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

Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …

Offline (or batch) reinforcement learning (RL) algorithms seek to learn an optimal policy from
a fixed dataset without active data collection. Based on the composition of the offline dataset …

Offline or batch reinforcement learning seeks to learn a near-optimal policy using history
data without active exploration of the environment. To counter the insufficient coverage and …

We study reinforcement learning (RL) with linear function approximation where the
underlying transition probability kernel of the Markov decision process (MDP) is a linear …

The goal of robust reinforcement learning (RL) is to learn a policy that is robust against the
uncertainty in model parameters. Parameter uncertainty commonly occurs in many real …

While policy-based reinforcement learning (RL) achieves tremendous successes in practice,
it is significantly less understood in theory, especially compared with value-based RL. In …

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 …

This paper investigates model robustness in reinforcement learning (RL) via the framework
of distributionally robust Markov decision processes (RMDPs). Despite recent efforts, the …

Consider a Markov decision process (MDP) that admits a set of state-action features, which
can linearly express the process's probabilistic transition model. We propose a parametric Q …