  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.
