17 citations · 71 across the 33 of their papers we have counts for
6 papers · 1 filter
Robust Offline Reinforcement learning with Heavy-Tailed Rewards
Jin Zhu, Runzhe Wan, Zhengling Qi +2
This paper endeavors to augment the robustness of offline reinforcement learning (RL) in scenarios laden with heavy-tailed rewards, a prevalent circumstance in real-world applicati…
Future-Dependent Value-Based Off-Policy Evaluation in POMDPs
Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett +5
We study off-policy evaluation (OPE) for partially observable MDPs (POMDPs) with general function approximation. Existing methods such as sequential importance sampling estimators…
A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes
Chengchun Shi, Masatoshi Uehara, Jiawei Huang +1
We consider off-policy evaluation (OPE) in Partially Observable Markov Decision Processes (POMDPs), where the evaluation policy depends only on observable variables and the behavio…
Pattern Transfer Learning for Reinforcement Learning in Order Dispatching
Runzhe Wan, Sheng Zhang, Chengchun Shi +2
Order dispatch is one of the central problems to ride-sharing platforms. Recently, value-based reinforcement learning algorithms have shown promising performance on this problem. H…
Robust Reinforcement Learning via Adversarial training with Langevin Dynamics
Parameswaran Kamalaruban, Yu-Ting Huang, Ya-Ping Hsieh +3
We introduce a sampling perspective to tackle the challenging task of training robust Reinforcement Learning (RL) agents. Leveraging the powerful Stochastic Gradient Langevin Dynam…
Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework
Chengchun Shi, Xiaoyu Wang, Shikai Luo +3
A/B testing, or online experiment is a standard business strategy to compare a new product with an old one in pharmaceutical, technological, and traditional industries. Major chall…