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Found 4 papers, 2 papers with code
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.
Policy-Based Self-Competition for Planning Problems
AlphaZero-type algorithms may stop improving on single-player tasks in case the value network guiding the tree search is unable to approximate the outcome of an episode sufficiently well.
Deep Anomaly Detection on Tennessee Eastman Process Data
Our extensive study will facilitate choosing appropriate anomaly detection methods in industrial applications.
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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