  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.
