Entropy Regularization Improves Policy Robustness in Continuous-Time Reinforcement Learning
Entropy regularization is widely used in continuous-time reinforcement learning (RL) to reduce sensitivity to environmental perturbations, yet its robustness benefits lack a rigorous theoretical foundation. This paper establishes the first robustness guarantees for entropy-regularized continuous-time Markov decision processes. We show that maximizing an entropy-regularized objective yields a lower bound on a worst-case robust RL problem with joint reward and transition perturbations. We analytically characterize the induced robust sets and prove that they expand monotonically with the regularization strength, justifying the empirical observation that stronger entropy improves robustness. ...
| Authors | Jialun Cao, Fernando Acero, David Šiška et al. |
|---|---|
| Primary category | math.OC |
| Published | 3 July 2026 |
| Last updated at source | 3 July 2026 |
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