Introduction
Reinforcement learning (RL) has become a staple of modern artificial intelligence, powering everything from game‑playing bots to autonomous vehicles. Yet the majority of tutorials and research papers focus on large‑scale, data‑intensive environments that require heavy computational resources. For many developers, educators, and hobbyists, the barrier to entry is the sheer complexity of setting up a learning loop, designing reward structures, and tuning hyper‑parameters. A more approachable route is to build a miniature RL environment that captures the core ideas—state observation, action selection, reward feedback, and policy improvement—while remaining lightweight enough to run on a laptop or even a browser.
In this post we walk through the construction of a compact, grid‑world RL system that demonstrates three intertwined agent roles: an Action Agent that proposes low‑level moves, a Tool Agent that evaluates the consequences of those moves, and a Supervisor that orchestrates the overall strategy. By layering decision‑making and embedding local feedback loops, the agents learn to navigate a maze, avoid obstacles, and cooperate when multiple agents share the same space. The resulting architecture is intentionally modular: each component can be swapped out for a more sophisticated model, allowing the reader to experiment with neural policies, Monte‑Carlo tree search, or symbolic planners without rewriting the entire framework.
The tutorial is written in Python and relies only on the standard library and NumPy, which means you can run it in any environment that supports Python 3.8+. Throughout the code snippets we emphasize clarity over performance, so you can easily trace how the state propagates through the system and how the reward signals shape behavior. By the end of the article you will have a working RL pipeline that you can extend, visualize, and share with colleagues.
Main Content
Designing the Grid World
The foundation of any RL experiment is the environment. A grid world offers a clean, discrete state space that is easy to visualize and manipulate. We represent the world as a two‑dimensional NumPy array where each cell holds an integer code: 0 for free space, 1 for walls, 2 for goals, and 3 for hazards. The agent’s position is stored as a tuple of coordinates. Movement is restricted to the four cardinal directions, and each step incurs a small negative reward to encourage efficiency.
To keep the environment lightweight, we avoid using external simulation libraries. Instead, we implement a simple step function that takes an action, updates the position, checks for collisions, and returns the new state, reward, and a boolean indicating whether the episode has terminated. Because the grid is small (e.g., 10x10), the computational cost of this function is negligible, allowing us to focus on the learning logic.
Agent Roles and Hierarchy
A single monolithic agent can be difficult to debug and extend. By decomposing the decision process into three roles we gain modularity and clarity.
- Action Agent – This is the lowest‑level component. It receives the current state and outputs a probability distribution over the four possible moves. In the baseline implementation we use a simple softmax over a linear transformation of the flattened state, but the architecture is agnostic to the underlying policy model.
- Tool Agent – Acting as a critic, the Tool Agent evaluates the immediate outcome of each candidate action. It simulates the environment step for each possible move, calculates the resulting reward, and ranks the actions. This local feedback loop provides the Action Agent with a richer signal than a raw reward, enabling faster convergence.
- Supervisor – The top‑level orchestrator maintains a high‑level plan. It can impose constraints (e.g., avoid revisiting the same cell), aggregate long‑term rewards, and coordinate multiple agents by sharing a global map of visited cells. The Supervisor can also trigger exploration bonuses when agents encounter novel states.
The hierarchy is implemented through simple function calls: the Supervisor calls the Tool Agent to evaluate actions, which in turn queries the Action Agent for a proposal. This layered approach mirrors biological systems where sensory input is processed locally before being integrated into a global strategy.
Local Feedback Mechanism
Traditional RL relies on sparse, delayed rewards that can make learning slow. To mitigate this, we introduce a local feedback mechanism that operates at each step. The Tool Agent simulates the outcome of every possible action and assigns a provisional reward based on immediate consequences: moving into a wall yields a large penalty, stepping onto a goal gives a positive bonus, and landing on a hazard incurs a moderate penalty. These provisional rewards are then fed back to the Action Agent as a target distribution.
Mathematically, we compute a soft target using a temperature‑scaled softmax over the provisional rewards. The Action Agent’s loss is the cross‑entropy between its predicted action distribution and this target. This approach is akin to imitation learning where the critic guides the policy, but here the critic is derived from the environment itself rather than from expert demonstrations.
Adaptive Decision‑Making
Even with local feedback, an agent can still get stuck in suboptimal loops, especially in environments with multiple goals or dynamic obstacles. To add adaptability, we incorporate a simple contextual bandit mechanism. The Supervisor monitors the agent’s recent trajectory and, if it detects a plateau in cumulative reward, it injects a random exploration bonus into the provisional rewards. This bonus is proportional to the novelty of the visited state, encouraging the agent to try new paths.
We also implement a decay schedule for the exploration bonus, ensuring that the agent gradually shifts from exploration to exploitation as it learns the optimal policy. The decay is controlled by a hyper‑parameter that can be tuned based on the size of the grid or the complexity of the task.
Coordinating Multiple Agents
A single agent can solve many grid‑world puzzles, but real‑world scenarios often involve collaboration. To demonstrate multi‑agent coordination, we extend the Supervisor to maintain a shared occupancy map. Each agent reports its intended next position, and the Supervisor resolves conflicts by assigning priority based on proximity to the goal or by random tie‑breaking.
The Tool Agent for each agent receives not only the local environment but also the occupancy map, allowing it to anticipate collisions. When two agents plan to move into the same cell, the Tool Agent can propose alternative actions that avoid deadlock. This simple coordination protocol scales linearly with the number of agents and can be replaced with more sophisticated negotiation algorithms if desired.
Putting It All Together
The final training loop ties all components together. For each episode, the Supervisor resets the environment and initializes the occupancy map. At every step, the Action Agent proposes an action, the Tool Agent evaluates it, and the Supervisor updates the global plan. The environment returns the new state and reward, which the Tool Agent uses to refine its provisional rewards. After a fixed number of episodes, we evaluate the learned policy by measuring success rate and average steps to goal.
The code is intentionally verbose to aid comprehension. You can replace the linear policy with a neural network, swap the softmax target for a Q‑learning update, or even port the entire framework to a web interface using Flask or Streamlit. The modular design ensures that each change is isolated and testable.
Conclusion
Building a miniature reinforcement learning environment from scratch offers a hands‑on understanding of the core mechanics that drive modern AI agents. By decomposing the decision process into Action, Tool, and Supervisor roles, we create a clear hierarchy that mirrors real‑world systems. Local feedback accelerates learning, while adaptive exploration guarantees that agents do not settle prematurely. Extending the architecture to multiple agents demonstrates how simple coordination protocols can scale to collaborative tasks.
The framework presented here is deliberately lightweight yet extensible. It serves as a sandbox for experimenting with new policy architectures, reward shaping techniques, and multi‑agent strategies. Whether you are a student learning RL fundamentals, a researcher prototyping a new algorithm, or a hobbyist building a game bot, this tutorial provides a solid foundation that you can build upon.
Call to Action
If you found this tutorial helpful, consider experimenting with the code on your own projects. Try swapping the linear policy for a small neural network, or add stochastic obstacles to the grid to see how the agents adapt. Share your results on GitHub or in a forum; the community thrives on open collaboration. For deeper dives, explore advanced topics such as hierarchical reinforcement learning, curriculum learning, or distributed training. Happy coding, and may your agents navigate the grid with grace and efficiency!