arXiv:2602.23242v3 Announce Type: replace 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 asym
The continuous advancements in AI research, particularly in reinforcement learning, are leading to novel theoretical breakthroughs that challenge long-held assumptions about optimal agent design.
This development represents a significant theoretical step towards more efficient and less resource-intensive general AI, potentially accelerating the development of highly capable autonomous systems.
The conventional wisdom that optimal general reinforcement learning agents must be model-based may be overturned, opening new avenues for AI architecture design and implementation.
- · AI research labs
- · Developers of AI agents
- · Industries relying on autonomous systems
- · Companies heavily invested in model-based AI architectures only
The theoretical foundation for model-free general AI is strengthened, potentially simplifying the development of future advanced AI systems.
Reduced computational overhead and data requirements for general AI agents could make them more accessible and deployable in diverse real-world scenarios.
A pathway to genuinely ubiquitous and autonomous AI agents could emerge, leading to profound economic and societal restructuring as new capabilities become commonplace.
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Read at arXiv cs.AI