First-Order Representations Advance Goal-Conditioned Reinforcement Learning

This ArXiv paper likely explores the application of first-order logic representations to enhance the performance and interpretability of goal-conditioned reinforcement learning (GCRL) algorithms. The focus is on how these representations can improve the efficiency and robustness of agents in achieving desired goals.