Paper
A Model-Free Universal AI
Authors
Yegon Kim, Juho Lee
Abstract
In general reinforcement learning, all established optimal agents, including AIXI, are model-based, explicitly maintaining and using environment models. This paper introduces Universal AI with Q-Induction (AIQI), the first model-free agent proven to be asymptotically $\varepsilon$-optimal in general RL. AIQI performs universal induction over distributional action-value functions, instead of policies or environments like previous works. Under a grain of truth condition, we prove that AIQI is strong asymptotically $\varepsilon$-optimal and asymptotically $\varepsilon$-Bayes-optimal. Our results significantly expand the diversity of known universal agents.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.23242v1</id>\n <title>A Model-Free Universal AI</title>\n <updated>2026-02-26T17:21:16Z</updated>\n <link href='https://arxiv.org/abs/2602.23242v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.23242v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>In general reinforcement learning, all established optimal agents, including AIXI, are model-based, explicitly maintaining and using environment models. This paper introduces Universal AI with Q-Induction (AIQI), the first model-free agent proven to be asymptotically $\\varepsilon$-optimal in general RL. AIQI performs universal induction over distributional action-value functions, instead of policies or environments like previous works. Under a grain of truth condition, we prove that AIQI is strong asymptotically $\\varepsilon$-optimal and asymptotically $\\varepsilon$-Bayes-optimal. Our results significantly expand the diversity of known universal agents.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n <published>2026-02-26T17:21:16Z</published>\n <arxiv:primary_category term='cs.AI'/>\n <author>\n <name>Yegon Kim</name>\n </author>\n <author>\n <name>Juho Lee</name>\n </author>\n </entry>"
}