A novel implicit hybrid machine learning model and its application for reinforcement learning
- University of Utah, Salt Lake City, UT (United States); OSTI
- University of Utah, Salt Lake City, UT (United States)
A novel methodology to develop implicit hybrid models is presented. PyTorch is used to integrate physics-based equations with machine learning models. Automatic differentiation of the hybrid model is leveraged to solve the implicit equations. Iterative solving enables gradient based updates to the machine learning model. The novel methodology is compared to an explicit hybrid approach on a continuously stirred tank reactor (CSTR). The novel method results in a lower modelling error. Both hybrid models effectively train with noisy data. To test the implicit hybrid model, it is employed as a reinforcement learning (RL) training model. The RL algorithm trained on the hybrid model outperforms real time optimization of the CSTR and performs nearly as well as RL trained directly on the CSTR and a traditional gradient based approach. Training RL directly on the CSTR requires over 60,000 system interactions compared to 6000 historical data points for hybrid model development.
- Research Organization:
- University of Utah, Salt Lake City, UT (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE)
- Grant/Contract Number:
- EE0007712
- OSTI ID:
- 1977010
- Journal Information:
- Computers and Chemical Engineering, Journal Name: Computers and Chemical Engineering Journal Issue: C Vol. 155; ISSN 0098-1354
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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