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PC‑Gym is an open‑source toolkit developed by Imperial College London for evaluating reinforcement‑learning algorithms on chemical process‑control problems. The dataset includes multiple simulated chemical‑process environments featuring nonlinear dynamics, disturbances and constraints. It provides custom constraint handling, disturbance generation and reward‑function design to accelerate research at the intersection of machine learning and process systems engineering. By offering a standardized platform, PC‑Gym bridges theoretical RL advances with real industrial process‑control applications, giving researchers a tool to explore data‑driven control solutions.
