Natural policy gradient (NPG) methods are among the most widely used policy optimization
algorithms in contemporary reinforcement learning. This class of methods is often applied in …

Abstract Model-based reinforcement learning (RL), which finds an optimal policy using an
empirical model, has long been recognized as one of the cornerstones of RL. It is especially …

Abstract The combination of Monte-Carlo tree search (MCTS) with deep reinforcement
learning has led to groundbreaking results in artificial intelligence. However, AlphaZero, the …

We address the challenge of exploration in reinforcement learning (RL) when the agent
operates in an unknown environment with sparse or no rewards. In this work, we study the …

Robust Markov decision processes (MDPs) provide a general framework to model decision
problems where the system dynamics are changing or only partially known. Efficient …

Incorporating expert demonstrations has empirically helped to improve the sample efficiency
of reinforcement learning (RL). This paper quantifies theoretically to what extent this extra …

We propose MDP-GapE, a new trajectory-based Monte-Carlo Tree Search algorithm for
planning in a Markov Decision Process in which transitions have a finite support. We prove …

We study robust Markov games (RMG) with $ s $-rectangular uncertainty. We show a
general equivalence between computing a robust Nash equilibrium (RNE) of a $ s …

Ratio maximization has applications in areas as diverse as finance, reward shaping for
reinforcement learning (RL), and the development of safe artificial intelligence, yet there has …

We consider the problem of planning in a Markov Decision Process (MDP) with a generative
model and limited computational budget. Despite the underlying MDP transitions having a …