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Research questionWhen a model-based agent performs well, how can we verify its visual world model supports reliable planning and transfer?A high return can conceal visual or dynamical errors in the model used to imagine future trajectories. Such errors may become apparent when a new policy learns entirely inside a frozen model and is then evaluated in the real environment.
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Reinforcement Learning
Research Paper
Latest papersRecent research connected to this question, newest first.Improving Weak World Models Behind Strong Agents in Atari PongThe evidence concerns visual world models for Atari Pong, using reproduced DreamerV3, DIAMOND, TWISTER, Simulus, and STORM agents. It independently examines frozen-model rollouts and native zero-shot model-based reinforcement learning, including pixel-space policy learning, with broader observations across Atari100K; the findings are specific to these models and benchmarks.research paper · Sep 4, 2026
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