A life-cycle optimization framework for deteriorating infrastructure under hazards is formulated as an MDP with a Kronecker-factored tensor method that reduces computational complexity from exponential to linear while preserving exact dynamic programming solutions.
arXiv preprint arXiv:2401.12455 , year=
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MARS replaces additive clipping and soft penalties in multi-agent trust-region methods with a symmetric geometric barrier, matching or exceeding MAPPO and MASPO performance across 47 tasks in eight environments.
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Probabilistic Hazard Analysis Framework with Stochastic Optimal Control for Deteriorating Civil Infrastructure Systems
A life-cycle optimization framework for deteriorating infrastructure under hazards is formulated as an MDP with a Kronecker-factored tensor method that reduces computational complexity from exponential to linear while preserving exact dynamic programming solutions.
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Rethinking Ratio-Based Trust Regions for Policy Optimization in Multi-Agent Reinforcement Learning
MARS replaces additive clipping and soft penalties in multi-agent trust-region methods with a symmetric geometric barrier, matching or exceeding MAPPO and MASPO performance across 47 tasks in eight environments.