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Found 5 papers, 1 papers with code
An Analysis of Multi-Agent Reinforcement Learning for Decentralized Inventory Control Systems
Most solutions to the inventory management problem assume a centralization of information that is incompatible with organisational constraints in real supply chain networks.
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.
Integrating process design and control using reinforcement learning
For this, one can formulate a bilevel optimization problem, with the design as the outer problem in the form of a mixed-integer nonlinear program (MINLP) and a stochastic optimal control as the inner problem.
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.
