Reinforcement Learning

Neural ODEs as Feedback Policies for Nonlinear Optimal Control
Neural ordinary differential equations (Neural ODEs) define continuous time dynamical systems with neural networks. The interest in …
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Integrating process design and control using reinforcement learning
To create efficient-high performing processes, one must find an optimal design with its corresponding controller that ensures optimal …
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Chance Constrained Policy Optimization for Process Control and Optimization
Chemical process optimization and control are affected by (1) plant-model mismatch, (2) process disturbances, and (3) constraints for …
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Constrained Reinforcement Learning for Dynamic Optimization under Uncertainty
Dynamic real-time optimization (DRTO) is a challenging task due to the fact that optimal operating conditions must be computed in real …
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Reinforcement Learning for Batch-to-Batch Bioprocess Optimisation
Bioprocesses have received great attention from the scientific community as an alternative to fossil-based products by …
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