Task-Agnostic Safety for Reinforcement Learning

Reinforcement learning (RL) has been an attractive potential for designing autonomous systems due to its learning-by-exploration approach. However, this learning process makes RL inherently vulnerable and thus unsuitable for applications where safety is a top priority. To address this issue, researchers have either jointly optimized task and safety or imposed constraints to restrict exploration. This paper takes a different approach, by utilizing exploration as an adaptive means to learn a robust and safe behavior. To this end, we propose Task-Agnostic Safety for Reinforcement Learning (TAS-RL) framework to ensure safety in RL by learning a representation of unsafe behaviors and excluding them from the agent's policy. TAS-RL is task-agnostic and can be integrated with any RL task policy in the same environment, providing a self-protection layer for the system. To evaluate the robustness of TAS-RL, we present a novel study where TAS-RL and 7 safe RL baselines are tested in constrained Markov decision processes (CMDP) environments under white-box action space perturbations and changes in the environment dynamics. The results show that TAS-RL outperforms all baselines by achieving consistent near-zero safety constraint violations in continuous action space with 10 times more variations in the testing environment dynamics.