Representation and Invariance in Reinforcement Learning

תקציר מקורי באנגליתarXiv:2112.07752v4 Announce Type: replace-cross Abstract: Researchers have formalized reinforcement learning (RL) in different ways. If an agent in one RL framework is to run within another RL framework's environments, the agent must first be converted, or mapped, into that other framework. In this paper, we lay foundations for studying relative-intelligence-preserving mappability between RL frameworks. We introduce a criterion which is sufficient for relative intelligence to be preserved according to one particular method of measuring intelligence. We show that this criterion cannot be met when mapping between certain deterministic and stochastic RL frameworks, suggesting inherent fundamental diffences between these different versions of RL.