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

Multi-agent reinforcement learning (MARL) has long been a significant research topic in
both machine learning and control systems. Recent development of (single-agent) deep …

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 …

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 …

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 …

The recently proposed distribution correction estimation (DICE) family of estimators has
advanced the state of the art in off-policy evaluation from behavior-agnostic data. While …

Despite the success of single-agent reinforcement learning, multi-agent reinforcement
learning (MARL) remains challenging due to complex interactions between agents …

Despite the empirical success of the actor-critic algorithm, its theoretical understanding lags
behind. In a broader context, actor-critic can be viewed as an online alternating update …