publication . Preprint . 2018

Actor-Attention-Critic for Multi-Agent Reinforcement Learning

Iqbal, Shariq; Sha, Fei;
Open Access English
  • Published: 05 Oct 2018
Abstract
Comment: ICML 2019 camera ready version
Subjects
free text keywords: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Multiagent Systems, Statistics - Machine Learning
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33 references, page 1 of 3

Lucian Bus¸oniu, Robert Babusˇka, and Bart De Schutter. Multi-agent reinforcement learning: An overview. In Innovations in multi-agent systems and applications-1, pp. 183-221. Springer, 2010.

Jinyoung Choi, Beom-Jin Lee, and Byoung-Tak Zhang. Multi-focus attention network for efficient deep reinforcement learning. arXiv preprint arXiv:1712.04603, December 2017. [OpenAIRE]

Felix Fischer, Michael Rovatsos, and Gerhard Weiss. Hierarchical reinforcement learning in communication-mediated multiagent coordination. In Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems-Volume 3, pp. 1334-1335. IEEE Computer Society, 2004.

Jakob Foerster, Ioannis Alexandros Assael, Nando de Freitas, and Shimon Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems, pp. 2137-2145, 2016.

Jakob Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson. Counterfactual Multi-Agent policy gradients. arXiv preprint arXiv:1705.08926, May 2017a.

Jakob Foerster, Nantas Nardelli, Gregory Farquhar, Triantafyllos Afouras, Philip H S Torr, Pushmeet Kohli, and Shimon Whiteson. Stabilising experience replay for deep Multi-Agent reinforcement learning. arXiv preprint arXiv:1702.08887, February 2017b. [OpenAIRE]

Alex Graves, Greg Wayne, and Ivo Danihelka. arXiv:1410.5401, 2014.

He He, Jordan Boyd-Graber, Kevin Kwok, and Hal Daume´ III. Opponent modeling in deep reinforcement learning. In International Conference on Machine Learning, pp. 1804-1813, 2016.

Nicolas Heess, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, Ali Eslami, Martin Riedmiller, et al. Emergence of locomotion behaviours in rich environments. arXiv preprint arXiv:1707.02286, 2017. [OpenAIRE]

Eric Jang, Shixiang Gu, and Ben Poole. Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144, 2016.

Jiechuan Jiang and Zongqing Lu. Learning attentional communication for multi-agent cooperation. arXiv preprint arXiv:1805.07733, 2018. [OpenAIRE]

Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.

Vijay R Konda and John N Tsitsiklis. Actor-critic algorithms. In Advances in neural information processing systems, pp. 1008-1014, 2000.

Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971, 2015.

Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. A structured self-attentive sentence embedding. arXiv preprint arXiv:1703.03130, 2017. [OpenAIRE]

33 references, page 1 of 3
Abstract
Comment: ICML 2019 camera ready version
Subjects
free text keywords: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Multiagent Systems, Statistics - Machine Learning
Download from
33 references, page 1 of 3

Lucian Bus¸oniu, Robert Babusˇka, and Bart De Schutter. Multi-agent reinforcement learning: An overview. In Innovations in multi-agent systems and applications-1, pp. 183-221. Springer, 2010.

Jinyoung Choi, Beom-Jin Lee, and Byoung-Tak Zhang. Multi-focus attention network for efficient deep reinforcement learning. arXiv preprint arXiv:1712.04603, December 2017. [OpenAIRE]

Felix Fischer, Michael Rovatsos, and Gerhard Weiss. Hierarchical reinforcement learning in communication-mediated multiagent coordination. In Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems-Volume 3, pp. 1334-1335. IEEE Computer Society, 2004.

Jakob Foerster, Ioannis Alexandros Assael, Nando de Freitas, and Shimon Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems, pp. 2137-2145, 2016.

Jakob Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson. Counterfactual Multi-Agent policy gradients. arXiv preprint arXiv:1705.08926, May 2017a.

Jakob Foerster, Nantas Nardelli, Gregory Farquhar, Triantafyllos Afouras, Philip H S Torr, Pushmeet Kohli, and Shimon Whiteson. Stabilising experience replay for deep Multi-Agent reinforcement learning. arXiv preprint arXiv:1702.08887, February 2017b. [OpenAIRE]

Alex Graves, Greg Wayne, and Ivo Danihelka. arXiv:1410.5401, 2014.

He He, Jordan Boyd-Graber, Kevin Kwok, and Hal Daume´ III. Opponent modeling in deep reinforcement learning. In International Conference on Machine Learning, pp. 1804-1813, 2016.

Nicolas Heess, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, Ali Eslami, Martin Riedmiller, et al. Emergence of locomotion behaviours in rich environments. arXiv preprint arXiv:1707.02286, 2017. [OpenAIRE]

Eric Jang, Shixiang Gu, and Ben Poole. Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144, 2016.

Jiechuan Jiang and Zongqing Lu. Learning attentional communication for multi-agent cooperation. arXiv preprint arXiv:1805.07733, 2018. [OpenAIRE]

Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.

Vijay R Konda and John N Tsitsiklis. Actor-critic algorithms. In Advances in neural information processing systems, pp. 1008-1014, 2000.

Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971, 2015.

Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. A structured self-attentive sentence embedding. arXiv preprint arXiv:1703.03130, 2017. [OpenAIRE]

33 references, page 1 of 3
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