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04/26/2023
Reinforcement Learning with Partial Parametric Model Knowledge
We adapt reinforcement learning (RL) methods for continuous control to b...
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04/07/2023
A modular framework for stabilizing deep reinforcement learning control
We propose a framework for the design of feedback controllers that combi...
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01/18/2023
Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system
The carbon-capturing process with the aid of CO2 removal technology (CDR...
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09/22/2022
Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey
Over the last ten years, we have seen a significant increase in industri...
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09/19/2022
Meta-Reinforcement Learning for Adaptive Control of Second Order Systems
Meta-learning is a branch of machine learning which aims to synthesize d...
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03/17/2022
Meta Reinforcement Learning for Adaptive Control: An Offline Approach
Meta-learning is a branch of machine learning which trains neural networ...
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11/13/2021
Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuning
Deep reinforcement learning (RL) is an optimization-driven framework for...
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03/26/2021
Almost Surely Stable Deep Dynamics
We introduce a method for learning provably stable deep neural network b...
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03/25/2021
A Meta-Reinforcement Learning Approach to Process Control
Meta-learning is a branch of machine learning which aims to quickly adap...
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05/10/2020
Optimal PID and Antiwindup Control Design as a Reinforcement Learning Problem
Deep reinforcement learning (DRL) has seen several successful applicatio...
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05/10/2020