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

arXiv: https://arxiv.org/abs/2204.10685 · HTML: https://mlsanity.com/paper/2204.10685/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 42 references that are in the map.
- First posted on arXiv 22 Apr 2022.

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Reinforcement Learning for Batch Bioprocess Optimization](https://mlsanity.com/paper/1904.07292/) (2019)
- [ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search](https://mlsanity.com/paper/1811.02696/) (2018)
- [Learning to Run with Actor-Critic Ensemble](https://mlsanity.com/paper/1712.08987/) (2017)
- [Twin actor twin delayed deep deterministic policy gradient (TATD3) learning for batch process control](https://mlsanity.com/paper/2102.13012/) (2021)

## Abstract (arXiv)

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.