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We present a framework combining hierarchical and multi-agent deep reinforcement learning approaches to solve coordination problems among a multitude of agents using a semi-decentralized model.
Reinforcement learning: An introduction
R. S. Sutton and A. G. Barto · 1998
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Playing atari with deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller · 2013
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Learning to communicate with deep multi-agent reinforcement learning
J. Foerster, Y. Assael, N. Freitas, and S. Whiteson · 2016
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Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation
T. D. Kulkarni, K. Narasimhan, A. Saeedi, and J. Tenenbaum · 2016
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Learning multiagent communication with backpropagation
S. Sukhbaatar, R. Fergus, et al · 2016
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Learning cooperative visual dialog agents with deep reinforcement learning
A. Das, S. Kottur, J. Moura, and D. Batra · 2017
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Counterfactual multi-agent policy gradients
J. Foerster, G. Farquhar, T. Afouras, N. Nardelli, and S. Whiteson · 2017
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Deal or no deal? end-to-end learning of negotiation dialogues
M. Lewis, D. Yarats, Y. Dauphin, D. Parikh, and D. Batra · 2017
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Emergence of compositional language in multi-agent populations
I. Mordatch and P. Abbeel · 2017
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Composite task-completion dialogue policy learning via hierarchical deep reinforcement learning
B. Peng, X. Li, L. Li, J. Gao, A. Celikyilmaz, S. Lee, and K.-F. Wong · 2017
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