Offered By: IBMSkillsNetwork
Adaptive Agentic RAG via Reinforcement Learning in LangGraph
Build an adaptive Agentic RAG system with LangGraph that uses reinforcement learning through Proximal Policy Optimization (PPO) to learn when to retrieve, rewrite, or answer. In this guided project, you will create a research-paper knowledge base, build a baseline RAG pipeline for comparison, model adaptive routing as a reinforcement learning problem, train a PPO policy, and integrate the learned policy into a LangGraph workflow. You will compare fixed RAG with adaptive Agentic RAG to understand how learned routing can improve retrieval decisions and workflow flexibility.
Continue readingGuided Project
Artificial Intelligence
At a Glance
Build an adaptive Agentic RAG system with LangGraph that uses reinforcement learning through Proximal Policy Optimization (PPO) to learn when to retrieve, rewrite, or answer. In this guided project, you will create a research-paper knowledge base, build a baseline RAG pipeline for comparison, model adaptive routing as a reinforcement learning problem, train a PPO policy, and integrate the learned policy into a LangGraph workflow. You will compare fixed RAG with adaptive Agentic RAG to understand how learned routing can improve retrieval decisions and workflow flexibility.
Who Is It For
This project is designed for learners with foundational Python and machine learning knowledge who want to explore Agentic AI, Retrieval-Augmented Generation, and reinforcement learning. It is especially relevant for AI practitioners, data scientists, machine learning engineers, and software developers who want to understand how reinforcement learning can be used to control the behavior of an AI workflow rather than directly train a language model. Prior experience with RAG is helpful, but no previous experience with PPO or LangGraph is required.
What You’ll Learn
By the end of this project, you will understand how a fixed RAG workflow can be extended into an adaptive Agentic RAG system whose routing decisions are learned through reinforcement learning. You will see how retrieval, query rewriting, PPO-based decision making, and LangGraph can work together within one end-to-end workflow.
- Build a research-paper knowledge base and perform semantic document retrieval.
- Implement a baseline RAG pipeline for comparison with Agentic RAG.
- Represent RAG routing as a reinforcement learning environment with observations, actions, and rewards.
- Train a PPO policy to choose among retrieve, rewrite, and answer actions.
- Build a stateful Agentic RAG workflow with LangGraph.
- Compare fixed RAG with PPO-guided Agentic RAG and examine their routing behavior.
What You’ll Need
You should be comfortable writing basic Python code and have a foundational understanding of machine learning concepts. Familiarity with large language models, embeddings, or Retrieval-Augmented Generation is helpful but not required. No prior experience with reinforcement learning, PPO, ChromaDB, or LangGraph is required. The necessary concepts are introduced as part of the project, and the required libraries can be installed directly in the IBM Skills Network Labs environment.
Certificate
No Certificate Offered
Estimated Effort
60 Minutes
Level
Advanced
Skills You Will Learn
Retrieval-Augmented Generation (RAG), Reinforcement Learning, Proximal Policy Optimization (PPO), AI Agents, LangGraph, ChromaDB
Language
English
Course Code
GPXX0Q0LEN
Released
August 21, 2026