Mojusko/experiment-design-mdp
Python library for adaptive experiment design with state-of-art ML tools
This tool helps scientists, engineers, and researchers efficiently design experiments in complex, sequential environments where actions influence future states. It takes in your experimental setup (environment, feedback model, and desired utility) and outputs an optimal strategy or "policy" for conducting your experiments. This allows practitioners to achieve goals like identifying the best treatment or maximizing information gain with fewer experiments.
Use this if you need to optimize an experimental process where decisions are sequential and the outcomes of your choices affect future experimental options.
Not ideal if your experiments are independent and not influenced by previous outcomes, or if you need a simple, non-adaptive statistical experimental design.
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11
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Language
Python
License
MIT
Category
Last pushed
Jan 27, 2026
Commits (30d)
0
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