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Keywords: reinforcement learning
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Proceedings Papers

Proc. ASME. IDETC-CIE2021, Volume 3A: 47th Design Automation Conference (DAC), V03AT03A022, August 17–19, 2021
Paper No: DETC2021-70425
... Proceedings of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC-CIE2021 August 17-19, 2021, Virtual, Online DETC2021-70425 EVALUATING HEURISTICS IN ENGINEERING DESIGN: A REINFORCEMENT LEARNING APPROACH Karim...
Proceedings Papers

Proc. ASME. IDETC-CIE2020, Volume 11A: 46th Design Automation Conference (DAC), V11AT11A007, August 17–19, 2020
Paper No: DETC2020-22519
...Abstract Abstract Particle swarm optimization (PSO) method is a well-known optimization algorithm, which shows good performance in solving different optimization problems. However, PSO usually suffers from slow convergence. In this paper, a reinforcement learning method is used to enhance PSO...
Proceedings Papers

Proc. ASME. IDETC-CIE2020, Volume 11A: 46th Design Automation Conference (DAC), V11AT11A038, August 17–19, 2020
Paper No: DETC2020-22019
...1 Assistant Professor, zequnw@mtu.edu, Corresponding Author 2 Graduate Student, narendra@mtu.edu RELIABILITY-BASED REINFORCEMENT LEARNING UNDER UNCERTAINTY Zequn Wang1 and Narendra Patwardhan2 Department of Mechanical Engineering-Engineering Mechanics, Michigan Technological University, Houghton...
Proceedings Papers

Proc. ASME. IDETC-CIE2020, Volume 11B: 46th Design Automation Conference (DAC), V11BT11A036, August 17–19, 2020
Paper No: DETC2020-22014
...Abstract Abstract Model-free reinforcement learning based methods such as Proximal Policy Optimization, or Q-learning typically require thousands of interactions with the environment to approximate the optimal controller which may not always be feasible in robotics due to safety and time...
Proceedings Papers

Proc. ASME. IDETC-CIE2019, Volume 1: 39th Computers and Information in Engineering Conference, V001T02A009, August 18–21, 2019
Paper No: DETC2019-97711
...Abstract Abstract This work presents a Procedural Content Generation (PCG) method based on a Neural Network Reinforcement Learning (RL) approach that generates new environments for Virtual Reality (VR) learning applications. The primary objective of PCG methods is to algorithmically generate...
Topics: Virtual reality
Proceedings Papers

Proc. ASME. IDETC-CIE2019, Volume 2A: 45th Design Automation Conference, V02AT03A024, August 18–21, 2019
Paper No: DETC2019-97190
... savings. To this end, this paper aims to maximize the arbitrage value of electricity through the optimal design of control strategies for DERs. Formulated as an arbitrage maximization problem using design optimization, and solved using reinforcement learning, the proposed approach is applied towards...
Proceedings Papers

Proc. ASME. IDETC-CIE2007, Volume 6: 33rd Design Automation Conference, Parts A and B, 91-100, September 4–7, 2007
Paper No: DETC2007-34718
...) learning algorithms sequential decision-making under uncertainty simulation-based optimization reinforcement learning internal combustion engine calibration fuel economy con dom styl basi to l time eng eve spec app igni resp Key algo sim com Proceedings of the ASME 2007 International Design...
Proceedings Papers

Proc. ASME. IDETC-CIE2005, Volume 5a: 17th International Conference on Design Theory and Methodology, 117-130, September 24–28, 2005
Paper No: DETC2005-85051
... Behavior Design Petri-Net Reinforcement Learning Heuristic Search Proceedings of IDETC 05 ASME 2005 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, ach, California, USA, September 24 to September 25, 2005 Proceedings of IDETC/CIE 2005 ASME...