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This paper investigates how action information can be incorporated into recurrent neural network architectures for reinforcement learning, examining design choices and empirically evaluating them across illustrative domains.
This paper proposes an epistemic state graph representation and an order-gap termination criterion for recursive reasoning systems, addressing how to manage evolving reasoning states and when to stop iteration.