提出多步近端策略改进，让离线强化学习更安全地突破数据分布限制。
Multi-step Proximal Policy Improvement in Offline Reinforcement Learning
- 将策略更新视为流形上的梯度流动，用几何视角统一多种离线算法
- 多步重中心更新使策略在保持稳定性前提下逐步提升性能
- 适用于多种策略形式，在D4RL上显著优化主流基线模型
离线强化学习需在保持值估计可靠性与实现有效策略改进之间取得平衡：更新应靠近数据支持的动作，但进步又常需超越行为分布。本文从几何角度建模策略为带度量的概率流形，发现一类广泛存在的离线策略目标可统一为单步近端策略改进（SPI），即由批评者定义能量诱导的流形梯度流的隐式离散化。基于此，我们提出多步近端策略改进（MPI）——一种可插拔的序列重中心优化机制。该方法在每一步中保持近端约束，同时允许策略逐步脱离原始数据分布进行可控提升。框架兼容多种策略几何结构，并给出了确定性与对角高斯策略的实际实现。在D4RL基准测试中，少量MPI迭代即可显著提升包括TD3+BC、ReBRAC和IQL在内的强基线模型表现。诊断分析进一步揭示了重中心优化与固定目标调度的区别，并刻画了批评者误差下的局限性。
原文摘要 · Abstract (English)
Offline reinforcement learning (RL) must reconcile two competing requirements: policy updates should stay near dataset-supported actions to keep value estimates reliable, yet meaningful gains often require moving beyond the behavior distribution. We develop a geometric view of offline actor updates by modeling policies as a probability manifold endowed with a chosen metric geometry. Under this lens, a broad class of offline actor objectives can be interpreted as a single proximal policy improvement step (SPI), i.e., an implicit discretization of a manifold gradient flow induced by a critic-defined energy. Building on this insight, we propose multi-step proximal policy improvement (MPI), a plug-in refinement mechanism that composes sequential re-centered proximal steps. MPI enables controlled policy improvement beyond dataset support while retaining proximal control at each refinement. The framework accommodates multiple policy geometries and admits practical instantiations for deterministic and diagonal-Gaussian policies. Experiments on D4RL benchmarks show that small numbers of MPI refinements improve strong offline baselines, including TD3+BC, ReBRAC, and IQL, on many tasks. Focused diagnostics further distinguish re-centered refinement from fixed-objective update scheduling and characterize limitations under critic error.
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