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.
Computers & Chemical Engineering , volume =
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.SY 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.