Reinforcement Learning strategies for A-Team solving the Resource-Constrained Project Scheduling Problem

In this paper strategies for the A-Team with Reinforcement Learning (RL) for solving the Resource Constrained Project Scheduling Problem (RCPSP) are proposed and experimentally validated. The RCPSP belongs to the NP-hard problem class. To solve this problem a team of asynchronous agents (A-Team) has been implemented using the JABAT multiagent system. An A-Team is the set of objects including multiple agents and the common memory which through interactions produce solutions of optimization problems. These interactions are usually managed by a static strategy. In this paper the dynamic learning strategies are suggested. The proposed strategies based on reinforcement learning supervising interactions between optimization agents and the common memory. To validate the approach and compare strategies computational experiment has been carried out.