szaguldo-kamaz/FRI-ReinforcementLearning
Fuzzy Rule Interpolation-based Reinforcement Learning
This framework helps researchers and engineers develop reinforcement learning systems where the decision-making rules need to be easily understood by humans. It takes observations from an environment and outputs a set of human-readable fuzzy rules that define how an agent should act, along with the expected 'Q' values for those actions. This is useful for anyone designing autonomous systems who needs transparency in the agent's learned behavior.
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Use this if you need to understand or explain why your reinforcement learning agent makes certain decisions, rather than treating it as a 'black box'.
Not ideal if your primary concern is raw performance or if human interpretability of the learned rules is not a priority.
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9
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Language
MATLAB
License
GPL-3.0
Category
Last pushed
Aug 01, 2022
Commits (30d)
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