Learning to Communicate with Deep Multi-Agent Reinforcement Learning

@inproceedings{Foerster2016LearningTC,
  title={Learning to Communicate with Deep Multi-Agent Reinforcement Learning},
  author={Jakob N. Foerster and Yannis M. Assael and Nando de Freitas and Shimon Whiteson},
  booktitle={NIPS},
  year={2016}
}
We consider the problem of multiple agents sensing and acting in environments with the goal of maximising their shared utility. In these environments, agents must learn communication protocols in order to share information that is needed to solve the tasks. By embracing deep neural networks, we are able to demonstrate end-to-end learning of protocols in complex environments inspired by communication riddles and multi-agent computer vision problems with partial observability. We propose two… CONTINUE READING

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