MSPR: Multi-scale Predictive Representations for Goal-conditioned Reinforcement Learning

Excerpt

arXiv:2605.09364v2 Announce Type: replace Abstract: This paper investigates robust representation learning in offline goal-conditioned reinforcement learning (GCRL). Particularly in sparse reward scenarios, learning representations that align state and goal latents is a challenge, as the encoder can learn goal-agnostic features that destabilize policy learning. We address this issue by learning the encoder's representation with alignment objectives that capture the environment across multiple sc