Goal Exploration via Adaptive Skill Distribution for Goal-Conditioned Reinforcement Learning

arXiv:2404.12999v1 Announce Type: new
Abstract: Exploration efficiency poses a significant challenge in goal-conditioned reinforcement learning (GCRL) tasks, particularly those with long horizons and sparse rewards. A primary limitation to exploration efficiency is the agent's inability to leverage environmental structural patterns. In this study, we introduce a novel framework, GEASD, designed to capture these patterns through an adaptive skill distribution during the learning process. This distribution optimizes the local entropy of achieved goals within a contextual horizon, enhancing goal-spreading behaviors and …

abstract agent arxiv challenge cs.ai cs.lg distribution efficiency environmental exploration framework novel patterns reinforcement reinforcement learning study tasks type via