Clustering-based Failed goal Aware Hindsight Experience Replay

In a multi-goal reinforcement learning environment, an agent learns a policy to perform tasks with multiple goals from experiences gained through exploration. In environments with sparse binary rewards, the replay buffer contains few successful experiences, posing a challenge for sampling efficiency. To address this, Hindsight Experience Replay (HER) generates successful experiences, named hindsight experiences, from unsuccessful ones. However, uniform sampling of experiences for the process of HER can lead to inefficient scenarios of generating hindsight experience. In this paper, a novel method called Failed goal Aware HER (FAHER) is proposed to enhance sampling efficiency. This method considers the properties of achieved goals with respect to failed goals during sampling. To account for these properties, a cluster model is used to cluster episodes in the replay buffer, and experiences are subsequently sampled in the manner of HER. The proposed method is validated through experiments on three robotic control tasks from the OpenAI Gym. The experimental results demonstrate that the proposed method is more sample-efficient and achieves improved performance over baseline approaches.