Position: Profiling Game Worlds by Transition Complexity
A new framework for quantifying environmental difficulty in reinforcement learning and game world modeling.
Game world modeling (GWM) and reinforcement learning (RL) are often confounded because research papers rarely quantify how difficult the underlying transition prediction problem is at the declared interface (pixels/tokens/latents with finite history). We propose the Transition Co...
ArXiv CS.AI
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The Transition Complexity Profile provides a much-needed standardized metric to quantify the difficulty of reinforcement learning environments, moving the field beyond subjective benchmarks. By formalizing transition uncertainty and dependency spans, researchers can finally compare the true efficacy of game world models across disparate digital domains.
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