Reinforcement Learning with Adaptive Control Regularization for Safe Control of Critical Systems

arXiv:2404.15199v1 Announce Type: new
Abstract: 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 Control Regularization (RL-ACR) that ensures RL safety by combining the RL policy with a control regularizer that hard-codes safety constraints over forecasted system behaviors. The adaptability is achieved by using a learnable "focus" weight trained to maximize the cumulative reward of the …

abstract arxiv control cs.lg dynamic regularization reinforcement reinforcement learning safe safety systems type undermine