Scalable Reinforcement Learning for Formation Control with Collision Avoidance : Localized policy gradient algorithm with continuous state and action space

Sammanfattning: In the last decades, significant theoretical advances have been made on the field of distributed mulit-agent control theory. One of the most common systems that can be modelled as multi-agent systems are the so called formation control problems, in which a network of mobile agents is controlled to move towards a desired final formation. These problems additionally pose practical challenges, namely limited access to information about the global state of the system, which justify the use distributed and localized approaches for solving the control problem. The problem is further complicated if partial or no information is known about the dynamic model of the system. A widely used fundamental challenge of this approach in this setting is that the state-action space size scales exponentially with the number of agents, rendering the problem intractable for a large networks. This thesis presents a scalable and localized reinforcement learning approach to a traditional multi-agent formation control problem, with collision avoidance. A scalable reinforcement learning advantage actor critic algorithm is presented, based on previous work in the literature. Sub-optimal bounds are calculated for the accumulated reward and policy gradient localized approximations. The algorithm is tested on a two dimensional setting, with a network of mobile agents following simple integrator dynamics and stochastic localized policies. Neural networks are used to approximate the continuous value functions and policies. The formation control with collisions avoidance formulation and the algorithm presented show good scalability properties, with a polynomial increase in the number of function approximations parameters with number of agents. The reduced number of parameters decreases learning time for bigger networks, although the efficiency of computation is decreased compared to state of the art machine learning implementations. The policies obtained achieve probably safe trajectories although the lack of dynamic model makes it impossible to guarantee safety.