Title: Adaptive Scaling of Policy Constraints for Offline Reinforcement Learning
Primary area: reinforcement learning
Keywords: Offline Reinforcement Learning, Policy Constraint, Behavioral cloning

Abstract: Offline reinforcement learning (RL) enables learning effective policies from fixed datasets without any environment interaction. Existing methods typically employ policy constraints to mitigate the distribution shift encountered during offline RL training. However, because the scale of the constraints varies across tasks and datasets of differing quality, existing methods must meticulously tune hyperparameters to match each dataset, which is time-consuming and often impractical. To bridge this gap, we propose Adaptive Scaling of Policy Constraints (ASPC), a second-order differentiable framework that automatically adjusts the scale of policy constraints during training. We theoretically analyze its performance improvement guarantee. In experiments on 39 datasets across four D4RL domains, ASPC using a single hyperparameter configuration outperforms other adaptive constraint methods and state-of-the-art offline RL algorithms that require per-dataset tuning, achieving an average 35\% improvement in normalized performance over the baseline while incurring only minimal computational overhead.

TL;DR: We dynamically adjust the policy constraint scale during the training process, enabling the algorithm to adapt to various datasets without manual tuning and surpass SOTA offline RL algorithms