Reinforcement Learning In Soccer Simulation

Being of a high complexity, most multi-agent systems are difficult to deal with by a hand-coded approach to decision making. In such complicated environments in which decision making processes should be controlled from both the individuals points' of view and the whole team, the common approach to the subject is the Reinforcement Learning (RL) method which is mainly based on learning the optimal policy through mapping this task to an episodic reinforcement learning framework. Reinforcement learning is the problem of generating optimal behavior in a sequential decision making environment given the opportunity of interacting with it. Since the Robocop domain is a multi-agent dynamic environment, with notable features making it outstanding for multi-agent simulation benchmarks, it has been largely used as a basis for international multi-agent simulation competitions and research challenges. For this purpose, reinforcement learning problems should be made "understandable" for "agents" which are Robocop players in this case. To make the agents "aware" of what they are intended to do, this paper will provide an overview of different methods and alternative approaches as a starting point for robocupers who are not familiar with reinforcement learning problems in the Robocop domain.