A General Control-Theoretic Approach for Reinforcement Learning: Theory and Algorithms
<|reference_start|>A General Control-Theoretic Approach for Reinforcement Learning: Theory and Algorithms: We devise a control-theoretic reinforcement learning approach to support direct learning of the optimal policy. We establish various theoretical properties of our approach, such as convergence and optimality of our control-theoretic operator, a new control-policy-parameter gradient ascent theorem, and a specific gradient ascent algorithm based on this theorem. As a representative example, we adapt our approach to a particular control-theoretic framework and empirically evaluate its performance on several classical reinforcement learning tasks, demonstrating significant improvements in solution quality, sample complexity, and running time of our control-theoretic approach over state-of-the-art baseline methods.<|reference_end|>
@article{chen2024a,
    title={A General Control-Theoretic Approach for Reinforcement Learning: Theory
  and Algorithms},
    author={Weiqin Chen, Mark S. Squillante, Chai Wah Wu and Santiago Paternain},
    journal={arXiv preprint arXiv:2406.14753},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14753},
    primaryClass={cs.LG stat.ME}
}