12 citations · 51 across the 14 of their papers we have counts for
6 papers · 1 filter
Reaching Goals is Hard: Settling the Sample Complexity of the Stochastic Shortest Path
Liyu Chen, Andrea Tirinzoni, Matteo Pirotta +1
We study the sample complexity of learning an -optimal policy in the Stochastic Shortest Path (SSP) problem. We first derive sample complexity bounds when the learner has access…
Near-Optimal Goal-Oriented Reinforcement Learning in Non-Stationary Environments
Liyu Chen, Haipeng Luo
We initiate the study of dynamic regret minimization for goal-oriented reinforcement learning modeled by a non-stationary stochastic shortest path problem with changing cost and tr…
Follow-the-Perturbed-Leader for Adversarial Markov Decision Processes with Bandit Feedback
Yan Dai, Haipeng Luo, Liyu Chen
We consider regret minimization for Adversarial Markov Decision Processes (AMDPs), where the loss functions are changing over time and adversarially chosen, and the learner only ob…
Policy Learning and Evaluation with Randomized Quasi-Monte Carlo
Sebastien M. R. Arnold, Pierre L'Ecuyer, Liyu Chen +2
Reinforcement learning constantly deals with hard integrals, for example when computing expectations in policy evaluation and policy iteration. These integrals are rarely analytica…
Policy Optimization for Stochastic Shortest Path
Liyu Chen, Haipeng Luo, Aviv Rosenberg
Policy optimization is among the most popular and successful reinforcement learning algorithms, and there is increasing interest in understanding its theoretical guarantees. In thi…
Learning Infinite-Horizon Average-Reward Markov Decision Processes with Constraints
Liyu Chen, Rahul Jain, Haipeng Luo
We study regret minimization for infinite-horizon average-reward Markov Decision Processes (MDPs) under cost constraints. We start by designing a policy optimization algorithm with…