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arXiv Machine Learning By Junwei Quan, Evgenii Opryshko, Nicholas Rhinehart, Igor Gilitschenski

World-Model Policy Arbiter for Goal-Conditioned Reinforcement Learning

Read the original on arXiv Machine Learning →

The paper introduces the World-Model Policy Arbiter (WMPA), a test‑time framework that selects among a portfolio of frozen goal‑conditioned policies by simulating each policy’s future trajectories in a learned world model and evaluating them with a shared value function. WMPA avoids frequent policy switching by committing to the chosen policy for a short interval and does not require retraining or task‑specific knowledge. On the OGBench benchmark, WMPA raises the macro‑average success rate from 44% to 58%, achieving significant improvements on 12 of 18 datasets, including large gains on cube‑double‑play and scene‑play.

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