Multi-agent reinforcement learning approach based on reduced value function approximations

Abstract

This paper introduces novel online adaptive Reinforcement Learning approach based on Policy Iteration for multi-agent systems interacting on graphs. The approach uses reduced value functions to solve the coupled Bellman and Hamilton-Jacobi-Bellman equations for multi-agent systems. This done using only partial knowledge about the agents' dynamics. The convergence of the approach is shown to depend on the properties of the communication graph. The Policy Iteration approach is implemented in real-time using neural networks, where reduced value functions are considered to reduce the computational complexity.