Position: Profiling Game Worlds by Transition Complexity

The article introduces the Transition Complexity Profile (TCP), a set of reproducible metrics designed to quantify the difficulty of transition prediction in game world modeling and reinforcement learning. TCP evaluates environments based on one-step branching, interaction-induced uncertainty, and temporal/spatial dependency span, and it includes reference distributions, protocol stochasticity, and a versioned measurement budget for comparability. The authors argue that TCP should become standard benchmark metadata and a required statistic in GWM and RL research.

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• reinforcement-learning
• benchmarks