  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 facilitating deep exploration in states
containing familiar structural patterns. Our experiments reveal marked
improvements in exploration efficiency using the adaptive skill distribution
compared to a uniform skill distribution. Additionally, the learned skill
distribution demonstrates robust generalization capabilities, achieving
substantial exploration progress in unseen tasks containing similar local
structures.
