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From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems
In this work, from a theoretical lens, we aim to understand why large language model (LLM) empowered agents are able to solve decision-making problems in the physical world.
Sample-efficient Learning of Infinite-horizon Average-reward MDPs with General Function Approximation
Moreover, for AMDPs, we propose a novel complexity measure -- average-reward generalized eluder coefficient (AGEC) -- which captures the challenge of exploration in AMDPs with general function approximation.
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