Actor-Critic Policy Learning in Cooperative Planning

Abstract

In this paper, we introduce a method for learning and adapting cooperative control strategies in real-time stochastic domains. Our framework is an instance of the intelligent cooperative control architecture (iCCA)[superscript 1]. The agent starts by following the "safe" plan calculated by the planning module and incrementally adapting its policy to maximize the cumulative rewards. Actor-critic and consensus-based bundle algorithm (CBBA) were employed as the building blocks of the iCCA framework. We demonstrate the performance of our approach by simulating limited fuel unmanned aerial vehicles aiming for stochastic targets. In one experiment where the optimal solution can be calculated, the integrated framework boosted the optimality of the solution by an average of %10, when compared to running each of the modules individually, while keeping the computational load within the requirements for real-time implementation.