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arXiv AI By Hongqiang Lin, Dongxu Zhang, Yiding Sun, Mingzhe Li, Ning Yang, Haijun Zhang

Offline Policy Optimization with Posterior Sampling

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The paper introduces PSPO, a method for offline policy optimization that treats the dynamics model as a random variable, enabling controlled exploration of out‑of‑distribution (OOD) regions while maintaining robustness. By alternating updates of the posterior distribution and the policy, PSPO achieves a regularized optimization algorithm with convergence guarantees. Experiments on standard benchmarks show that PSPO outperforms state‑of‑the‑art baselines and retains a pessimism‑free property without sacrificing robustness.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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