ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Offline reinforcement learning (RL) uses static datasets without environment interaction.
• Existing methods often create overly conservative policies due to Q-value overestimation.
• Current approaches lack fine-grained control over policy conservatism.

Purpose of the Study:

• To address limitations in offline RL, specifically over-conservatism and lack of nuanced control.
• To propose a framework for adaptive conservatism in Q-learning.
• To enhance policy performance in offline RL settings.

Main Methods:

• Introduced Adaptive Conservative Level in Q-learning (ACL-QL) framework.
• Developed two learnable adaptive weight functions for transition-specific conservatism.
• Designed monotonicity and surrogate losses for training Q-function, policy network, and weight functions.
• Theoretically analyzed conditions for mild Q-value ranges and adaptive optimization.

Main Results:

• ACL-QL effectively limits Q-values within a mild range.
• Adaptive control over conservatism improves policy performance.
• Demonstrated state-of-the-art performance on D4RL benchmark datasets.
• Ablation studies confirmed the effectiveness of the proposed method.

Conclusions:

• ACL-QL offers a novel approach to mitigate over-conservatism in offline RL.
• The adaptive weighting mechanism allows for fine-grained control over policy conservatism.
• ACL-QL achieves superior performance compared to existing offline RL baselines.