TASAC: a twin-actor reinforcement learning framework with stochastic policy for batch process control

TL;DR

This paper introduces TASAC, a novel reinforcement learning framework with twin actors and stochastic policies, designed to improve control of complex, nonlinear batch processes with model uncertainties.

Contribution

The paper proposes TASAC, an ensemble of twin actors within a maximum entropy RL framework, enhancing exploration and policy learning for batch process control.

Findings

Improved exploration through twin-actor ensemble.

Enhanced policy robustness in nonlinear batch processes.

Potential for better control performance under model mismatch.

Abstract

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.