Build Simple Games for AI Agents: A Practical Guide

Updated 10 November 2026 · Standard formula, shown below

Simple games are a useful way to test whether an AI agent can observe a situation, choose an action, and improve through feedback. You’ll build a small turn-based game with clear rules and a score, then make it easy to play repeatedly so you can compare strategies. No machine-learning framework is required to start; a little programming experience and a language such as Python are enough.

What You’ll Need

A computer with Python 3 installed, or another language you can run locally.
A code editor and a terminal or command prompt.
A small game idea that can be represented with a few variables and a short list of legal actions.
Optional: a notebook or spreadsheet for recording results across repeated games.

Step-by-Step

Choose a tiny, testable game. Start with a game that finishes quickly and has no hidden physical components. For example, make a number-guessing game: the agent chooses a number from 1 to 5, and receives one point if it matches a number selected by the game. Another option is a grid game where two players take turns placing marks. Avoid complicated rules, long campaigns, and graphics until the basic game works.
Write down the rules before coding. Specify how a game begins, which player acts, what actions are legal, how the game ends, and how rewards are calculated. Decide how ties work. Keep the rules deterministic at first, or clearly identify any randomness. A written ruleset prevents you from changing the objective accidentally while testing.
Represent the game state with ordinary data. For the number-guessing example, the state could contain the round number and any score so far. For a grid game, use a list or array for the board and a value for the current player. Include everything needed to determine legal actions and the result. Do not store information the agent should not be able to observe in the state you give it.
Separate the game engine from the agent. The engine should handle rules; the agent should select an action. Create functions with clear jobs, such as new_game() to create a starting state, legal_actions(state) to list valid moves, step(state, action) to apply a move, and observation(state, player) to return what a player can see. Have step return the next state, whether the game is over, and the reward or score. This separation lets you replace a strategy without rewriting the rules.
Make invalid actions explicit. Decide what happens if an agent returns an unavailable move. For development, raise a clear error or return a defined penalty; do not silently substitute a different action. Check action types and ranges before applying them. This catches bugs early and ensures every agent faces the same rules.
Build a simple baseline agent. Start with a random agent that chooses uniformly from the legal actions. It gives you a reference point and checks that the action interface is usable. Then add a basic rule-based agent, such as one that chooses the move with the highest immediate reward. Keep each agent’s input and output format identical.
Run one complete game from start to finish. Write a loop that asks the current agent for an action, passes it to the engine, and continues until the terminal condition is reached. Print the state, action, and result while debugging. Ensure the loop always ends: add a maximum-turn limit as a safeguard, and report when that limit is reached instead of treating it as a normal win.
Test the rules independently. Check that a new game has the correct starting state, only legal actions are offered, valid actions change the state as expected, and terminal states cannot continue. Test edge cases such as a full board, a tie, or the final round. Use fixed random seeds where possible so a failing run can be reproduced.
Measure agents over many games. A single result is noisy, particularly when the game includes randomness. Run agents against the random baseline over a fixed number of games, alternating which player goes first. Record wins, losses, draws, average reward, and games that hit the turn limit. Report both the number of games and the scoring method so the result is interpretable.
Improve one thing at a time. Change a strategy, reward, or game rule only after saving a baseline result. Run the same evaluation again and compare results. If an agent learns from experience, separate training games from evaluation games and test on fresh seeds. Otherwise, repeated tuning against one fixed set can make the agent look better than it is.
Document the interface. Explain the observation format, legal action format, turn order, reward scale, and end conditions. Include a short example game and instructions for running tests. A clear interface makes it possible to add new agents without requiring each author to study every line of the engine.

Common Mistakes

Ambiguous rules: If a rule can be interpreted in two ways, different agents may effectively play different games. Specify tie handling, turn order, and reward timing.
Leaking hidden information: Passing the full internal state to an agent can reveal secrets the game is meant to hide. Give it only the information available to that player.
Changing rules during evaluation: Even a small change can make old scores incomparable. Version the rules or restart the evaluation after a change.
Testing only one game: Randomness and starting position can dominate a single outcome. Use repeated games and alternate player order.
Rewarding the wrong behavior: An agent may exploit a reward loophole instead of achieving the intended goal. Review its actions and define rewards around the actual objective.
Ignoring invalid moves or endless games: Silent repairs hide interface bugs, while unbounded loops waste time. Reject invalid moves clearly and set a reasonable turn cap.

Tips

Keep the first version small enough that you can explain every state variable and rule.
Use the same game engine for every strategy so comparisons are fair.
Log a compact record of each game: seed, starting player, actions, final outcome, and any errors.
Separate the score an agent receives during play from the metrics you use to evaluate it. A reward can guide behavior, while win rate or average score measures performance.
Make the game configurable where useful, such as board size or round count, but keep one standard configuration for comparisons.
When sharing the game, include a baseline agent and automated tests so users can verify their setup before building a new strategy.

Troubleshooting

The game does not finish: Print the turn number and state on every move. Check that each action advances the state and that the terminal condition is reachable. Add a turn cap and inspect games that reach it.

The agent makes illegal moves: Log the observation, returned action, and legal-action list. Confirm that both sides use the same data types and action numbering. Keep validation in the engine, even if the agent is expected to behave correctly.

Scores vary widely: Run more games, record random seeds, and balance starting positions. Compare averages and win rates rather than relying on a memorable match.

A stronger-looking agent performs worse later: Check for accidental changes to rules or evaluation settings. Keep training and evaluation runs separate, and test on new seeds or opponents.

Results differ between machines: Check language and dependency versions, random-number seeding, and any reliance on unordered data. Log the settings needed to reproduce a run.

Frequently Asked Questions

Do I need machine learning to make an agent game?
No. A random or rule-based agent is enough to test the game interface. Add learning only when you have a clear reason and a reliable baseline.

Which programming language should I use?
Use a language you can run and debug comfortably. Python is approachable for small experiments, but the important part is a consistent state-and-action interface.

How many games should I run when comparing agents?
There is no universal number. Start with enough repeated games to see whether results are stable, then increase the sample if outcomes vary substantially. Report the count and setup.

Should the agent receive the whole game state?
Only if every part of that state is meant to be observable. For games with hidden information, provide a player-specific observation instead.

Can I add graphics or multiplayer later?
Yes. Keep the rules engine separate from display and networking code. That lets you change how people interact with the game without changing its core rules.

How do I know whether an agent is actually improving?
Compare it with a fixed baseline over repeated games, using the same rules and balanced starting positions. Evaluate on games or random seeds not used to tune the agent.

Method: the standard formula for this calculation, rounded as each result states. See how the calculators work or report an error.