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TASAC: a twin-actor reinforcement learning framework with stochastic policy for batch process control. (arXiv:2204.10685v1 [cs.LG])
cs.LG updates on arXiv.org arxiv.org
Due to their complex nonlinear dynamics and batch-to-batch variability, batch
processes pose a challenge for process control. Due to the absence of accurate
models and resulting plant-model mismatch, these problems become harder to
address for advanced model-based control strategies. Reinforcement Learning
(RL), wherein an agent learns the policy by directly interacting with the
environment, offers a potential alternative in this context. RL frameworks with
actor-critic architecture have recently become popular for controlling systems
where state and action spaces are continuous. …
arxiv framework learning policy process reinforcement reinforcement learning stochastic