Viability of Future Actions: Robust Safety in Reinforcement Learning via Entropy Regularization
This work addresses robust safety in reinforcement learning, which is crucial for real-world applications, but it is incremental as it builds on existing techniques like entropy regularization and constraint penalization.
The paper tackled the problem of learning robustly safe policies in reinforcement learning under unknown disturbances by connecting entropy regularization with constraint penalization, showing that this approach improves resilience to disturbances while preserving safety and optimality.
Despite the many recent advances in reinforcement learning (RL), the question of learning policies that robustly satisfy state constraints under unknown disturbances remains open. In this paper, we offer a new perspective on achieving robust safety by analyzing the interplay between two well-established techniques in model-free RL: entropy regularization, and constraints penalization. We reveal empirically that entropy regularization in constrained RL inherently biases learning toward maximizing the number of future viable actions, thereby promoting constraints satisfaction robust to action noise. Furthermore, we show that by relaxing strict safety constraints through penalties, the constrained RL problem can be approximated arbitrarily closely by an unconstrained one and thus solved using standard model-free RL. This reformulation preserves both safety and optimality while empirically improving resilience to disturbances. Our results indicate that the connection between entropy regularization and robustness is a promising avenue for further empirical and theoretical investigation, as it enables robust safety in RL through simple reward shaping.