Adaptive Scaling of Policy Constraints for Offline Reinforcement Learning

2025-08-27

AI Summary

In offline reinforcement learning, policy constraint hyperparameters require laborious, task- and dataset-specific tuning, resulting in poor efficiency and generalization. To address this, we propose the Adaptive Strategy Policy Constraint (ASPC) framework, the first to introduce a second-order differentiable mechanism that dynamically balances behavior cloning and RL objectives, enabling real-time adaptation of constraint strength during training. We provide theoretical guarantees on convergence and performance improvement, eliminating the need for dataset-specific hyperparameter optimization. Evaluated across all four D4RL domains—comprising 39 benchmark datasets—ASPC achieves state-of-the-art performance using a single, fixed hyperparameter configuration, outperforming prior methods that require per-dataset tuning. It delivers substantial average performance gains while incurring negligible computational overhead.

Technology Category

• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Multiagent Systems: Adversarial Agents
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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. We propose Adaptive Scaling of Policy Constraints (ASPC), a second-order differentiable framework that dynamically balances RL and behavior cloning (BC) 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 while incurring only minimal computational overhead. The code will be released at https://github.com/Colin-Jing/ASPC.

Problem

• Adaptively scaling policy constraints for offline RL
• Eliminating per-dataset hyperparameter tuning requirements
• Dynamically balancing reinforcement learning and behavior cloning

Innovation

• Adaptive policy constraint scaling framework
• Dynamically balances RL and behavior cloning
• Second-order differentiable with minimal overhead