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Clustering-based Failed goal Aware Hindsight Experience Replay.

Taeyoung Kim1, Taemin Kang2, Haechan Jeong1

  • 1CCS Graduate School of Mobility, Korea Advanced Institute of Science & Technology, Daejeon, Republic of South Korea.

Peerj. Computer Science
|February 3, 2025
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Summary
This summary is machine-generated.

Failed goal Aware HER (FAHER) improves multi-goal reinforcement learning by intelligently sampling experiences. This novel method enhances sampling efficiency and agent performance in sparse reward environments.

Keywords:
Cluster modelHindsight experience replayMulti-goal reinforcement learningRobotics

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Robotics

Background:

  • Multi-goal reinforcement learning (RL) agents learn policies from exploration experiences.
  • Sparse rewards in RL environments challenge sampling efficiency due to limited successful experiences.
  • Hindsight Experience Replay (HER) generates 'hindsight' experiences from failures to improve learning.

Purpose of the Study:

  • To propose a novel method, Failed goal Aware HER (FAHER), to enhance sampling efficiency in multi-goal RL.
  • To address the inefficiency of uniform sampling in the Hindsight Experience Replay (HER) process.
  • To improve the performance of RL agents in environments with sparse rewards.

Main Methods:

  • Developed Failed goal Aware HER (FAHER) to prioritize sampling of relevant failed experiences.
  • Integrated a cluster model to group episodes based on goal properties within the replay buffer.
  • Applied FAHER to sample experiences for HER in multi-goal RL tasks.

Main Results:

  • FAHER demonstrated superior sample efficiency compared to baseline methods.
  • Experiments on robotic control tasks showed improved performance with FAHER.
  • The proposed method effectively addresses sampling inefficiencies in HER.

Conclusions:

  • FAHER significantly enhances sampling efficiency in multi-goal reinforcement learning.
  • The method improves agent performance, particularly in challenging sparse reward settings.
  • Considering failed goal properties during sampling is crucial for efficient HER.