Toward competitive multi-agents in Polo game based on reinforcement learning

The learning of agents in a competitive space such as a game is a challenging task. The aim of the proposed research is to improve the reinforcement learning techniques in a competitive multi-agent for the Polo game. First, the video dataset is prepared. Then, the rules of the Polo game are extracted as a class diagram. An architecture is designed for multi-agent team in the Polo game. Therefore, an algorithm is proposed for the temporal difference in the game belief space for improving reward catching. The reward function is implemented in the agent team. Finally, the research improvement is evaluated by increasing 31 units in comparison with previous work. Therefore, competitive learning in the agent team has been improved.