用于机器人任务的Hindsight Experience Replay中基于聚类的采样（学生摘要）
Cluster-based Sampling in Hindsight Experience Replay for Robotic Tasks (Student Abstract)
AI总结：
针对稀疏奖励下多目标强化学习训练困难的问题，提出一种基于聚类的HER采样策略，通过聚类模型对回合分组采样以提升样本效率，在OpenAI Gym机器人任务中表现优于基线方法。
摘要：
在具有稀疏二元奖励的多目标强化学习中，由于缺乏成功经验，训练智能体尤其具有挑战性。为解决此问题，hindsight experience replay (HER)（后见经验回放）即使从失败经验中也能生成成功经验。然而，从均匀采样的经验中生成成功经验的过程并不高效。本文研究了在生成成功经验时利用已达成目标特性的影响，并提出了一种新颖的基于聚类的采样策略。该策略利用聚类模型将具有不同已达成目标的回合进行分组，并以HER的方式采样经验来创建训练批次。通过在OpenAI Gym的三个机器人控制任务上的实验，验证了所提方法。实验结果表明，该方法具有显著的样本效率，并取得了优于基线方法的性能。
英文摘要：
In multi-goal reinforcement learning with a sparse binary reward, training agents is particularly challenging, due to a lack of successful experiences. To solve this problem, hindsight experience replay (HER) generates successful experiences even from unsuccessful ones. However, generating successful experiences from uniformly sampled ones is not an efficient process. In this paper, the impact of exploiting the property of achieved goals in generating successful experiences is investigated and a novel cluster-based sampling strategy is proposed. The proposed sampling strategy groups episodes with different achieved goals by using a cluster model and samples experiences in the manner of HER to create the training batch. The proposed method is validated by experiments with three robotic control tasks of the OpenAI Gym. The results of experiments demonstrate that the proposed method is substantially sample efficient and achieves better performance than baseline approaches.
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。
