Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

Deep reinforcement learning (DRL) augments the reinforcement learning framework, which
learns a sequence of actions that maximizes the expected reward, with the representative …

This open-source book represents our attempt to make deep learning approachable,
teaching readers the concepts, the context, and the code. The entire book is drafted in …

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

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

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 …

Reinforcement learning (RL) with linear function approximation has received increasing
attention recently. However, existing work has focused on obtaining $\sqrt {T} $-type regret …

Modern tasks in reinforcement learning have large state and action spaces. To deal with
them efficiently, one often uses predefined feature mapping to represent states and actions …

Recent studies have shown that episodic reinforcement learning (RL) is not more difficult
than bandits, even with a long planning horizon and unknown state transitions. However …

We study reinforcement learning with linear function approximation where the transition
probability and reward functions are linear with respect to a feature mapping $\boldsymbol …