Learning Multi-Robot Decentralized Macro-Action-Based Policies via a Centralized Q-Net

19 Sep 2019Yuchen XiaoJoshua HoffmanTian XiaChristopher Amato

In many real-world multi-robot tasks, high-quality solutions often require a team of robots to perform asynchronous actions under decentralized control. Decentralized multi-agent reinforcement learning methods have difficulty learning decentralized policies because of the environment appearing to be non-stationary due to other agents also learning at the same time... (read more)

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