publication . Preprint . 2019

Learning Transferable Cooperative Behavior in Multi-Agent Teams

Agarwal, Akshat; Kumar, Sumit; Sycara, Katia;
Open Access English
  • Published: 04 Jun 2019
Abstract
While multi-agent interactions can be naturally modeled as a graph, the environment has traditionally been considered as a black box. We propose to create a shared agent-entity graph, where agents and environmental entities form vertices, and edges exist between the vertices which can communicate with each other. Agents learn to cooperate by exchanging messages along the edges of this graph. Our proposed multi-agent reinforcement learning framework is invariant to the number of agents or entities present in the system as well as permutation invariance, both of which are desirable properties for any multi-agent system representation. We present state-of-the-art r...
Subjects
arXiv: Computer Science::Multiagent Systems
free text keywords: Computer Science - Machine Learning, Computer Science - Multiagent Systems, Statistics - Machine Learning
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25 references, page 1 of 2

Tucker Balch and Ronald C Arkin. Behavior-based formation control for multirobot teams. IEEE transactions on robotics and automation, 14(6):926-939, 1998.

Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. Curriculum learning. In Proceedings of the 26th annual international conference on machine learning, pages 41-48. ACM, 2009. [OpenAIRE]

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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, pages 2137-2145, 2016.

Jakob N Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson. Counterfactual multi-agent policy gradients. In Thirty-Second AAAI Conference on Artificial Intelligence, 2018.

Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. Neural message passing for quantum chemistry. In Proceedings of the 34th International Conference on Machine Learning-Volume 70, pages 1263-1272. JMLR. org, 2017. [OpenAIRE]

Yedid Hoshen. Vain: Attentional multi-agent predictive modeling. In Advances in Neural Information Processing Systems, pages 2701-2711, 2017. [OpenAIRE]

Jiechuan Jiang, Chen Dun, and Zongqing Lu. Graph convolutional reinforcement learning for multi-agent cooperation. arXiv preprint arXiv:1810.09202, 2018. [OpenAIRE]

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.

Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch. Multi-agent actor-critic for mixed cooperative-competitive environments. In Advances in Neural Information Processing Systems, pages 6379-6390, 2017.

Mehran Mesbahi and Magnus Egerstedt. Graph theoretic methods in multiagent networks, volume 33. Princeton University Press, 2010. [OpenAIRE]

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013. [OpenAIRE]

Igor Mordatch and Pieter Abbeel. Emergence of grounded compositional language in multi-agent populations. In Thirty-Second AAAI Conference on Artificial Intelligence, 2018.

Venkatesh G Rao and Dennis S Bernstein. Naive control of the double integrator. IEEE Control Systems Magazine, 21(5):86-97, 2001.

Tabish Rashid, Mikayel Samvelyan, Christian Schroeder de Witt, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson. Qmix: monotonic value function factorisation for deep multi-agent reinforcement learning. arXiv preprint arXiv:1803.11485, 2018. [OpenAIRE]

25 references, page 1 of 2
Abstract
While multi-agent interactions can be naturally modeled as a graph, the environment has traditionally been considered as a black box. We propose to create a shared agent-entity graph, where agents and environmental entities form vertices, and edges exist between the vertices which can communicate with each other. Agents learn to cooperate by exchanging messages along the edges of this graph. Our proposed multi-agent reinforcement learning framework is invariant to the number of agents or entities present in the system as well as permutation invariance, both of which are desirable properties for any multi-agent system representation. We present state-of-the-art r...
Subjects
arXiv: Computer Science::Multiagent Systems
free text keywords: Computer Science - Machine Learning, Computer Science - Multiagent Systems, Statistics - Machine Learning
Related Organizations
Download from
25 references, page 1 of 2

Tucker Balch and Ronald C Arkin. Behavior-based formation control for multirobot teams. IEEE transactions on robotics and automation, 14(6):926-939, 1998.

Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. Curriculum learning. In Proceedings of the 26th annual international conference on machine learning, pages 41-48. ACM, 2009. [OpenAIRE]

Abhishek Das, Théophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Michael Rabbat, and Joelle Pineau. Tarmac: Targeted multi-agent communication. arXiv preprint arXiv:1810.11187, 2018. [OpenAIRE]

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, pages 2137-2145, 2016.

Jakob N Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson. Counterfactual multi-agent policy gradients. In Thirty-Second AAAI Conference on Artificial Intelligence, 2018.

Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. Neural message passing for quantum chemistry. In Proceedings of the 34th International Conference on Machine Learning-Volume 70, pages 1263-1272. JMLR. org, 2017. [OpenAIRE]

Yedid Hoshen. Vain: Attentional multi-agent predictive modeling. In Advances in Neural Information Processing Systems, pages 2701-2711, 2017. [OpenAIRE]

Jiechuan Jiang, Chen Dun, and Zongqing Lu. Graph convolutional reinforcement learning for multi-agent cooperation. arXiv preprint arXiv:1810.09202, 2018. [OpenAIRE]

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.

Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch. Multi-agent actor-critic for mixed cooperative-competitive environments. In Advances in Neural Information Processing Systems, pages 6379-6390, 2017.

Mehran Mesbahi and Magnus Egerstedt. Graph theoretic methods in multiagent networks, volume 33. Princeton University Press, 2010. [OpenAIRE]

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013. [OpenAIRE]

Igor Mordatch and Pieter Abbeel. Emergence of grounded compositional language in multi-agent populations. In Thirty-Second AAAI Conference on Artificial Intelligence, 2018.

Venkatesh G Rao and Dennis S Bernstein. Naive control of the double integrator. IEEE Control Systems Magazine, 21(5):86-97, 2001.

Tabish Rashid, Mikayel Samvelyan, Christian Schroeder de Witt, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson. Qmix: monotonic value function factorisation for deep multi-agent reinforcement learning. arXiv preprint arXiv:1803.11485, 2018. [OpenAIRE]

25 references, page 1 of 2
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