active-inference
Skill0 starsChanged 6 months agoNo licenceView on GitHub
Bayesian agent decision-making via the Free Energy Principle. An Active Inference agent maintains a generative model (A/B/C/D matrices) over hidden world states, selects actions by minimizing Expected Free Energy (EFE), and updates its posterior belief via variational inference after each observation. The EFE decomposes into epistemic value (reduce uncertainty about states) and pragmatic value (reach preferred outcomes) — no scalar reward function required. Use when the environment is partially observable, reward design is hard, or principled uncertainty-driven exploration is needed. Complements deep-q-rl (which assumes full observability + known evaluator).
- Installs packages
In this repository219 other files it gives its agents.
- agents/AGENTS.mdAGENTS.md
- AGENTS.mdAGENTS.md
- codex-agents/AGENTS.mdAGENTS.md
- memory-bank/AGENTS.mdAGENTS.md
- copilot-instructions.mdCopilot instructions
- .github/copilot-instructions.mdCopilot instructions
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- The first 60 shown.
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 2 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
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