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Arxiv
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Viability of Future Actions: Robust Safety in Reinforcement Learning via Entropy Regularization
- Recent advances in reinforcement learning have not fully addressed robustly learning policies that adhere to state constraints under unknown disruptions.
- A new study explores achieving robust safety in reinforcement learning through a combination of entropy regularization and constraints penalization.
- Entropy regularization in constrained RL is found to bias learning towards maximizing future viable actions and enhancing constraints satisfaction in the presence of action noise.
- Relaxing strict safety constraints through penalties allows the constrained RL problem to be approximated closely by an unconstrained one, facilitating solution using standard model-free RL techniques.
- This reformulation preserves safety and optimality while enhancing resilience to disturbances in RL environments.
- The empirical findings suggest a promising correlation between entropy regularization and robustness in reinforcement learning, indicating a path for further theoretical and empirical exploration in achieving robust safety.
- The study emphasizes the significance of simple reward shaping techniques in enabling robust safety in RL scenarios.
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