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A major challenge for Multi-Agent Systems is enabling agents to adapt dynamically to diverse environments in which opponents and teammates may continually change.
Intention, Plans, and Practical Reason
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C. L. Baker, R. Saxe, and J. B. Tenenbaum · 2009
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P. Stone, G. Kaminka, S. Kraus, and J. Rosenschein · 2010
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Coordinate to cooperate or compete: abstract goals and joint intentions in social interaction
M. Kleiman-Weiner, M. K. Ho, J. L. Austerweil, M. L. Littman, and J. B. Tenenbaum · 2016
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Asynchronous methods for deep reinforcement learning
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Multi-agent actor-critic for mixed cooperative-competitive environments
R. Lowe, Y. Wu, A. Tamar, J. Harb, P. Abbeel, and I. Mordatch · 2017
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Emergence of grounded compositional language in multi-agent populations
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Attention is all you need
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N. Rabinowitz, F. Perbet, F. Song, C. Zhang, S. A. Eslami, and M. Botvinick · 2018
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Reasoning about hypothetical agent behaviours and their parameters
S. V. Albrecht and P. Stone · 2019
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Theory of minds: Understanding behavior in groups through inverse planning
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Expected value of communication for planning in ad hoc teamwork
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Towards open ad hoc teamwork using graph-based policy learning
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Modeling communication to coordinate perspectives in cooperation
S. Stacy, C. Li, M. Zhao, Y. Yun, Q. Zhao, M. Kleiman-Weiner, and T. Gao · 2021
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Too many cooks: Bayesian inference for coordinating multi-agent collaboration
S. A. Wu, R. E. Wang, J. A. Evans, J. B. Tenenbaum, D. C. Parkes, and M. Kleiman-Weiner · 2021
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Learning latent representations to influence multi-agent interaction
A. Xie, D. Losey, R. Tolsma, C. Finn, and D. Sadigh · 2021
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Deep interactive Bayesian reinforcement learning via meta-learning
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X. Gao, R. Gong, Y. Zhao, S. Wang, T. Shu, and S.-C. Zhu · 2020
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Evolutionary population curriculum for scaling multi-agent reinforcement learning
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Score-based generative modeling through stochastic differential equations
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Bootstrapping an imagined we for cooperation
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ToM2C: Target-oriented multi-agent communication and cooperation with theory of mind
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TarGF: Learning target gradient field for object rearrangement
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The surprising effectiveness of PPO in cooperative multi-agent games
C. Yu, A. Velu, E. Vinitsky, J. Gao, Y. Wang, A. Bayen, and Y. Wu · 2022
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SocialGFs: Learning social gradient fields for multi-agent reinforcement learning
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