Application of Deep Reinforcement Learning to Control Drainage in a Lab-Scale Geosystem
Publication: Geo-Congress 2024
ABSTRACT
This paper explores the deployment of deep reinforcement learning (DRL), a subset of machine learning for automated decision-making, in a lab-scale geosystem. The developed lab-scale geosystem is a slope equipped with a pumping system, a rainfall simulator, a microcontroller, and a water level sensor. The main goal of this study is to establish an intelligent control of the groundwater in the slope. The DRL agent, deep Q-network (DQN), was first trained using the virtual environment to reduce the risk of failure and increase safety. Pre-training helps the agent learn the policy and goal of the system before deploying it in the real world. When the pre-training is complete, the agent was fine-tuned in the lab to adjust the DQN hyperparameters. The results showed that the pre-trained and fine-tuned DRL agent can autonomously regulate a pump to control the groundwater in the geosystem.
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REFERENCES
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Information & Authors
Information
Published In
Geo-Congress 2024
Pages: 425 - 435
History
Published online: Feb 22, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Automation and robotics
- Business management
- Computer programming
- Computing in civil engineering
- Decision making
- Drainage
- Engineering fundamentals
- Equipment and machinery
- Geomechanics
- Geotechnical engineering
- Irrigation engineering
- Laboratory tests
- Neural networks
- Practice and Profession
- Pumps
- Slopes
- Systems engineering
- Tests (by type)
- Water and water resources
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