Auxiliary Midpoint Learning for Goal-Reaching Reinforcement Learning

We propose *Auxiliary Midpoint Learning* (AML), a technique for quasi-metric learning in goal-reaching reinforcement learning (GRRL). In addition to standard temporal-difference (TD) learning of the policy and quasi-metric networks, AML jointly trains a network that predicts midpoints with respect to the learned quasi-metric and uses these predictions to improve the global consistency of the quasi-metric network. This enables more effective learning on goal-reaching tasks with long horizons. Experiments show that AML consistently improves over the quasi-metric TD baseline and achieves the highest success rate among the compared methods on several benchmark environments.