A Coding Guide to Build a Procedural Memory Agent That Learns, Stores, Retrieves, and Reuses Skills as Neural Modules Over Time

This tutorial introduces a framework for developing an intelligent agent capable of forming procedural memory by learning and reusing skills as neural modules through environmental interactions. These neural modules store action sequences, contextual embeddings, and are retrieved based on similarity to current situations, enabling the agent to progressively shift from primitive exploration to efficient behavior by leveraging a growing library of learned skills. The approach emphasizes modularity and similarity-based retrieval, facilitating scalable skill acquisition and reuse over multiple episodes. The technical implementation involves defining skills as neural modules with attributes such as preconditions, action sequences, and embeddings, which are stored in a skill library. As