A Meta-Reinforcement Learning Approach to Process Control

• URL: http://arxiv.org/abs/2103.14060v1
• Date: Thu, 25 Mar 2021 18:20:56 GMT
• Title: A Meta-Reinforcement Learning Approach to Process Control
• Authors: Daniel G. McClement, Nathan P. Lawrence, Philip D. Loewen, Michael G. Forbes, Johan U. Backstr\"om, R. Bhushan Gopaluni
• Abstract summary: Meta-learning aims to quickly adapt models, such as neural networks, to perform new tasks. We construct a controller and meta-train the controller using a latent context variable through a separate embedding neural network. In both cases, our meta-learning algorithm adapts very quickly to new tasks, outperforming a regular DRL controller trained from scratch.
• Score: 3.9146761527401424
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Meta-learning is a branch of machine learning which aims to quickly adapt models, such as neural networks, to perform new tasks by learning an underlying structure across related tasks. In essence, models are being trained to learn new tasks effectively rather than master a single task. Meta-learning is appealing for process control applications because the perturbations to a process required to train an AI controller can be costly and unsafe. Additionally, the dynamics and control objectives are similar across many different processes, so it is feasible to create a generalizable controller through meta-learning capable of quickly adapting to different systems. In this work, we construct a deep reinforcement learning (DRL) based controller and meta-train the controller using a latent context variable through a separate embedding neural network. We test our meta-algorithm on its ability to adapt to new process dynamics as well as different control objectives on the same process. In both cases, our meta-learning algorithm adapts very quickly to new tasks, outperforming a regular DRL controller trained from scratch. Meta-learning appears to be a promising approach for constructing more intelligent and sample-efficient controllers.

Related papers

• ConML: A Universal Meta-Learning Framework with Task-Level Contrastive Learning [49.447777286862994]
  ConML is a universal meta-learning framework that can be applied to various meta-learning algorithms. We demonstrate that ConML integrates seamlessly with optimization-based, metric-based, and amortization-based meta-learning algorithms.
  arXiv  Detail & Related papers  (2024-10-08T12:22:10Z)
• Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning [58.3994826169858]
  We introduce RoboFuME, a reset-free fine-tuning system for robotic reinforcement learning. Our insights are to utilize offline reinforcement learning techniques to ensure efficient online fine-tuning of a pre-trained policy. Our method can incorporate data from an existing robot dataset and improve on a target task within as little as 3 hours of autonomous real-world experience.
  arXiv  Detail & Related papers  (2023-10-23T17:50:08Z)
• Meta-Reinforcement Learning for Adaptive Control of Second Order Systems [3.131740922192114]
  In process control, many systems have similar and well-understood dynamics, which suggests it is feasible to create a generalizable controller through meta-learning. We formulate a meta reinforcement learning (meta-RL) control strategy that takes advantage of known, offline information for training, such as a model structure. A key design element is the ability to leverage model-based information offline during training, while maintaining a model-free policy structure for interacting with new environments.
  arXiv  Detail & Related papers  (2022-09-19T18:51:33Z)
• Meta-Learning Online Control for Linear Dynamical Systems [2.867517731896504]
  We propose a meta-learning online control algorithm for the control setting. We characterize its performance by textitmeta-regret, the average cumulative regret across the tasks. We show that when the number of tasks are sufficiently large, our proposed approach achieves a meta-regret that is smaller by a factor $D/D*$ compared to an independent-learning online control algorithm.
  arXiv  Detail & Related papers  (2022-08-18T20:44:07Z)
• Meta Reinforcement Learning with Successor Feature Based Context [51.35452583759734]
  We propose a novel meta-RL approach that achieves competitive performance comparing to existing meta-RL algorithms. Our method does not only learn high-quality policies for multiple tasks simultaneously but also can quickly adapt to new tasks with a small amount of training.
  arXiv  Detail & Related papers  (2022-07-29T14:52:47Z)
• On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning [71.55412580325743]
  We show that multi-task pretraining with fine-tuning on new tasks performs equally as well, or better, than meta-pretraining with meta test-time adaptation. This is encouraging for future research, as multi-task pretraining tends to be simpler and computationally cheaper than meta-RL.
  arXiv  Detail & Related papers  (2022-06-07T13:24:00Z)
• Complex Locomotion Skill Learning via Differentiable Physics [30.868690308658174]
  Differentiable physics enables efficient-based optimizations of neural network (NN) controllers. We present a practical learning framework that outputs unified NN controllers capable of tasks with significantly improved complexity and diversity.
  arXiv  Detail & Related papers  (2022-06-06T04:01:12Z)
• Meta Reinforcement Learning for Adaptive Control: An Offline Approach [3.131740922192114]
  We formulate a meta reinforcement learning (meta-RL) control strategy that takes advantage of known, offline information for training. Our meta-RL agent has a recurrent structure that accumulates "context" for its current dynamics through a hidden state variable. In tests reported here, the meta-RL agent was trained entirely offline, yet produced excellent results in novel settings.
  arXiv  Detail & Related papers  (2022-03-17T23:58:52Z)
• Fully Online Meta-Learning Without Task Boundaries [80.09124768759564]
  We study how meta-learning can be applied to tackle online problems of this nature. We propose a Fully Online Meta-Learning (FOML) algorithm, which does not require any ground truth knowledge about the task boundaries. Our experiments show that FOML was able to learn new tasks faster than the state-of-the-art online learning methods.
  arXiv  Detail & Related papers  (2022-02-01T07:51:24Z)
• Learning Multi-Objective Curricula for Deep Reinforcement Learning [55.27879754113767]
  Various automatic curriculum learning (ACL) methods have been proposed to improve the sample efficiency and final performance of deep reinforcement learning (DRL) In this paper, we propose a unified automatic curriculum learning framework to create multi-objective but coherent curricula. In addition to existing hand-designed curricula paradigms, we further design a flexible memory mechanism to learn an abstract curriculum.
  arXiv  Detail & Related papers  (2021-10-06T19:30:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.