Trending repositories: reinforcement-learning
9 tracked repositories tagged with reinforcement-learning, ordered by stars. Use the topic filters below to narrow further.
9 of 9 repositories
Developer-Y/cs-video-courses
List of Computer Science courses with video lectures.
AI summary: A heavily curated index of free, university-level computer science video lectures covering every major discipline.
82,947learningunslothai/unsloth
Unsloth is a local UI for training and running Kimi K3, Gemma 4, Qwen3.6, DeepSeek-V4, GLM and other models.
AI summary: A local UI and framework for efficiently training and running large language models.
69,627ai-mlPythonApache-2.0rohitg00/ai-engineering-from-scratch
Learn it. Build it. Ship it for others.
AI summary: A comprehensive, code-first curriculum to master AI engineering and build production-ready LLM applications.
46,045learningPythonMITbojieli/ai-agent-book
《深入理解 AI Agent：设计原理与工程实践》（李博杰 著）开源主仓库：全书正文、编译版 PDF 与按章配套代码
AI summary: An open-source, comprehensive book on building and understanding AI agents.
33,975learningPythonApache-2.0microsoft/agent-lightning
The absolute trainer to light up AI agents.
AI summary: A multi-framework trainer that optimizes AI agents using reinforcement learning and automatic prompt optimization with zero code changes.
17,457ai-mlPythonMITHenryNdubuaku/maths-cs-ai-compendium
Become a cracked AI/ML researcher/engineer with this unconventional textbook covering maths, computing, and ML with intuition.
AI summary: A comprehensive compendium covering Mathematics, Computer Science, and Artificial Intelligence concepts.
7,254learningTypeScriptApache-2.0yaojingang/yao-meta-skill
YAO = Yielding AI Outcomes. A rigorous engineering, evaluation, governance, and portability system for reusable agent skills.
AI summary: A meta-learning framework for training AI agents to rapidly acquire new skills.
2,338ai-mlPythonMITray-r-ren/agent-apprenticeship
The living ecosystem where AI agents complete tasks through workflow loops, improve through iterative execution, are evaluated by mentor agents or humans in the loop, and turn completed work into reusable work experience and data to improve future agents.
AI summary: Ecosystem framework for capturing and utilizing AI agent work traces for iterative learning.
1,342ai-mlPythonMITray-r-ren/agent-apprenticeship
The living ecosystem where AI agents complete tasks through workflow loops, improve through iterative execution, are evaluated by mentor agents or humans in the loop, and turn completed work into reusable work experience and data to improve future agents.
AI summary: Ecosystem framework for capturing and utilizing AI agent work traces for iterative learning.
1,339ai-mlPythonMIT