Learning cooperative behaviours in adversarial multi-agent systems

This work extends an existing virtual multi-agent platform called RoboSumo to create TripleSumo – a platform for investigating multi-agent cooperative behaviors in continuous action spaces, with physical contact in an adversarial environment. In this paper we investigate a scenario in which two agents, namely Bug' and Ant’, must team up and push another agent Spider' out of the arena. To tackle this goal, the newly added agent Bug’ is trained during an ongoing match between Ant' and Spider’. `Bug’ must develop awareness of the other agents’ actions, infer the strategy of both sides, and eventually learn an action policy to cooperate. The reinforcement learning algorithm Deep Deterministic Policy Gradient (DDPG) is implemented with a hybrid reward structure combining dense and sparse rewards. The cooperative behavior is quantitatively evaluated by the mean probability of winning the match and mean number of steps needed to win.