aiqbench analysis
This paper establishes foundational criteria for mapping reinforcement learning agents between different deterministic and stochastic frameworks while preserving relative intelligence. The authors demonstrate that such mappings are fundamentally impossible in certain cases, highlighting intrinsic theoretical differences among RL formalizations rather than proposing a new product or policy shift. These findings offer critical insights into the structural limits of transferring learned policies across varying algorithmic environments.
Representation and Invariance in Reinforcement Learning
arXiv: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.
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.
Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
arXiv:2112.07752 [cs.AI]
(or arXiv:2112.07752v4 [cs.AI] for this version)
Full text under the publisher's open licence · 117 words
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