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Experimental data-efficient reinforcement learning with an ensemble of surrogate models
1School of Engineering, University of Newcastle, Callaghan, NSW, 2308, Australia.
Summary
This study introduces double surrogate models using symbolic regression to improve reinforcement learning (RL) sample efficiency. This method significantly reduces the need for real-world experimental data by creating accurate synthetic environments for training RL agents.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Robotics
Background:
- Model-based reinforcement learning (MBRL) uses synthetic data to improve sample efficiency.
- Modeling errors in MBRL can lead to significant performance degradation in real-world applications due to discrepancies in learned dynamics.
- Existing methods struggle with model inaccuracies and potential exploitation by agents.
Purpose of the Study:
- To develop a novel ensemble of double surrogate models using symbolic regression for enhanced data-efficient reinforcement learning.
- To uncover fundamental physical principles governing system behavior for improved model interpretability and generalization.
- To mitigate model bias and prevent agents from exploiting inaccuracies in synthetic environments.
Main Methods:
- Ensemble of double surrogate models constructed via symbolic regression.
- Symbolic regression identifies interpretable models reflecting underlying physical principles.
- Reinforcement learning agents interact exclusively within the synthetic surrogate models.
- Double surrogate model structure designed to mitigate bias and improve robustness.
Main Results:
- Achieved comparable training performance to conventional reinforcement learning algorithms in real environments.
- Required less than 1% of the experimental data typically needed for traditional RL methods.
- Demonstrated enhanced model interpretability and generalization capabilities through symbolic regression.
- Successfully reduced the need for extensive real-world data collection.
Conclusions:
- The proposed double surrogate model approach significantly enhances sample efficiency in model-based reinforcement learning.
- Symbolic regression provides interpretable and generalizable models, crucial for real-world applications.
- This method offers a robust and data-efficient alternative to conventional reinforcement learning techniques, particularly in complex systems.
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