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

Most AI systems are great at learning patterns, but they’re surprisingly bad at holding onto skills. We train a model, move on, and then end up retraining it later for something it already knew how to do. 

Humans don’t do that. When we learn a skill, we keep it, reuse it, and adapt it to new situations. That’s procedural memory at work.

This guide is about building an agent that behaves more like that. You’ll learn how an agent can pick up skills, save them as reusable neural modules, pull out the right skill when a similar problem shows up, and even combine multiple skills to solve new tasks. 

Instead of one massive model that keeps growing, the focus is on systems that get better over time by building on what they already know. Everything here is hands-on and code-focused, based on real design choices rather than abstract theory.

Skill Representation, Storage, and Retrieval

Skills in a procedural memory agent are represented as independent neural modules rather than being embedded inside a single, growing model. This design allows learned behaviors to persist, remain reusable, and evolve independently over time. 

Each skill is stored together with structured metadata, such as task context, training conditions, performance metrics, and usage history, which helps the agent determine when a skill is relevant.

• When facing a new state, the agent generates a state embedding and compares it with stored skill embeddings using cosine similarity to measure relevance.
• Similarity-based retrieval allows the agent to select or rank previously learned skills instead of relearning behavior from scratch.
• Usage statistics such as frequency, recency, and success rate are updated continuously to reinforce reliable skills and deprioritize ineffective ones.

Environment Design for Procedural Memory

To evaluate procedural memory in a clear and interpretable way,  construct a simple, task-driven environment where the agent learns to pick up a key, open a door, and reach a goal. These tasks are intentionally minimal yet structured, allowing us to observe how learning unfolds across episodes. 

Early on, the agent relies on primitive actions such as movement and interaction. Over time, these actions begin to form higher-level behaviors that can be reused in different situations.

 This environment acts as a controlled playground for testing procedural memory, making it easy to see how stored skills improve efficiency, consistency, and overall task performance.

• As training progresses, these primitives naturally evolve into higher-level skills, such as “navigate to key” or “unlock door,” which can be reused across episodes.
• This setup acts as a testing ground for the procedural memory system, making it possible to track when skills are learned, stored, retrieved, and reused.
• Because the environment is simple and interpretable, improvements in behavior—such as faster completion or fewer errors—are easy to observe and directly attribute to skill reuse.

Learning Embeddings and Skill Extraction

Here, the goal is to turn the agent’s raw experience into something it can actually reuse. Instead of treating each interaction in isolation, we build embeddings that capture the context of a state–action sequence—what was happening and why a particular action worked. 

This makes it possible to compare different skills in a meaningful way. From there, we extract skills from successful trajectories, pulling out patterns that consistently lead to good outcomes. 

As the code runs, you can see a clear shift: early exploration looks random, but over time it starts producing structured knowledge. The agent begins to recognize familiar situations, recall what worked before, and apply that knowledge later, rather than starting from scratch each time.

• Context-aware embeddings enable skill comparison
• Skills are extracted from successful trajectories
• Exploration evolves into structured, reusable knowledge

Balancing Skill Reuse and Exploration During Training

In this phase, we define how the agent decides between reusing an existing skill and falling back to primitive actions when faced with uncertainty. Rather than always exploiting what it already knows, the agent maintains a balance between exploration and reuse, allowing it to discover new behaviors while still benefiting from previously learned skills. 

Training is carried out over multiple episodes, during which we track how the skill library evolves—recording when new skills are added, how often existing skills are selected, and how successful they are over time.

As training progresses, clear patterns begin to emerge. Skills that consistently perform well are reused more frequently, while ineffective ones are gradually deprioritized. This shift leads to shorter episodes, smoother behavior, and higher overall rewards. 

The results highlight how controlled skill reuse not only improves efficiency but also stabilizes learning as the agent gains experience.

• Clear rules govern when to reuse skills versus explore with primitives
• Skill usage frequency and success rates are tracked across episodes
• High-performing skills naturally become preferred over time
• Skill reuse reduces episode length and improves reward outcomes

Evaluating Learning and Procedural Memory Growth

In the final stage, we run the full training loop and observe the procedural memory system in action. Learned skills are printed and inspected, allowing us to see how raw behaviors have been transformed into structured, reusable capabilities. 

Alongside this, we can plot key behavior statistics to visualize how the agent’s performance changes over time. Reward trends clearly show improvement across episodes, while the growth of the skill library highlights when new skills are discovered and when existing ones are reused more effectively.

These visualizations complete the lifecycle of procedural memory formation. They confirm that, with experience, the agent shifts from trial-and-error behavior to more deliberate and efficient decision-making. 

• Run the full training loop to observe how the procedural memory system operates end to end.
• Print and inspect learned skills to understand how primitive behaviors evolve into reusable modules.
• Track and visualize reward trends across episodes to measure learning progress and stability.
• Plot the growth of the skill library to see when new skills are created and how reuse increases over time.
• Analyze behavior statistics to confirm reductions in episode length and improved decision efficiency.

Bottom Line

Procedural memory emerges naturally when an agent learns to recognize and extract skills from its own successful experiences. Over time, these skills take on structure, gaining metadata, embeddings, and usage patterns that make them easier to retrieve and reuse in new situations. 

What stands out is how quickly this process becomes effective: even within a small environment and using simple heuristics, the agent begins to show meaningful learning dynamics. Skills are no longer isolated behaviors but internal competencies that improve with experience. 

This gives us a concrete and intuitive understanding of how agents can move beyond repeated trial and error, gradually developing reusable knowledge that supports smarter, more efficient behavior over time.