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.

翻译：在基于稀疏二值奖励的多目标强化学习中，由于缺乏成功经验，训练智能体颇具挑战性。为解决此问题，后见经验回放（HER）能从未成功经验中生成成功经验。然而，从均匀采样经验中生成成功经验并非高效过程。本文研究了利用已达成目标属性生成成功经验的影响，并提出了一种新颖的基于聚类的抽样策略。该策略通过聚类模型将具有不同已达成目标的回合分组，并采用HER方式抽取经验以构建训练批次。通过OpenAI Gym的三个机器人控制任务实验验证了该方法，结果表明所提方法在样本效率上显著提升，且性能优于基线方法。