Teaching coordinated strategies to soccer robots via imitation

Developing coordination among multiple agents and enabling them to exhibit teamwork is a challenging yet exciting task that can benefit many of the complex real-life problems. This research uses imitation to learn collaborative strategies for a team of agents. Imitation based learning involves learning from an expert by observing him/her demonstrating a task and then replicating it. The key idea is to involve multiple human experts during demonstration to teach autonomous agents how to work in coordination. The effectiveness of the proposed methodology has been assessed in a goal defending scenario of the RoboCup Soccer Simulation 3D league. The process involves multiple human demonstrators controlling soccer agents via game controllers and demonstrating them how to play soccer in coordination. The data gathered during this phase is used as training data to learn a classification model which is later used by the soccer agents to make autonomous decisions during actual matches. Different performance evaluation metrics are derived to compare the performance of imitating agent with that of the human-driven agent and hand-coded (if-then-else rules) agent.