Interpretable Reinforcement Learning for Robotics and Continuous Control.
CoRR(2023)
摘要
Interpretability in machine learning is critical for the safe deployment of
learned policies across legally-regulated and safety-critical domains. While
gradient-based approaches in reinforcement learning have achieved tremendous
success in learning policies for continuous control problems such as robotics
and autonomous driving, the lack of interpretability is a fundamental barrier
to adoption. We propose Interpretable Continuous Control Trees (ICCTs), a
tree-based model that can be optimized via modern, gradient-based,
reinforcement learning approaches to produce high-performing, interpretable
policies. The key to our approach is a procedure for allowing direct
optimization in a sparse decision-tree-like representation. We validate ICCTs
against baselines across six domains, showing that ICCTs are capable of
learning policies that parity or outperform baselines by up to 33% in
autonomous driving scenarios while achieving a 300x-600x reduction in the
number of parameters against deep learning baselines. We prove that ICCTs can
serve as universal function approximators and display analytically that ICCTs
can be verified in linear time. Furthermore, we deploy ICCTs in two realistic
driving domains, based on interstate Highway-94 and 280 in the US. Finally, we
verify ICCT's utility with end-users and find that ICCTs are rated easier to
simulate, quicker to validate, and more interpretable than neural networks.
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