@inproceedings{
tian2024reinforcement,
title={Reinforcement Learning with Adaptive Regularization for Safe Control of Critical Systems},
author={Haozhe Tian and Homayoun Hamedmoghadam and Robert Noel Shorten and Pietro Ferraro},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=MRO2QhydPF}
}
Reinforcement Learning (RL) is a powerful method for controlling dynamic systems, but its learning mechanism can lead to unpredictable actions that undermine the safety of critical systems. Here, we propose RL with Adaptive Regularization (RL-AR), an algorithm that enables safe RL exploration by combining the RL policy with a policy regularizer that hard-codes the safety constraints. RL-AR performs policy combination via a "focus module," which determines the appropriate combination depending on the state—relying more on the safe policy regularizer for less-exploited states while allowing unbiased convergence for well-exploited states. In a series of critical control applications, we demonstrate that RL-AR not only ensures safety during training but also achieves a return competitive with the standards of model-free RL that disregards safety.
Reinforcement Learning with Adaptive Regularization for Safe Control of Critical Systems
Haozhe Tian Homayoun Hamedmoghadam Robert Noel Shorten Pietro Ferraro