tf-agent

Source

• Source kind: github-repository
• URL: https://github.com/karpathy/tf-agent
• Discovery source: https://github.com/karpathy/tf-agent
• License: NOASSERTION
• Distribution policy: public-summary-local-archive-only
• Public mirror status: summary-only
• Content hash: c40d78c43029b64ed5d93c17edae95db0dbc691d9c1dc28bfe3e7a47bd45c66d
• First seen: 2026-05-15
• Last changed: 2026-05-15

Classification

• Primary category: Research automation / agentic science
• Corpus source note: 2026-05-15-karpathy-public-corpus
• Project taxonomy: karpathy-project-taxonomy
• Idea map: karpathy-idea-map
• Topic hub: karpathy-public-work

Summary

tf-agent Some RL agents code for OpenAI gym envs.

What This Teaches

• How Karpathy frames autonomous research loops, experiment iteration, and AI-assisted discovery.
• Useful for designing agent workflows that improve through concrete experiments instead of one-off prompting.

Why It Matters

This is high-priority for Vipin because it informs agent workflows, paper digestion, wiki maintenance, and autonomous research loops.

Repository Snapshot

• Full name: karpathy/tf-agent
• Default branch: master
• HEAD: 245882955d1af95a8d455c7ab83c9cc1b342bb45
• Stars at crawl: 145
• Forks at crawl: 33
• File count: 2
• README path: README.md
• License path: “
• Created: 2016-09-30T08:30:55Z
• Updated: 2026-05-13T16:22:39Z
• Pushed: 2017-07-21T23:23:54Z

Top-Level Structure

• [root]: 2

File Extension Profile

• .md: 1
• .py: 1

Tags / Release-Like Markers

• No git tags found in the shallow local clone.

Sample File Tree

• policy_gradient.py
• README.md

Public Handling Notes

• EXTRACTED: This page records public metadata and a source-grounded summary.
• INFERRED: Full local preservation, when available, is for private/local use unless a license or explicit source policy makes public redistribution safe.
• Do not treat this page as permission to republish unlicensed source text or code wholesale.