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Found 4 papers, 3 papers with code
Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvement
Current methods for end-to-end constructive neural combinatorial optimization usually train a policy using behavior cloning from expert solutions or policy gradient methods from reinforcement learning.
Deep reinforcement learning uncovers processes for separating azeotropic mixtures without prior knowledge
Deep reinforcement learning agents, trained without prior knowledge, have shown to outperform humans in various complex planning problems in recent years.
EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data
In this paper, we present EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data (EVARS-GPR), a novel online algorithm that is able to handle sudden shifts in the target variable scale of seasonal data.
Automated Synthesis of Steady-State Continuous Processes using Reinforcement Learning
The method is applied successfully to a reaction-distillation process in a quaternary system.
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