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Position: Profiling Game Worlds by Transition Complexity
Not specified
game world modelingreinforcement learningtransition complexitybenchmarking
2608.18079
Builder Relevance
1h ago70%
Abstract
The paper proposes the Transition Complexity Profile (TCP) as a set of metrics to quantify the difficulty of transition prediction in game world modeling and reinforcement learning.
Reality Card
Core Claim
The authors introduce the Transition Complexity Profile (TCP) as a reproducible metric set that characterizes the transition kernel of game environments.
Method / Result
TCP includes metrics for intrinsic one-step branching, interaction-induced uncertainty, and temporal/spatial dependency span.
Limitations
The paper does not specify the authors, which may limit reproducibility and credibility.
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