Online Model-Free Distributed Reinforcement Learning Approach for Networked Systems of Self-organizing Agents

Abstract

Control of large groups of robotic agents is driven by applications including military, aeronautics and astronautics, transportation network, and environmental monitoring. Cooperative control of networked multi-agent systems aims at driving the behavior of the group via feedback control inputs that encode the groups’ dynamics based on information sharing, with inter-agent communications that can be time varying and be spatially non-uniform. Notably, local interaction rules can induce coordinated behaviour, provided suitable network topologies.

Distributed learning paradigms are often necessary for this class of systems to be able to operate autonomously and robustly, without the need of external units providing centralized information. Compared with model-based protocols that can be computationally prohibitive due to their mathematical complexity and requirements in terms of feedback information, we present an online model-free algorithm for some nonlinear tracking problems with unknown system dynamics. This method prescribes the actuation forces of agents to follow the time-varying trajectory of a moving target. The tracking problem is addressed by an online value iteration process which requires measurements collected along the trajectories. A set of simulations are conducted to illustrate that the presented algorithm is well functioning in various reference-tracking scenarios.