Reinforcement learning optimizes controlled variable selection for self-optimizing control by embedding the structure in an actor network and using economic rewards, showing better dynamic performance than a steady-state baseline in a CSTR simulation under disturbances.
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Self-Optimizing Control of Continuous Processes Based on Reinforcement Learning
Reinforcement learning optimizes controlled variable selection for self-optimizing control by embedding the structure in an actor network and using economic rewards, showing better dynamic performance than a steady-state baseline in a CSTR simulation under disturbances.