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

Actor-critic methods are widely used in offline reinforcement learningpractice, but are not so
well-understood theoretically. We propose a newoffline actor-critic algorithm that naturally …

We study reinforcement learning (RL) with linear function approximation. For episodic time-
inhomogeneous linear Markov decision processes (linear MDPs) whose transition …

We study the exploration problem with approximate linear action-value functions in episodic
reinforcement learning under the notion of low inherent Bellman error, a condition normally …

Two central paradigms have emerged in the reinforcement learning (RL) community: online
RL and offline RL. In the online RL setting, the agent has no prior knowledge of the …

The success of reinforcement learning in a variety of challenging sequential decision-
making problems has been much discussed, but often ignored in this discussion is the …

Direct policy gradient methods for reinforcement learning are a successful approach for a
variety of reasons: they are model free, they directly optimize the performance metric of …

Reward-free reinforcement learning (RL) considers the setting where the agent does not
have access to a reward function during exploration, but must propose a near-optimal policy …

We design a new provably efficient algorithm for episodic reinforcement learning with
generalized linear function approximation. We analyze the algorithm under a new …

In this work, we develop provably efficient reinforcement learning algorithms for two-player
zero-sum Markov games with simultaneous moves. We consider a family of Markov games …