arXiv:2603.23461v2 Announce Type: replace Abstract: We study reinforcement learning (RL) with linear function approximation in Markov Decision Processes (MDPs) satisfying \emph{linear Bellman completeness} -- a fundamental setting where the Bellman backup of any linear value function remains linear. While statistically tractable, prior computationally efficient algorithms are either limited to small action spaces or require strong oracle assumptions over the feature space. We provide a computationally efficient algorithm for linear Bellman complete MDPs with \emph{deterministic transitions}, s
This paper represents continued progress in the fundamental understanding and computational efficiency of reinforcement learning, a core component of advanced AI systems.
Improved algorithmic efficiency in RL can accelerate the development of more capable and resource-effective AI agents, impacting various applications from robotics to autonomous decision-making.
The research advances the theoretical and practical feasibility of efficient reinforcement learning within specific complex environments, potentially broadening the scope of solvable problems for AI.
- · AI researchers
- · Developers of AI agents
- · Robotics sector
- · High-autonomy system developers
- · Computational resource providers (less need for brute force in some areas)
- · Companies relying on less efficient RL methods
More computationally efficient AI models for complex tasks become feasible.
Faster iteration cycles for AI development and deployment, particularly in domains requiring deep RL.
Enhanced AI agent capabilities could lead to more sophisticated autonomous systems capable of tackling previously intractable real-world problems.
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Read at arXiv cs.LG