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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.06881 |
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Table of Contents:
- We develop a continuous-time entropy-regularized reinforcement learning framework under model uncertainty. By applying Sion's minimax theorem, we transform the intractable robust control problem into an equivalent standard entropy-regularized stochastic control problem, facilitating reinforcement learning algorithms. We establish sufficient conditions for the theorem's validity and demonstrate our approach on linear-quadratic problems with uncertain model parameters following Bernoulli and uniform distributions.