Reinforcement Learning as (Discrete) Potential Theory
This paper explores the potential-theoretic viewpoint for core reinforcement learning representations and algorithms under a fixed-policy assumption and may offer a path for improved sample efficiency and formal constraints that can be applied to RL.
Christopher Connolly
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