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Build the game as a small, explicit state machine rather than as a visual app first. The environment owns the rules and hidden state; an AI agent receives an observation, chooses a legal action, and gets the next observation, a reward, an end-of-episode status, and optional information. For a first project, use a bounded grid world with one agent, one target, and four movement actions. Implement the rules in a Gymnasium environment, test the reset-and-step loop, and add rendering only after the contract works. If several agents must play, use PettingZoo: its Agent Environment Cycle (AEC) API models sequential turns, while its Parallel API models simultaneous choices.
This approach gives you a game an agent can actually control, evaluate, and replay. It also keeps game logic independent from graphics, so you can later swap a rule-based bot for a learned policy without rewriting the game.
What does an AI-playable game need?
An AI-playable game needs a precise agent-facing contract. At every decision point, the environment must know the current state, expose an observation, accept an action from a declared action space, apply the rules, and return the resulting observation and outcome.
- State: Everything required to simulate the game, such as positions, inventory, health, score, turn number, and remaining moves.
- Observation: The portion of that state the agent is allowed to see. It can be structured values such as coordinates, an image, or both.
- Action: A value the agent can submit, such as “move north” or “play card 3.” The environment should define what happens when an action is illegal.
- Reward: A numeric signal describing the immediate result of the action. It may be sparse, such as a reward only when the target is reached.
- Termination status: Whether the task ended because the objective was achieved or a failure condition occurred.
- Truncation status: Whether the run stopped for an external limit, such as a maximum number of turns.
- Info: Optional diagnostic data that helps a human or evaluator without becoming part of the learning signal.
Gymnasium’s custom-environment tutorial demonstrates this pattern with a grid world in which the observation contains agent and target coordinates, the action is one of four moves, and reaching the target ends the episode. See the official custom-environment tutorial for the framework’s complete example.
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How do I choose a first game?
Choose a game whose complete state and legal actions fit on one page. A small grid-navigation task is a strong starting point because you can inspect every transition by hand and tell immediately whether the agent won.
A useful starter design
- A square board with fixed dimensions.
- One controllable agent and one target cell.
- Four discrete actions: up, down, left, and right.
- A reset that places the two objects in valid, non-overlapping cells.
- A success condition that ends the episode when the agent reaches the target.
- A turn limit to prevent an agent from wandering forever.
Write these rules in plain language before writing code. Decide whether an agent may stand still, whether it can move through obstacles, how ties are resolved, and whether touching the target is enough to win. Ambiguous rules produce ambiguous training data and make failures difficult to diagnose.
How should I separate game state from the agent’s observation?
Keep simulation state and visible observation separate. The game may track more information than the agent receives; separating the two lets you add hidden information later without changing the rule engine.
| Element | Example in a grid game | Design question |
|---|---|---|
| Internal state | Agent position, target position, obstacles, turn count, random seed | What must the simulator know to apply the next rule? |
| Observation | A dictionary containing agent and target coordinates | What information is available to the policy at this turn? |
| Action | An integer representing one of four directions | Which values are legal, and what does each mean? |
| Reward | Binary success reward when the target is reached | What immediate signal reflects progress toward the objective? |
| Info | Optional distance, collision, or debug flag | What should be visible to diagnostics but excluded from learning? |
The declared observation space must match the values returned by the environment. If the space says coordinates are bounded integers but the implementation returns a different shape, type, or range, wrappers and agents can fail even when the game appears to run.
How do I define actions and invalid moves?
Use a small, discrete action space while the rules are still changing. Map each integer to one operation and document the mapping in code so a policy and a human debugger interpret actions identically.
- Choose a stable action order, such as up, right, down, left.
- Declare the number of actions in the environment’s action space.
- Apply exactly one action per step.
- Define edge behavior. A simple grid can clip movement at the boundary, leaving the agent in the nearest valid cell.
- Define behavior for malformed actions. Reject them clearly during development rather than silently applying a random move.
- Keep action effects deterministic when possible; deterministic transitions make unit tests and replay easier.
Clipping at a boundary is different from allowing an action to pass through a wall. Pick one rule and test it explicitly. If an invalid action should consume a turn, state that in the rules; if it should be ignored, return an informative diagnostic so the agent developer can find the mistake.
How do I implement a custom Gymnasium environment?
For a single-agent game, Gymnasium provides the standard environment shape: define spaces and game state, implement reset and step, and optionally implement rendering. The following compact skeleton shows the responsibilities; adapt dimensions, rewards, and rules to your game.
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- Declare the spaces and metadata. Use a discrete action space for the four directions and a structured observation space for the two coordinates.
- Initialize state in the constructor. Store board size and any fixed rule parameters. Do not start an episode by hiding initialization inside an unrelated method.
- Implement reset. Reinitialize the random generator and positions when requested, then return the first observation and an info dictionary.
- Implement step. Translate the action, update the state, calculate reward, set termination or truncation, and return the five expected values.
- Run a short episode manually. Confirm that reset produces a valid observation and that repeated steps eventually terminate or truncate.
import gymnasium as gym
from gymnasium import spaces
import numpy as np
class GridChase(gym.Env):
def __init__(self, size=5, max_steps=50):
self.size = size
self.max_steps = max_steps
self.action_space = spaces.Discrete(4)
self.observation_space = spaces.Dict({
"agent": spaces.Box(0, size - 1, shape=(2,), dtype=np.int64),
"target": spaces.Box(0, size - 1, shape=(2,), dtype=np.int64),
})
self._steps = 0
self._agent = None
self._target = None
def _observation(self):
return {"agent": self._agent.copy(),
"target": self._target.copy()}
def reset(self, *, seed=None, options=None):
super().reset(seed=seed)
self._steps = 0
self._agent = self.np_random.integers(0, self.size, size=2,
dtype=np.int64)
self._target = self.np_random.integers(0, self.size, size=2,
dtype=np.int64)
while np.array_equal(self._agent, self._target):
self._target = self.np_random.integers(0, self.size, size=2,
dtype=np.int64)
return self._observation(), {}
def step(self, action):
moves = {0: np.array([-1, 0]), 1: np.array([0, 1]),
2: np.array([1, 0]), 3: np.array([0, -1])}
if not self.action_space.contains(action):
raise ValueError("action is outside the declared action space")
self._agent = np.clip(self._agent + moves[int(action)],
0, self.size - 1)
self._steps += 1
terminated = np.array_equal(self._agent, self._target)
truncated = self._steps >= self.max_steps and not terminated
reward = 1.0 if terminated else 0.0
return self._observation(), reward, terminated, truncated, {}The code uses a structured dictionary observation like the documented grid-world pattern. It clips movement to the board and gives a sparse binary reward for success. It is intentionally small: add obstacles, multiple targets, or shaped rewards only after this loop is reliable.
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What should reset and step return?
reset starts a fresh episode and returns an initial observation plus an information dictionary. step returns the updated observation, reward, a termination flag, a truncation flag, and an information dictionary. The agent loop must handle both end flags rather than treating every stop as a win or every stop as a failure.
observation, info = env.reset(seed=7)
for turn in range(100):
action = env.action_space.sample() # replace with your agent
observation, reward, terminated, truncated, info = env.step(action)
if terminated or truncated:
breakA success termination means the game’s objective was reached. A truncation means the run was cut short by a limit or another outside condition. Keeping those meanings distinct lets evaluation report “won,” “lost,” and “timed out” separately.
How should I design rewards and end conditions?
Make the objective and episode endings explicit before tuning the reward. In a reach-the-target game, a binary reward on success and termination at the target are easy to interpret. A turn limit supplies a separate truncation condition.
Reward choices
- Sparse reward: Give a non-zero value only for a win. This mirrors the simple documented grid-world example and keeps the objective unambiguous.
- Progress reward: Add a small signal for reducing distance or completing a subtask. Use this only when the extra signal does not encourage behavior that conflicts with winning.
- Penalty for illegal play: Apply a clearly documented penalty or leave the state unchanged. Do not let an accidental penalty become the main objective.
Ending choices
- Terminate on success.
- Terminate on a defined failure, such as losing all health.
- Truncate after a maximum number of steps or turns.
- Return the final observation and status on the same step that causes the ending condition.
Write tests for each ending path. An agent should never need to infer from a zero reward whether it won, lost, or merely ran out of time.
When should I add rendering?
Add rendering after the observation-action contract works. Rendering is a human inspection tool; it does not need to be the agent’s only input. Gymnasium’s tutorial demonstrates both a human mode and an RGB-array mode, allowing you to watch play or capture frames while retaining structured observations.
Useful rendering modes
- Human: Draw the board in a window or terminal for interactive debugging.
- RGB array: Return image data for notebooks, videos, or an agent that is intentionally trained from pixels.
- No rendering: Run fast automated tests and training without creating visual output.
Keep drawing code out of transition logic. If a rendering bug can change the game state, a policy may learn artifacts of the display instead of the rules. Render the current state after a transition and close resources cleanly when the environment is finished.
Which framework fits my game?
Choose by agent count and turn timing rather than by a presumed performance ranking. Gymnasium is aimed at single-agent environments; PettingZoo is designed for multi-agent environments; the Arcade Learning Environment (ALE) targets existing Atari 2600 games.
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|---|---|---|
| One agent in a custom game | Gymnasium | One observation, one action, one transition at a time |
| Several agents taking sequential turns | PettingZoo AEC API | The environment identifies the current agent, which submits its action before play advances |
| Several agents choosing in the same round | PettingZoo Parallel API | The environment receives an action for each participating agent in that round |
| Existing Atari 2600 title | Arcade Learning Environment | Agent development is separated from the console emulation details |
Farama lists Gymnasium among its projects and describes it as a standard API for single-agent reinforcement-learning environments; see Farama’s projects page. PettingZoo documents both multi-agent interaction styles at its official site. ALE provides Gymnasium, Python, and C++ interfaces in its official documentation. Gymnasium also maintains an external-environments catalog for third-party environments.
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How do I make a turn-based game for multiple agents?
Use a sequential API when only one player acts at a time. PettingZoo’s AEC model exposes the currently selected agent, its observation and reward, and the action that advances the game. This maps naturally to chess-like turns, card games, and board games.
- List every agent and give each a stable identifier.
- Define the turn order and the conditions that skip, eliminate, or reintroduce an agent.
- At each turn, read the selected agent’s observation, reward, termination status, and truncation status.
- Submit an action for that agent, or submit the API’s required no-op representation when that agent is already done.
- Advance until all agents are finished or the episode is truncated.
A minimal control loop follows the documented AEC idea:
env.reset()
for agent in env.agent_iter():
observation, reward, termination, truncation, info = env.last()
if termination or truncation:
action = None
else:
action = policy(agent, observation)
env.step(action)For simultaneous games, use PettingZoo’s Parallel API instead. Collect one action per active agent for the round, submit them together, then process the returned observations, rewards, and status values. Do not force a simultaneous game into a sequential loop unless the rule set explicitly says that the order matters.
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How do I test an environment before training an agent?
Test the rules independently of the policy. A random policy can exercise transitions, but it cannot prove that the reward or end condition is correct.
- Reset validity: Every reset returns values inside the declared observation space.
- Action validity: Legal actions produce a transition; malformed actions fail clearly during development.
- Boundary behavior: Moving beyond each edge follows the documented clipping or collision rule.
- Win path: A short, known action sequence reaches the target and sets termination.
- Timeout path: The maximum-step rule sets truncation without falsely reporting a win.
- Reward alignment: The reward corresponds to the state after the action, not the state before it.
- Reproducibility: A reset with the same supported seed produces the same starting setup and replayable transitions.
- Observation consistency: Returned coordinates, images, and status values describe the same underlying state.
- Multi-agent turns: Only the selected agent can act in an AEC game, and simultaneous actions are collected together in a Parallel game.
Log a short episode as a table of turn number, acting agent, observation, action, reward, and end flags. A human-readable trace often exposes an off-by-one turn or stale observation faster than a training curve.
What observation format should an agent receive?
Use structured observations when the game’s important variables are naturally symbolic, and pixels when visual perception is part of the task. A coordinate dictionary is easier to inspect and usually simpler to debug than an image of the same board.
Structured observations
Return coordinates, discrete statuses, counters, or small arrays with declared bounds. This format makes it straightforward to check whether an observation is legal and to write deterministic tests. It is appropriate for a rules-first prototype in which perception is not the challenge.
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Pixel observations
Return RGB arrays when the agent should learn from what a human sees, or when visual input is an intentional part of the game. Keep the rendering pipeline stable: identical game states should produce equivalent frames, and overlays should not reveal information the rules say is hidden.
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You can maintain structured observations for a policy while also exposing RGB rendering for humans. Treat the two outputs as separate interfaces and test that they remain synchronized after every action.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What commonly breaks, and how do I fix it?
The agent receives an impossible observation
Check the observation-space declaration against the actual type, shape, dtype, and bounds returned by reset and step. Add an assertion immediately after each call during development.
The episode never ends
Verify that the success comparison uses the updated state and that a finite turn limit sets truncation. A target that can be unreachable without a timeout will create infinite rollouts.
The agent learns to exploit a loophole
Inspect every action that produces reward. An unintended reward for bumping into a wall, repeating a move, or triggering a rendering event can become easier than the stated objective. Remove the loophole or revise the rule and reward together.
Players act in the wrong order
In a sequential game, log the selected agent before and after every action. If the game rules require simultaneous choices, switch to a Parallel API instead of adding ad-hoc buffering to an AEC loop.
Visual play disagrees with logged state
Render from the same canonical state used for the observation. Do not keep a second, independently updated copy of positions or scores.
Training changes after a restart
Control seeding at reset, record the environment configuration, and save the action trace for a failing episode. Replaying the trace against the same configuration distinguishes a stochastic rule from a state-management bug.
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- Write the board, actors, legal actions, objective, failure rules, and turn limit in a short specification.
- Implement a pure transition function or small state-update method and test it with hand-written cases.
- Wrap the rules in a Gymnasium environment for one agent, declaring observation and action spaces.
- Make
resetandsteppass a complete episode, including both success and timeout. - Add deterministic traces and boundary tests before training.
- Add human rendering and, if needed, RGB-array output.
- Move to PettingZoo AEC for sequential multi-agent turns or Parallel for simultaneous actions.
- Only then add obstacles, inventory, combat, hidden information, richer rewards, or learned policies.
This progression separates three problems that are often confused: whether the rules are correct, whether the agent can observe and act through the API, and whether a particular learning method can solve the task.
How can I tell whether the design is ready for an agent?
A game is ready when an external loop can play it without knowing implementation details. The loop should be able to reset, inspect the declared observation, choose an allowed action, receive a consistent transition, detect success or failure, and start another episode.
- The environment contract is documented next to the code.
- Every returned value has a stable type and meaning.
- Success, failure, and time-limit endings are distinguishable.
- The game can run without a window.
- A human can inspect a replay through logs or rendering.
- For multi-agent play, the API matches the actual turn structure.
Once those checks pass, the same game can support a hand-written policy, a random baseline, or a learning agent. The environment—not the training algorithm—remains the source of truth for rules and outcomes.
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Can I build an AI-playable game without graphics?
Yes. A game can run entirely from structured observations and actions, with rendering omitted or added later for debugging. This is often the fastest way to validate transitions, rewards, and episode endings.
How do I keep hidden information out of a multi-agent observation?
Construct each player’s observation from that player’s permitted view instead of returning the full simulator state. Test observations from every seat, especially after actions that change private cards, inventory, or fog-of-war areas.
Should a game use a fixed random seed in production?
Use fixed seeds for repeatable tests and saved bug reports, then vary seeds when measuring a policy across episodes. Record the seed and configuration with each evaluation so results can be replayed.
Can an agent play a game that has continuous controls?
Yes, but declare a continuous action space and validate ranges, precision, and clipping explicitly. Start with discrete controls when possible; discretization makes a first environment easier to inspect and test.
How do I package an environment for someone else to run?
Document the observation and action spaces, reset and step return values, rendering modes, and required configuration in the project README. Include a short random-policy script and at least one deterministic replay so another developer can verify the installation.
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