Projection Implicit Q-Learning with Support Constraint for Offline Reinforcement Learning

• URL: http://arxiv.org/abs/2501.08907v1
• Date: Wed, 15 Jan 2025 16:17:02 GMT
• Title: Projection Implicit Q-Learning with Support Constraint for Offline Reinforcement Learning
• Authors: Xinchen Han, Hossam Afifi, Michel Marot,
• Abstract summary: Implicit Q-Learning (IQL) algorithm employs expectile regression to achieve in-sample learning. We propose Proj-IQL, a projective IQL algorithm enhanced with the support constraint. Proj-IQL achieves state-of-the-art performance on D4RL benchmarks.
• Score: 1.8789068567093286
• License:
• Abstract: Offline Reinforcement Learning (RL) faces a critical challenge of extrapolation errors caused by out-of-distribution (OOD) actions. Implicit Q-Learning (IQL) algorithm employs expectile regression to achieve in-sample learning, effectively mitigating the risks associated with OOD actions. However, the fixed hyperparameter in policy evaluation and density-based policy improvement method limit its overall efficiency. In this paper, we propose Proj-IQL, a projective IQL algorithm enhanced with the support constraint. In the policy evaluation phase, Proj-IQL generalizes the one-step approach to a multi-step approach through vector projection, while maintaining in-sample learning and expectile regression framework. In the policy improvement phase, Proj-IQL introduces support constraint that is more aligned with the policy evaluation approach. Furthermore, we theoretically demonstrate that Proj-IQL guarantees monotonic policy improvement and enjoys a progressively more rigorous criterion for superior actions. Empirical results demonstrate the Proj-IQL achieves state-of-the-art performance on D4RL benchmarks, especially in challenging navigation domains.

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