Shen, Y., & Huang, H. (2024). Hypercube Policy Regularization Framework for Offline Reinforcement Learning. Neural Networks. arXiv:2411.04534v1 [cs.LG].
This paper introduces a novel hypercube policy regularization framework to address the limitations of existing policy regularization methods in offline reinforcement learning, particularly in handling low-quality datasets.
The authors propose dividing the state space into hypercubes and allowing the agent to explore actions associated with states within the same hypercube. This approach enables the agent to learn from a broader range of actions while maintaining constraints on out-of-distribution state actions. The framework is integrated with two baseline algorithms, TD3-BC and Diffusion-QL, resulting in TD3-BC-C and Diffusion-QL-C algorithms. The performance of these algorithms is evaluated on the D4RL benchmark dataset, including Gym, AntMaze, and Adroit environments.
The hypercube policy regularization framework effectively enhances the performance of policy regularization methods in offline reinforcement learning by enabling limited exploration within a constrained space. This approach offers a promising direction for improving the efficiency and effectiveness of offline RL algorithms.
This research contributes a novel and practical framework for enhancing policy learning in offline reinforcement learning, addressing the challenges posed by limited and potentially low-quality datasets.
The authors suggest exploring the optimal utilization of static datasets and further investigating the impact of hyperparameter settings on the framework's performance in future research.
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by Yi Shen, Han... ที่ arxiv.org 11-08-2024
https://arxiv.org/pdf/2411.04534.pdfสอบถามเพิ่มเติม