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Found 4 papers, 3 papers with code
Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement
Comprehensive experiments show that VDGN policies significantly outperform error threshold-based policies in global error and cost metrics.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding
Symbolic regression is the process of identifying mathematical expressions that fit observed output from a black-box process.
Reinforcement Learning for Adaptive Mesh Refinement
Large-scale finite element simulations of complex physical systems governed by partial differential equations (PDE) crucially depend on adaptive mesh refinement (AMR) to allocate computational budget to regions where higher resolution is required.
Single Episode Policy Transfer in Reinforcement Learning
An even greater challenge is performing near-optimally in a single attempt at test time, possibly without access to dense rewards, which is not addressed by current methods that require multiple experience rollouts for adaptation.
