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- W4282019967 abstract "Factored decentralized Markov decision process (Dec-MDP) is a framework for modeling sequential decision making problems in multi-agent systems. In this paper, we formalize the learning of numerical methods for hyperbolic partial differential equations (PDEs), specifically the Weighted Essentially Non-Oscillatory (WENO) scheme, as a factored Dec-MDP problem. We show that different reward formulations lead to either reinforcement learning (RL) or behavior cloning, and a homogeneous policy could be learned for all agents under the RL formulation with a policy gradient algorithm. Because the trained agents only act on their local observations, the multi-agent system can be used as a general numerical method for hyperbolic PDEs and generalize to different spatial discretizations, episode lengths, dimensions, and even equation types." @default.
- W4282019967 created "2022-06-13" @default.
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- W4282019967 date "2022-01-01" @default.
- W4282019967 modified "2023-09-27" @default.
- W4282019967 title "Multi-agent Learning of Numerical Methods for Hyperbolic PDEs with Factored Dec-MDP" @default.
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- W4282019967 doi "https://doi.org/10.1007/978-3-031-18192-4_15" @default.
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