9 citations · 22 across the 7 of their papers we have counts for
7 papers
Dynamics Models for Offline Hyperparameter Selection in Real-World RL
Jordan Coblin, Han Wang, Martha White +1
A key obstacle to deploying reinforcement learning in real-world systems is hyperparameter selection, particularly when simulators are unavailable and online experimentation is cos…
Measuring and Mitigating Interference in Reinforcement Learning
Vincent Liu, Han Wang, Ruo Yu Tao +3
Catastrophic interference is common in many network-based learning systems, and many proposals exist for mitigating it. Before overcoming interference we must understand it better.…
The In-Sample Softmax for Offline Reinforcement Learning
Chenjun Xiao, Han Wang, Yangchen Pan +2
Reinforcement learning (RL) agents can leverage batches of previously collected data to extract a reasonable control policy. An emerging issue in this offline RL setting, however,…
Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneity
Han Wang, Aritra Mitra, Hamed Hassani +2
We initiate the study of federated reinforcement learning under environmental heterogeneity by considering a policy evaluation problem. Our setup involves agents interacting wi…
No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL
Han Wang, Archit Sakhadeo, Adam White +7
The performance of reinforcement learning (RL) agents is sensitive to the choice of hyperparameters. In real-world settings like robotics or industrial control systems, however, te…
DouFu: A Double Fusion Joint Learning Method For Driving Trajectory Representation
Han Wang, Zhou Huang, Xiao Zhou +3
Driving trajectory representation learning is of great significance for various location-based services, such as driving pattern mining and route recommendation. However, previous…
Investigating the Properties of Neural Network Representations in Reinforcement Learning
Han Wang, Erfan Miahi, Martha White +5
In this paper we investigate the properties of representations learned by deep reinforcement learning systems. Much of the early work on representations for reinforcement learning…