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新强化学习方法利用策略Hessian加速收敛

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的二阶Actor-Critic方法，用于折扣马尔可夫决策过程中的强化学习。该方法旨在通过整合策略Hessian的曲率信息来加速收敛，克服了二阶优化在强化学习中通常伴随的计算复杂性。该方法在一个双时间尺度框架内利用Hessian-向量乘积计算，将Critic视为准静态的，以便在Actor更新期间提高效率和稳定性。 AI

影响 引入了一种更有效、更稳定的强化学习二阶优化技术，有可能加速复杂决策任务的收敛。

排序理由 该集群包含一篇详细介绍强化学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新强化学习方法利用策略Hessian加速收敛

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该集群包含一篇详细介绍强化学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Shuban V ·

    基于策略Hessian分解的折扣MDP二阶Actor-Critic方法

    We address the discounted reward setting in reinforcement learning (RL). To mitigate the value approximation challenges in policy gradient methods, actor-critic approaches have been developed and are known to converge to stationary points under suitable assumptions. However, thes…

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