后见经验回放加速近端策略优化
Hindsight Experience Replay Accelerates Proximal Policy Optimization
AI总结：
针对HER因违反同策略假设难用于同策略算法的问题，研究验证了后见经验回放（HER）可在定制捕食者-猎物环境中显著加速同策略强化学习算法近端策略优化（PPO）。
摘要：
后见经验回放（Hindsight experience replay, HER）通过事后将回合目标修改为回合内达到的某个状态，来加速稀疏奖励环境下的离策略强化学习算法。由于观测目标的事后修改违反了同策略算法的假设，HER通常不应用于同策略算法。本文表明，在定制的捕食者-猎物环境上测试时，HER可显著加速同策略强化学习算法近端策略优化（PPO）。
英文摘要：
Hindsight experience replay (HER) accelerates off-policy reinforcement learning algorithms for environments that emit sparse rewards by modifying the goal of the episode post-hoc to be some state achieved during the episode. Because post-hoc modification of the observed goal violates the assumptions of on-policy algorithms, HER is not typically applied to on-policy algorithms. Here, we show that HER can dramatically accelerate proximal policy optimization (PPO), an on-policy reinforcement learning algorithm, when tested on a custom predator-prey environment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
