Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability

@inproceedings{Omidshafiei2017DeepDM,
  title={Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability},
  author={Shayegan Omidshafiei and Jason Pazis and Christopher Amato and Jonathan P. How and John Vian},
  booktitle={ICML},
  year={2017}
}
Many real-world tasks involve multiple agents with partial observability and limited communication. Learning is challenging in these settings due to local viewpoints of agents, which perceive the world as non-stationary due to concurrentlyexploring teammates. Approaches that learn specialized policies for individual tasks face problems when applied to the real world: not only do agents have to learn and store distinct policies for each task, but in practice identities of tasks are often non… CONTINUE READING
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