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Discriminator Based Resilient Multi-agent Deep Deterministic Policy Gradient Under Uncertain Faulty Agents

In recent years, Multi-agent reinforcement learning (MARL) is widely applied in various of fields, to achieve a global goal in a centralized or distributed manner. However, during its application it is crucial to be fault-tolerance as some agents behave abnormal. In this paper, we propose a Resilient Multi-gent Deep Deterministic Policy Gradient (RMADDPG) algorithm to achieve a cooperative task in the presence of faulty agents via centralized training decentralized execution. At training stage, each normal agent observes and records information only from other normal ones, without access to the faulty ones. Meanwhile, a discriminator is generated based on the well-trained actor network to identify each faulty agent via supervised learning. Followed by executing stage, each normal agent selects its action based on local observation according to its actor network and its discriminator, so as to achieve certain system goal. Specifically, RMADDPG offers a scheme to train agents for improved resilience against arbitrary number of faulty agents. Finally, a cooperative navigation experiment is provided to validate the effectiveness of the proposed algorithm.