KohlerHECTOR/interpreter-py
Implementation of Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning (Kohler, Delfosse, et. al. 2024).
This tool helps machine learning engineers or researchers simplify complex neural network behaviors into understandable decision trees. It takes a pre-trained expert policy (like a neural network from Stable Baselines3) and an environment as input. The output is a decision tree that mimics the expert's actions, making it easier to analyze and modify. This is particularly useful for those working with reinforcement learning models that need transparent decision-making processes.
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Use this if you need to explain or edit the learned behavior of a complex reinforcement learning agent by converting it into a simpler, human-readable decision tree.
Not ideal if your primary goal is to achieve state-of-the-art performance in reinforcement learning tasks, as the simplified policy might not match the expert's full capability.
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Python
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MIT
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Last pushed
Sep 10, 2024
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