A Finite Horizon Markov Decision Process Based Reinforcement Learning Control of a Rapid Thermal Processing system

  title={A Finite Horizon Markov Decision Process Based Reinforcement Learning Control of a Rapid Thermal Processing system},
  author={Darsy John Pradeep and Mathew Mithra Noel},
  journal={Journal of Process Control},

Multi-UAV Cooperative Task Assignment Based on Half Random Q-Learning

Simulation experiments show that compared with Q-learning algorithm and other heuristic algorithms, HR Q- learning algorithm can improve the performance of task execution, including the ability to improve the rationality of task assignment, and increasing the value of gains by 12.12%, which provides a meaningful attempt for UAV task assignment.

Reinforcement Learning based Intelligent Semiconductor Manufacturing Applied to Laser Annealing

- The recent shift in the paradigm of the industrial revolution, i.e., Industry 4.0, has forced industries to reemphasize manufacturing standards to improve the manufacturing and production systems.

Effects of thermal slip and chemical reaction on free convective nanofluid from a horizontal plate embedded in a porous media.

It is found that the velocity and temperature decrease with thermal slip and heat absorption whilst it increases by increasing heat generation and chemical reaction order.



A learning approach of wafer temperature control in a rapid thermal processing system

This paper presents a learning approach for wafer temperature control in a rapid thermal processing system (RTP) without exact information on the dynamics, and it is demonstrated that the proposed method can achieve an accurate output tracking even without an exact RTP model.

Semi-empirical model-based multivariable iterative learning control of an RTP system

Comprehensive study on control system design for a rapid thermal processing (RTP) equipment has been conducted with the purpose to obtain maximum temperature uniformity across the wafer surface,

Reinforcement Learning for Partially Observable Dynamic Processes: Adaptive Dynamic Programming Using Measured Output Data

  • F. LewisK. Vamvoudakis
  • Computer Science
    IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
  • 2011
It is shown that, similar to Q-learning, the new methods have the important advantage that knowledge of the system dynamics is not needed for the implementation of these learning algorithms or for the OPFB control.

Decentralized control of wafer temperature for multizone rapid thermal processing systems

Decentralized control is shown through analysis and experimentation to be an appropriate strategy for wafer temperature control in certain multizone rapid thermal processing (RTP) systems and straightforward nonmodel-based tuning of the controller is enabled due to the simplicity of the decentralized control structure.

Reinforcement learning in multidimensional continuous action spaces

  • Jason PazisM. Lagoudakis
  • Computer Science
    2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)
  • 2011
This paper proposes an effective approach to learning and acting in domains with multidimensional and/or continuous control variables where efficient action selection is embedded in the learning process using a value function over an implied augmented MDP.