TASAC: a twin-actor reinforcement learning framework with stochastic policy for batch process control
This addresses control problems in batch processes for industries like chemical manufacturing, but it appears incremental as it builds on existing actor-critic methods.
The paper tackles the challenge of controlling batch processes with complex nonlinear dynamics and variability by proposing TASAC, a twin-actor reinforcement learning framework with stochastic policy, which learns policies through direct interaction without accurate models.
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. It has been shown that an ensemble of actor and critic networks further helps the agent learn better policies due to the enhanced exploration due to simultaneous policy learning. To this end, the current study proposes a stochastic actor-critic RL algorithm, termed Twin Actor Soft Actor-Critic (TASAC), by incorporating an ensemble of actors for learning, in a maximum entropy framework, for batch process control.