Deep reinforcement learning for weakly coupled MDP's with continuous actions
<|reference_start|>Deep reinforcement learning for weakly coupled MDP's with continuous actions: This paper introduces the Lagrange Policy for Continuous Actions (LPCA), a reinforcement learning algorithm specifically designed for weakly coupled MDP problems with continuous action spaces. LPCA addresses the challenge of resource constraints dependent on continuous actions by introducing a Lagrange relaxation of the weakly coupled MDP problem within a neural network framework for Q-value computation. This approach effectively decouples the MDP, enabling efficient policy learning in resource-constrained environments. We present two variations of LPCA: LPCA-DE, which utilizes differential evolution for global optimization, and LPCA-Greedy, a method that incrementally and greadily selects actions based on Q-value gradients. Comparative analysis against other state-of-the-art techniques across various settings highlight LPCA's robustness and efficiency in managing resource allocation while maximizing rewards.<|reference_end|>
@article{robledo2024deep,
    title={Deep reinforcement learning for weakly coupled MDP's with continuous
  actions},
    author={Francisco Robledo (LMAP, UPPA, UPV / EHU), Urtzi Ayesta (IRIT-RMESS,
  UPV/EHU, CNRS), Konstantin Avrachenkov (Inria)},
    journal={arXiv preprint arXiv:2406.01099},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01099},
    primaryClass={cs.LG cs.AI cs.MA}
}