradient method and …

We study sequential decision-making problems in which each agent aims to maximize the
expected total reward while satisfying a constraint on the expected total utility. We employ …

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 …

In the classical Reinforcement Learning from Human Feedback (RLHF) framework, Proximal
Policy Optimization (PPO) is employed to learn from sparse, sentence-level rewards--a …

We present new policy mirror descent (PMD) methods for solving reinforcement learning
(RL) problems with either strongly convex or general convex regularizers. By exploring 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 …

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 …