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

Reinforcement learning (RL) algorithms find applications in inventory control, recommender
systems, vehicular traffic management, cloud computing, and robotics. The real-world …

In a standard view of the reinforcement learning problem, an agent's goal is to efficiently
identify a policy that maximizes long-term reward. However, this perspective is based on a …

We propose a black-box reduction that turns a certain reinforcement learning algorithm with
optimal regret in a (near-) stationary environment into another algorithm with optimal …

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

Reinforcement learning (RL) methods learn optimal decisions in the presence of a stationary
environment. However, the stationary assumption on the environment is very restrictive. In …

We consider un-discounted reinforcement learning (RL) in Markov decision processes
(MDPs) under drifting non-stationarity,\ie, both the reward and state transition distributions …

We develop a model selection approach to tackle reinforcement learning with adversarial
corruption in both transition and reward. For finite-horizon tabular MDPs, without prior …

We study online reinforcement learning in average-reward stochastic games (SGs). An SG
models a two-player zero-sum game in a Markov environment, where state transitions and …

We consider model-free reinforcement learning (RL) in non-stationary Markov decision
processes. Both the reward functions and the state transition functions are allowed to vary …