Corpus ID: 38548158

MULTI-AGENT DEEP REINFORCEMENT LEARNING

@inproceedings{Stanford2016MULTIAGENTDR,
  title={MULTI-AGENT DEEP REINFORCEMENT LEARNING},
  author={Maxim Egorov Stanford},
  year={2016}
}
This work introduces a novel approach for solving reinforcement learning problems in multi-agent settings. We propose a state reformulation of multi-agent problems in R2 that allows the system state to be represented in an image-like fashion. We then apply deep reinforcement learning techniques with a convolution neural network as the Q-value function approximator to learn distributed multi-agent policies. Our approach extends the traditional deep reinforcement learning algorithm by making use… Expand

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