12 citations · 51 across the 14 of their papers we have counts for
14 papers
Layered State Discovery for Incremental Autonomous Exploration
Liyu Chen, Andrea Tirinzoni, Alessandro Lazaric +1
We study the autonomous exploration (AX) problem proposed by Lim & Auer (2012). In this setting, the objective is to discover a set of -optimal policies reaching a set $\mathcal…
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…
Improved No-Regret Algorithms for Stochastic Shortest Path with Linear MDP
Liyu Chen, Rahul Jain, Haipeng Luo
We introduce two new no-regret algorithms for the stochastic shortest path (SSP) problem with a linear MDP that significantly improve over the only existing results of (Vial et al.…
Online Learning for Stochastic Shortest Path Model via Posterior Sampling
Mehdi Jafarnia-Jahromi, Liyu Chen, Rahul Jain +1
We consider the problem of online reinforcement learning for the Stochastic Shortest Path (SSP) problem modeled as an unknown MDP with an absorbing state. We propose PSRL-SSP, a si…
Implicit Finite-Horizon Approximation and Efficient Optimal Algorithms for Stochastic Shortest Path
Liyu Chen, Mehdi Jafarnia-Jahromi, Rahul Jain +1
We introduce a generic template for developing regret minimization algorithms in the Stochastic Shortest Path (SSP) model, which achieves minimax optimal regret as long as certain…
Finding the Stochastic Shortest Path with Low Regret: The Adversarial Cost and Unknown Transition Case
Liyu Chen, Haipeng Luo
We make significant progress toward the stochastic shortest path problem with adversarial costs and unknown transition. Specifically, we develop algorithms that achieve $\widetilde…
Impossible Tuning Made Possible: A New Expert Algorithm and Its Applications
Liyu Chen, Haipeng Luo, Chen-Yu Wei
We resolve the long-standing "impossible tuning" issue for the classic expert problem and show that, it is in fact possible to achieve regret $O\left(\sqrt{(\ln d)\sum_t \ell_{t,i}…
Minimax Regret for Stochastic Shortest Path with Adversarial Costs and Known Transition
Liyu Chen, Haipeng Luo, Chen-Yu Wei
We study the stochastic shortest path problem with adversarial costs and known transition, and show that the minimax regret is and $\widetilde{O}…
Synthesized Policies for Transfer and Adaptation across Tasks and Environments
Hexiang Hu, Liyu Chen, Boqing Gong +1
The ability to transfer in reinforcement learning is key towards building an agent of general artificial intelligence. In this paper, we consider the problem of learning to simulta…