সংরক্ষণ করুন:
| প্রধান লেখক: | |
|---|---|
| বিন্যাস: | Preprint |
| প্রকাশিত: |
2026
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | https://arxiv.org/abs/2602.08075 |
| ট্যাগগুলো: |
ট্যাগ যুক্ত করুন
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সূচিপত্রের সারণি:
- In this work, we propose, for the first time, a reinforcement learning framework specifically designed for zero-sum linear-quadratic stochastic differential games. This approach offers a generalized solution for scenarios in which accurate system parameters are difficult to obtain, thereby overcoming a key limitation of traditional iterative methods that rely on complete system information. In correspondence with the game-theoretic algebraic Riccati equations associated with the problem, we develop both semi-model-based and model-free reinforcement learning algorithms by combining an iterative solution scheme with dynamic programming principles. Notably, under appropriate rank conditions on data sampling, the convergence of the proposed algorithms is rigorously established through theoretical analysis. Finally, numerical simulations are conducted to verify the effectiveness and feasibility of the proposed method.