Resolving Implicit Coordination in Multi-Agent Deep Reinforcement Learning with Deep Q-Networks & Game Theory

We address two major challenges of implicit coordination in multi-agent deep reinforcement learning: non-stationarity and exponential growth of state-action space, by combining Deep-Q Networks for policy learning with Nash equilibrium for action selection. Q-values proxy as payoffs in Nash settings, and mutual best responses define joint action selection... (read more)

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METHOD TYPE
Dense Connections
Feedforward Networks
Convolution
Convolutions
Double Q-learning
Off-Policy TD Control
Dueling Network
Q-Learning Networks