CMU 11-768: AI Agents – Lectures and Slides

Carnegie Mellon 11-768: AI Agents

Guide

All

01-Course Overview - What Is an Agent
01-Course Overview - What Is an Agent
02-Agent Capabilities 1 - Tool Use
02-Agent Capabilities 1 - Tool Use
03-Agent Capabilities 2 - Context Management for Long-Context Agents
03-Agent Capabilities 2 - Context Management for Long-Context Agents
04-Agent Capabilities 3 - Skills and Memory
04-Agent Capabilities 3 - Skills and Memory
05-Agent Capabilities 4 - Planning, Task Decomposition, and Multi-Agent Coordination
05-Agent Capabilities 4 - Planning, Task Decomposition, and Multi-Agent Coordination
06-Domains 1 - Coding Agents
06-Domains 1 - Coding Agents
07-Domains 2 - Computer Use Agents
07-Domains 2 - Computer Use Agents
08-Training 1 - Supervised Fine-Tuning (SFT)
08-Training 1 - Supervised Fine-Tuning (SFT)
09-Training 2 - Reinforcement Learning Basics
09-Training 2 - Reinforcement Learning Basics
10-Domains 3 - Deep Research Agents
10-Domains 3 - Deep Research Agents
11-Training 3 - Advanced RL Algorithms
11-Training 3 - Advanced RL Algorithms
12-Training 4 - RL Systems
12-Training 4 - RL Systems
13-Safety 1 - Agent Safety
13-Safety 1 - Agent Safety
14-Frameworks 1 - OpenHands
14-Frameworks 1 - OpenHands
15-Frameworks 2 - Orchestration and Monitoring
16-Agents and the Future of Work
17-Interaction 1 - Multi-Agent Interaction

Winda

Introduction to reinforcement learning for agents, bridging from SFT to policy gradient methods, illustrated through a number-guessing task and covering rewards for trajectories.

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