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Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach
Multi-agent reinforcement learning (MARL) becomes more challenging in th...
05/18/2021 ∙ by Yan Li, et al. ∙
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Reinforcement Learning for Adaptive Mesh Refinement
Large-scale finite element simulations of complex physical systems gover...
03/01/2021 ∙ by Jiachen Yang, et al. ∙
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GraphOpt: Learning Optimization Models of Graph Formation
Formation mechanisms are fundamental to the study of complex networks, b...
07/07/2020 ∙ by Rakshit Trivedi, et al. ∙
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Learning to Incentivize Other Learning Agents
The challenge of developing powerful and general Reinforcement Learning ...
06/10/2020 ∙ by Jiachen Yang, et al. ∙
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Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill Discovery
Human players in professional team sports achieve high level coordinatio...
12/07/2019 ∙ by Jiachen Yang, et al. ∙
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Single Episode Policy Transfer in Reinforcement Learning
Transfer and adaptation to new unknown environmental dynamics is a key c...
10/17/2019 ∙ by Jiachen Yang, et al. ∙
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Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimization
Traffic congestion in metropolitan areas is a world-wide problem that ca...
09/23/2019 ∙ by Zhi Zhang, et al. ∙
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CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning
We propose CM3, a new deep reinforcement learning method for cooperative...
09/13/2018 ∙ by Jiachen Yang, et al. ∙
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Learning Deep Mean Field Games for Modeling Large Population Behavior
We consider the problem of representing collective behavior of large pop...
11/08/2017 ∙ by Jiachen Yang, et al. ∙
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Deep Mean Field Games for Learning Optimal Behavior Policy of Large Populations
We consider the problem of representing a large population's behavior po...
11/08/2017 ∙ by Jiachen Yang, et al. ∙
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