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This paper introduces a white-box instrument using hidden deterministic finite automata to separately measure reward success and latent-state learning in reinforcement learning agents, finding that high reward does not imply task understanding.
An introductory literate Haskell document exploring the relationship between coalgebras and automata, demonstrating how state machines can be modeled using fold and unfold operations within category theory.