Guided Policy Search Based Control of a High Dimensional Advanced Manufacturing Process

• URL: http://arxiv.org/abs/2009.05838v1
• Date: Sat, 12 Sep 2020 17:58:50 GMT
• Title: Guided Policy Search Based Control of a High Dimensional Advanced Manufacturing Process
• Authors: Amit Surana, Kishore Reddy, Matthew Siopis
• Abstract summary: We apply guided policy search (GPS) based reinforcement learning framework for a high dimensional optimal control problem arising in an additive manufacturing process. A realistic simulation model of the deposition process is used to train a neural network policy using GPS. A closed loop control based on the trained policy and in-situ measurement of the deposition profile is tested experimentally, and shows promising performance.
• Score: 1.5469452301122175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we apply guided policy search (GPS) based reinforcement learning framework for a high dimensional optimal control problem arising in an additive manufacturing process. The problem comprises of controlling the process parameters so that layer-wise deposition of material leads to desired geometric characteristics of the resulting part surface while minimizing the material deposited. A realistic simulation model of the deposition process along with carefully selected set of guiding distributions generated based on iterative Linear Quadratic Regulator is used to train a neural network policy using GPS. A closed loop control based on the trained policy and in-situ measurement of the deposition profile is tested experimentally, and shows promising performance.

Related papers

• Continual Model-based Reinforcement Learning for Data Efficient Wireless Network Optimisation [73.04087903322237]
  We formulate throughput optimisation as Continual Reinforcement Learning of control policies. Simulation results suggest that the proposed system is able to shorten the end-to-end deployment lead-time by two-fold.
  arXiv  Detail & Related papers  (2024-04-30T11:23:31Z)
• Tensor-based process control and monitoring for semiconductor manufacturing with unstable disturbances [13.114681056884832]
  This paper proposes a novel process control and monitoring method for the complex structure of high-dimensional image-based overlay errors. The proposed method aims to reduce overlay errors using limited control recipes.
  arXiv  Detail & Related papers  (2024-01-31T03:35:08Z)
• In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution States [84.24300005271185]
  We propose a control filter that wraps any reference policy and effectively encourages the system to stay in-distribution with respect to offline-collected safe demonstrations. Our method is effective for two different visuomotor control tasks in simulation environments, including both top-down and egocentric view settings.
  arXiv  Detail & Related papers  (2023-01-27T22:28:19Z)
• Regret Analysis of Online Gradient Descent-based Iterative Learning Control with Model Mismatch [4.922572106422331]
  The performance of an online gradient-descent based scheme using inexact gradient information is analyzed. Fundamental limitations of the scheme and its integration with adaptation mechanisms are investigated.
  arXiv  Detail & Related papers  (2022-04-10T16:35:27Z)
• Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects and Dynamics [90.81186513537777]
  We propose a novel compound kernel that captures the control-affine nature of the problem. We show that this resulting optimization problem is convex, and we call it Gaussian Process-based Control Lyapunov Function Second-Order Cone Program (GP-CLF-SOCP)
  arXiv  Detail & Related papers  (2020-11-14T01:27:32Z)
• Optimal Control-Based Baseline for Guided Exploration in Policy Gradient Methods [8.718494948845711]
  In this paper, a novel optimal control-based baseline function is presented for the policy gradient method in deep reinforcement learning. We validate our baseline on robot learning tasks, showing its effectiveness in guided exploration.
  arXiv  Detail & Related papers  (2020-11-04T00:11:56Z)
• Logistic Q-Learning [87.00813469969167]
  We propose a new reinforcement learning algorithm derived from a regularized linear-programming formulation of optimal control in MDPs. The main feature of our algorithm is a convex loss function for policy evaluation that serves as a theoretically sound alternative to the widely used squared Bellman error.
  arXiv  Detail & Related papers  (2020-10-21T17:14:31Z)
• Reinforcement Learning for Low-Thrust Trajectory Design of Interplanetary Missions [77.34726150561087]
  This paper investigates the use of reinforcement learning for the robust design of interplanetary trajectories in presence of severe disturbances. An open-source implementation of the state-of-the-art algorithm Proximal Policy Optimization is adopted. The resulting Guidance and Control Network provides both a robust nominal trajectory and the associated closed-loop guidance law.
  arXiv  Detail & Related papers  (2020-08-19T15:22:15Z)
• Single-step deep reinforcement learning for open-loop control of laminar and turbulent flows [0.0]
  This research gauges the ability of deep reinforcement learning (DRL) techniques to assist the optimization and control of fluid mechanical systems. It combines a novel, "degenerate" version of the prototypical policy optimization (PPO) algorithm, that trains a neural network in optimizing the system only once per learning episode.
  arXiv  Detail & Related papers  (2020-06-04T16:11:26Z)
• Adaptive Control and Regret Minimization in Linear Quadratic Gaussian (LQG) Setting [91.43582419264763]
  We propose LqgOpt, a novel reinforcement learning algorithm based on the principle of optimism in the face of uncertainty. LqgOpt efficiently explores the system dynamics, estimates the model parameters up to their confidence interval, and deploys the controller of the most optimistic model.
  arXiv  Detail & Related papers  (2020-03-12T19:56:38Z)

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.