This paper analyzes a two-timescale stochastic algorithm framework for bilevel optimization.
Bilevel optimization is a class of problems which exhibits a two-level structure, and its goal 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 …

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 …

Actor-critic algorithm and their extensions have made great achievements in real-world
decision-making problems. In contrast to its empirical success, the theoretical understanding …

Reinforcement learning, mathematically described by Markov Decision Problems, may be
approached either through dynamic programming or policy search. Actor-critic algorithms …

Whittle index policy is a heuristic to the intractable restless multi-armed bandits (RMAB)
problem. Although it is provably asymptotically optimal, finding Whittle indices remains …

Linear two-timescale stochastic approximation (SA) scheme is an important class of
algorithms which has become popular in reinforcement learning (RL), particularly for the …

We investigate safe multi-agent reinforcement learning, where agents seek to collectively
maximize an aggregate sum of local objectives while satisfying their own safety constraints …

As an important type of reinforcement learning algorithms, actor-critic (AC) and natural actor-
critic (NAC) algorithms are often executed in two ways for finding optimal policies. In the first …

This paper provides a finite-time analysis of linear stochastic approximation (LSA)
algorithms with fixed step size, a core method in statistics and machine learning. LSA is …