# Twin actor twin delayed deep deterministic policy gradient (TATD3) learning for batch process control

arXiv: https://arxiv.org/abs/2102.13012 · HTML: https://mlsanity.com/paper/2102.13012/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2021.

## Built on by (most cited first, 5 of 5)

- [Safe Chance Constrained Reinforcement Learning for Batch Process Control](https://mlsanity.com/paper/2104.11706/) (2021)
- [Machine learning for industrial sensing and control: A survey and practical perspective](https://mlsanity.com/paper/2401.13836/) (2024)
- [Surgical Task Automation Using Actor-Critic Frameworks and Self-Supervised Imitation Learning](https://mlsanity.com/paper/2409.02724/) (2024)
- [Reinforcement Learning-based Control via Y-wise Affine Neural Networks (YANNs)](https://mlsanity.com/paper/2508.16474/) (2025)
- [TASAC: a twin-actor reinforcement learning framework with stochastic policy for batch process control](https://mlsanity.com/paper/2204.10685/) (2022)

## Abstract (arXiv)

Control of batch processes is a difficult task due to their complex nonlinear dynamics and unsteady-state operating conditions within batch and batch-to-batch. It is expected that some of these challenges can be addressed by developing control strategies that directly interact with the process and learning from experiences. Recent studies in the literature have indicated the advantage of having an ensemble of actors in actor-critic Reinforcement Learning (RL) frameworks for improving the policy. The present study proposes an actor-critic RL algorithm, namely, twin actor twin delayed deep deterministic policy gradient (TATD3), by incorporating twin actor networks in the existing twin-delayed deep deterministic policy gradient (TD3) algorithm for the continuous control. In addition, two types of novel reward functions are also proposed for TATD3 controller. We showcase the efficacy of the TATD3 based controller for various batch process examples by comparing it with some of the existing RL algorithms presented in the literature.