We study time-inhomogeneous episodic reinforcement learning (RL) under general function
approximation and sparse rewards. We design a new algorithm, Variance-weighted …

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 …

Abstract Multi-distribution learning (MDL), which seeks to learn a shared model that
minimizes the worst-case risk across $ k $ distinct data distributions, has emerged as a …

A central issue lying at the heart of online reinforcement learning (RL) is data efficiency.
While a number of recent works achieved asymptotically minimal regret in online RL, the …

Recently, several studies\citep {zhou2021nearly, zhang2021variance, kim2021improved,
zhou2022computationally} have provided variance-dependent regret bounds for linear …

We study sample efficient reinforcement learning (RL) under the general framework of
interactive decision making, which includes Markov decision process (MDP), partially …

We study variance-dependent regret bounds for Markov decision processes (MDPs).
Algorithms with variance-dependent regret guarantees can automatically exploit …

We study reward-free reinforcement learning (RL) with linear function approximation, where
the agent works in two phases:(1) in the exploration phase, the agent interacts with the …

The key assumption underlying linear Markov Decision Processes (MDPs) is that the learner
has access to a known feature map φ (x, a) that maps state-action pairs to d-dimensional …

Imitation learning (IL) aims to mimic the behavior of an expert in a sequential decision
making task by learning from demonstrations, and has been widely applied to robotics …