Adaptive Scaling of Policy Constraints for Offline Reinforcement Learning
This addresses the time-consuming and impractical need for per-dataset tuning in offline RL, making it more accessible for real-world applications.
The paper tackles the problem of hyperparameter tuning in offline reinforcement learning by proposing Adaptive Scaling of Policy Constraints (ASPC), which dynamically balances RL and behavior cloning, and it outperforms state-of-the-art methods on 39 datasets across four D4RL domains with a single configuration.
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.