Build a Reinforcement Learning Powered Agent that Learns to Retrieve Relevant Long-Term Memories for Accurate LLM Question Answering

A tutorial shows how to train a PPO-based retrieval agent for long-term memory in LLM QA.

It uses OpenAI embeddings to encode memories and queries, then learns to pick the best memory in a custom Gymnasium setup.

The approach is evaluated against a similarity-search baseline and aims to improve answer accuracy when relevant facts are stored.

The setup uses stable-baselines3, a synthetic memory dataset, and models such as text-embedding-3-small and gpt-4o-mini.