English(EN) Towards Understanding the Impact of Plasticity Loss on Reinforcement Learning in Stochastic Environments

塑性损失影响了噪声环境中的强化学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员调查了塑性损失对在随机环境中运行的强化学习代理的影响。他们的研究在三个添加了噪声的DeepMind控制任务上进行，结果显示，旨在保持塑性的方法增强了学习的鲁棒性，尤其是在观察噪声增加的情况下。研究结果还表明，随机性会加速更具挑战性环境中的塑性损失，而更容易的环境可能会将噪声视为一种正则化器。塑性损失在训练的早期阶段最为明显，在最困难的随机环境中，TD不稳定性成为主要的失败模式。 AI

影响理解强化学习代理中的塑性损失可能有助于在复杂、现实世界的场景中构建更鲁棒、更可靠的AI系统。

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arXiv cs.LG TIER_1 English(EN) · Philipp Bordne, Andr\'e Biedenkapp · 2026-11-02 05:00

Towards Understanding the Impact of Plasticity Loss on Reinforcement Learning in Stochastic Environments

arXiv:2610.42100v1 Announce Type: new Abstract: Deep reinforcement learning (RL) is a powerful framework for sequential decision-making. Despite many high-profile successes in recent years, deep RL is known to be brittle. An emerging line of research attributes this brittleness t…

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Towards Understanding the Impact of Plasticity Loss on Reinforcement Learning in Stochastic Environments

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