Improving Offline Goal-Conditioned Reinforcement Learning via Selective Reward Stimulation
This work proposes Reward Stimulation Implicit Q-Learning (RSIQL), a simple non-hierarchical method that introduces additional reward signals at progress-making intermediate states in offline trajectories and achieves performance competitive with hierarchical offline goal-conditioned methods, while retaining a simple f...