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A Reinforcement Learning Approach for Dynamic Rebalancing in Bike-Sharing System
Our work offers practical insights for operators and enriches the integration of reinforcement learning into dynamic rebalancing problems, paving the way for more intelligent and robust urban mobility solutions.
Wasserstein Diversity-Enriched Regularizer for Hierarchical Reinforcement Learning
Hierarchical reinforcement learning composites subpolicies in different hierarchies to accomplish complex tasks. Automated subpolicies discovery, which does not depend on domain knowledge, is a promising approach to generating subpolicies. However, the degradation problem is a challenge that existing methods can hardly deal with due to the lack of consideration of diversity or the employment of weak regularizers.
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