IDRL: Identifying Identities in Multi-Agent Reinforcement Learning with Ambiguous Identities

Multi-agent reinforcement learning(MARL) is a prevalent learning paradigm for solving stochastic games. In previous studies, agents in a game are defined to be teammates or enemies beforehand, and the relation of the agents is fixed throughout the game. Those works can hardly work in the games where the competitive and collaborative relationships are not public and dynamically changing, which is decided by the \textit{identities} of the agents. How to learn a successful policy in such a situation where the identities of agents are ambiguous is still a problem. Focusing on this problem, in this work, we develop a novel MARL framework: IDRL, which identifies the identities of the agents dynamically and then chooses the corresponding policy to perform in the task. In the IDRL framework, a relation network is constructed to deduce the identities of the multi-agents through feeling the kindness and hostility unleashed by other agents; a dangerous network is built to estimate the risk of the identification. We also propose an intrinsic reward to help train the relation network and the dangerous network to get a trade-off between the need to maximize external reward and the accuracy of identification. After identifying the cooperation-competition pattern among the agents, the proposed method IDRL applies one of the off-the-shelf MARL methods to learn the policy. Taking the poker game \textit{red-10} as the experiment environment, experiments show that the IDRL can achieve superior performance compared to the other MARL methods. Significantly, the relation network has the par performance to identify the identities of agents with top human players; the dangerous network reasonably avoids the risk of imperfect identification.