Consensus, Cooperative Learning, and Flocking for Multi-agent Predator Avoidance

Multi-agent coordination is highly desirable with many uses in a variety of tasks. In nature the phenomenon of coordinated flocking is highly common with applications related to defending or escaping from predators. In this thesis a hybrid multi-agent system that integrates consensus, cooperative learning, and flocking control to determine the direction of attacking predators and learn to flock away from them in a coordinated manner is proposed. This system is entirely distributed requiring only communication between neighboring agents. The fusion of consensus and collaborative reinforcement learning allows agents to cooperatively learn in a variety of multi-agent coordination tasks, but this thesis focuses on flocking away from attacking predators. The results of the flocking show that the agents are able to effectively flock to a target without collision with each other or obstacles. Multiple reinforcement learning methods are evaluated for the task with cooperative learning utilizing function approximation for state space reduction performing the best. The results of the proposed consensus algorithm show that it provides quick and accurate transmission of information between agents in the flock. Simulations are conducted to show and validate the proposed hybrid system in both one and two predator environments resulting in an efficient cooperative learning behavior. In the future the system of using consensus to determine the state and reinforcement learning to learn the states can be applied to additional multi-agent tasks