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Found 6 papers, 2 papers with code
Distributional Reinforcement Learning for Scheduling of Chemical Production Processes
In this work, we present a RL methodology tailored to efficiently address production scheduling problems in the presence of uncertainty.
Safe Chance Constrained Reinforcement Learning for Batch Process Control
Specifically, we propose a data-driven approach that utilizes Gaussian processes for the offline simulation model and use the associated posterior uncertainty prediction to account for joint chance constraints and plant-model mismatch.
Constrained Model-Free Reinforcement Learning for Process Optimization
We propose an 'oracle'-assisted constrained Q-learning algorithm that guarantees the satisfaction of joint chance constraints with a high probability, which is crucial for safety critical tasks.
Chance Constrained Policy Optimization for Process Control and Optimization
We propose a chance constrained policy optimization (CCPO) algorithm which guarantees the satisfaction of joint chance constraints with a high probability - which is crucial for safety critical tasks.
Constrained Reinforcement Learning for Dynamic Optimization under Uncertainty
We use chance constraints to guarantee the probabilistic satisfaction of process constraints, which is accomplished by introducing backoffs, such that the optimal policy and backoffs are computed simultaneously.
Reinforcement Learning for Batch Bioprocess Optimization
In this work, we applied the Policy Gradient method from batch-to-batch to update a control policy parametrized by a recurrent neural network.
Optimization and Control Systems and Control
