arXiv Artificial Intelligence

Do Better Goal Representations Improve Goal-Conditioned Reinforcement Learning?

1 hour ago 01.10.2026 07:00 👁 1 views
Do Better Goal Representations Improve Goal-Conditioned Reinforcement Learning?

Quick summary

arXiv:2609.39901v1 Announce Type: cross Abstract: Goal-conditioned reinforcement learning (GCRL) relies heavily on how target goals are represented to the policy. While recent methods encode goals via temporal distance, occupancy, or controllability, it remains unclear how much downstream performance actually depends on representation quality. We study this in offline GCRL by constructing an exact temporal-distance goal representation in deterministic mazes. We then systematically corrupt its geometric quality while keeping the downstream learner fixed. Across OGBench navigation tasks and two

Key takeaways

  • arXiv:2609.39901v1 Announce Type: cross Abstract: Goal-conditioned reinforcement learning (GCRL) relies heavily on how target goals are represented to the policy.
  • While recent methods encode goals via temporal distance, occupancy, or controllability, it remains unclear how much downstream performance actually depends on representation quality.
  • We study this in offline GCRL by constructing an exact temporal-distance goal representation in deterministic mazes.

Why it matters

“Do Better Goal Representations Improve Goal-Conditioned Reinforcement Learning?” may affect what data AI products can use and where accountability sits. Product teams should watch compliance duties, rights holders should watch enforcement, and users should watch transparency and appeal mechanisms.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗