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Conference Papers Year : 2024

Deep reinforcement learning for weakly coupled MDP's with continuous actions

Abstract

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 chal- lenge 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 evolu- tion 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 robust- ness and efficiency in managing resource allocation while maximizing rewards.
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Dates and versions

hal-04594762 , version 1 (30-05-2024)
hal-04594762 , version 2 (11-06-2024)

Identifiers

  • HAL Id : hal-04594762 , version 2

Cite

Francisco Robledo, Urtzi Ayesta, Konstantin Avrachenkov. Deep reinforcement learning for weakly coupled MDP's with continuous actions. ACM SIGMETRICS / ASMTA 2024, Jun 2024, Venise, Italy. ⟨hal-04594762v2⟩
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