Multiagent Learning Through Indirect Encoding

Designing a system of multiple, heterogeneous agents that cooperate to achieve a common goal is a difficult task, but it is also a common real-world problem. Multiagent learning addresses this problem by training the team to cooperate through a learning algorithm. However, most traditional approaches treat multiagent learning as a combination of multiple single-agent learning problems. This perspective leads to many inefficiencies in learning such as the problem of reinvention, whereby fundamental skills and policies that all agents should possess must be rediscovered independently for each team member. For example, in soccer, all the players know how to pass and kick the ball, but a traditional algorithm has no way to share such vital information because it has no way to relate the policies of agents to each other. In this dissertation a new approach to multiagent learning that seeks to address these issues is presented. This approach, called multiagent HyperNEAT, represents teams as a pattern of policies rather than individual agents. The main idea is that an agent’s location within a canonical team layout (such as a soccer team at the start of a game) tends to dictate its role within that team, called the policy geometry. For example, as soccer positions move from goal to center they become more offensive and less defensive, a concept that is compactly represented as a pattern. iii The first major contribution of this dissertation is a new method for evolving neural network controllers called HyperNEAT, which forms the foundation of the second contribution and primary focus of this work, multiagent HyperNEAT. Multiagent learning in this dissertation is investigated in predator-prey, room-clearing, and patrol domains, providing a real-world context for the approach. Interestingly, because the teams in multiagent HyperNEAT are represented as patterns they can scale up to an infinite number of multiagent policies that can be sampled from the policy geometry as needed. Thus the third contribution is a method for teams trained with multiagent HyperNEAT to dynamically scale their size without further learning. Fourth, the capabilities to both learn and scale in multiagent HyperNEAT are compared to the traditional multiagent SARSA(λ) approach in a comprehensive study. The fifth contribution is a method for efficiently learning and encoding multiple policies for each agent on a team to facilitate learning in multi-task domains. Finally, because there is significant interest in practical applications of multiagent learning, multiagent HyperNEAT is tested in a real-world military patrolling application with actual Khepera III robots. The ultimate goal is to provide a new perspective on multiagent learning and to demonstrate the practical benefits of training heterogeneous, scalable multiagent teams through generative encoding

Neuroevolution of mobile ad hoc networks

This paper describes a study of the evolution of distributed behavior, specifically the control of agents in a mobile ad hoc network, using neuroevolution. In neuroevolution, a population of artificial neural networks (ANNs) are subject to mutation and natural selection. For this study, we compare three different neuroevolutionary systems: a direct encoding, an indirect encoding, and an indirect encoding that supports heterogeneity. Multiple variations of each of these systems were tested on a problem where agents were able to coordinate their collective behavior. Specifically, movement of agents in a simulated physics environment affected which agents were able to communicate with each other. The results of experiments indicate that this is a challenging problem domain for neuroevolution, and although direct and indirect encodings tended to perform similarly in our tests, the strategies employed by indirect encodings tended to favor stable, cohesive groups, while the direct encoding versions appeared more stochastic in nature