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Inferring Probabilistic Reward Machines from Non-Markovian Reward Processes for Reinforcement Learning
The success of reinforcement learning in typical settings is predicated on Markovian assumptions on the reward signal by which an agent learns optimal policies.
Functional Decision Theory in an Evolutionary Environment
Functional decision theory (FDT) is a fairly new mode of decision theory and a normative viewpoint on how an agent should maximize expected utility.
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