Reinforcement Learning of Competitive Skills with Soccer Agents
Faculty of Computing, Health and Science
School of Computer and Security Science
Reinforcement learning plays an important role in Multi-Agent Systems. The reasoning and learning ability of agents is the key for autonomous agents. Autonomous agents are required to be able to adapt and learn in uncertain environments via communication and collaboration (in both competitive and cooperative situations). For real-time, non-deterministic and dynamic systems, it is often extremely complex and difficult to formally verify their properties a priori. In this paper, we adopt the reinforcement learning algorithms to verify goal-oriented agents’ competitive and cooperative learning abilities for decision making. In doing so, a simulation testbed is applied to test the learning algorithms in the specified scenarios. In addition, the function approximation technique known as tile coding (TC), is used to generate value functions, which can avoid the value function growing exponentially with the number of the state values.