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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 agent receives an observation, chooses an action from a declared action space, and gets the next observation, reward, episode status, and optional information. For one agent, a custom Gymnasium environment is the clearest starting point. For games with several players, use PettingZoo: its AEC API models sequential turns and its Parallel API models simultaneous choices.
A five-by-five grid with one player, one target, four moves, a bounded board, and a binary success reward is enough to exercise the complete loop. Define the state, observation, actions, transitions, reward, and ending rules before adding graphics. Once that contract is reliable, rendering, opponents, richer maps, and learning algorithms become replaceable layers instead of sources of ambiguity.
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An AI-playable game needs an agent-facing contract that is precise enough for a program to use without looking at the screen. At every decision point, the environment must answer five questions: what can the agent observe, which actions are legal, what state follows an action, what reward was earned, and whether the episode has ended.
| Contract element | What to define | Example in a grid game |
|---|---|---|
| State | Everything required to simulate the rules, including information hidden from the agent | Agent coordinates, target coordinates, board limits, step count |
| Observation | The information delivered to the agent at a decision point | A dictionary containing agent and target coordinates |
| Action space | The complete set of choices the agent may submit | Four discrete movement actions |
| Transition | How an action changes the state | Move one cell, clipped at an edge |
| Reward | The numerical signal associated with the transition | Binary reward when the target is reached |
| Episode status | Whether success, failure, or a time limit ended the run | Terminate on target; truncate at a step limit |
The distinction between state and observation matters. The simulator may need a complete board, opponent plans, or a hidden card deck, while the agent may receive only a partial view. Whatever you declare as an observation space must match the values your environment actually returns. Gymnasium’s custom-environment tutorial demonstrates this principle with agent and target coordinates in a dictionary observation.
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Keep the first version inspectable by hand. A small board lets you predict every transition, verify edge behavior, and understand why a reward appeared. A complicated ruleset can hide interface mistakes behind apparently intelligent behavior.
How should you design the first game?
Choose a game whose complete state can fit in your head and whose objective can be stated in one sentence. A bounded grid-navigation task is a useful template because it has a finite action set, visible transitions, and an obvious success condition.
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Choose a compact objective
Start with one controllable agent and one target. The objective can be “reach the target before the episode limit.” Avoid adding inventory, combat, procedural maps, or multiple opponents until the basic environment can reset, accept actions, and finish an episode reliably.
Write the transition rules before coding
Specify the initial positions, coordinate convention, movement directions, board boundaries, and collision behavior in plain language. For example, actions 0 through 3 can mean up, right, down, and left. If a move would leave the board, clip the coordinate to the nearest valid cell. Decide whether an invalid action is rejected, treated as a no-op, or converted to a penalty; use one policy consistently.
Separate success from time limits
Success is a game outcome; a time limit is an administrative cutoff. Keep those concepts separate in the episode status returned by the environment. An agent that reaches the target on the final allowed move should be marked successful rather than merely timed out.
How do you create a custom Gymnasium environment?
For a single-agent game, implement the standard environment contract: declare spaces, initialize an episode in reset, apply one action in step, and return the updated observation, reward, termination status, truncation status, and auxiliary information. The following skeleton mirrors the documented grid-world structure while leaving room for your own rules.
- Declare the spaces. Use a discrete action space for the four moves. Use a structured dictionary observation with bounded integer coordinate arrays for the agent and target.
- Initialize reproducibly. Reset the episode state and pass the framework’s seed through the reset method when you need repeatable runs.
- Apply exactly one action. Convert the action into a movement vector, update the position, and enforce board boundaries.
- Calculate the outcome. Compare the new position with the target, assign the reward, and distinguish success from a step-limit truncation.
- Return a matching observation. Every value returned by
resetandstepmust fit the declared observation space.
class GridWorldEnv(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.int32),
'target': spaces.Box(0, size - 1, shape=(2,), dtype=np.int32)
})
def _observation(self):
return {
'agent': self.agent.copy(),
'target': self.target.copy()
}
def reset(self, seed=None, options=None):
super().reset(seed=seed)
self.agent = np.array([0, 0], dtype=np.int32)
self.target = np.array([self.size - 1, self.size - 1], dtype=np.int32)
self.steps = 0
return self._observation(), {}
def step(self, action):
moves = {
0: np.array([0, 1]),
1: np.array([1, 0]),
2: np.array([0, -1]),
3: np.array([-1, 0])
}
self.agent = np.clip(
self.agent + moves[int(action)], 0, self.size - 1
).astype(np.int32)
self.steps += 1
terminated = bool(np.array_equal(self.agent, self.target))
reward = 1 if terminated else 0
truncated = bool(self.steps >= self.max_steps and not terminated)
return self._observation(), reward, terminated, truncated, {}This is an illustrative implementation pattern, not a complete package. Add the imports and adapt the class to the Gymnasium version in your project. The important behavior is the contract: one action produces one transition, and the returned values describe the state immediately after that transition.
What should the agent loop look like?
The control loop should be simple enough to print and inspect before you train anything. Reset once, repeatedly choose an action, call step, and stop when either termination or truncation is true.
observation, info = env.reset(seed=7)
done = False
while not done:
action = env.action_space.sample()
observation, reward, terminated, truncated, info = env.step(action)
done = terminated or truncated
env.close()A random policy is useful here because it exercises the interface without introducing a learning problem. Log the observation, action, reward, and both end flags for a few episodes. If the episode never ends, ends immediately, returns an out-of-range observation, or reports a reward inconsistent with the board, fix the environment before changing the agent.
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How should observations, rewards, and actions be designed?
Design each element around the decision the agent must make, not around what is convenient to draw on screen.
Observations should contain decision-relevant information
Coordinate observations are compact and easy to validate. If the agent must avoid walls, include wall information or encode the board in another declared structure. If the target is hidden, do not leak its coordinates merely because the simulator stores them. Conversely, do not omit information required to distinguish two situations that demand different actions.
Actions should be complete and unambiguous
A discrete action should have one meaning in every state. Define what happens at boundaries and when an action is not legal. A no-op at an edge is often easier to debug than silently changing the action into a different direction, but either choice can work if it is documented and tested.
Rewards should reflect the objective
The documented grid-world pattern uses a sparse binary reward: reaching the target produces a reward and other moves do not. That is a reasonable first test because it makes success unmistakable. If you later add step costs, danger penalties, or intermediate goals, write down how those signals relate to the actual objective so the agent cannot earn a high score by exploiting an unintended shortcut.
How do termination and truncation differ?
Termination means the game reached a natural end state such as success or an unavoidable loss. Truncation means the episode stopped for an external limit such as a maximum number of moves. Return both statuses instead of collapsing them into one vague “done” value, because training and evaluation code may treat a natural terminal state differently from a cutoff.
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For a target game, set terminated when the agent reaches the target. Set truncated when the step counter reaches the configured limit and the target was not reached. If both conditions become true on the same move, preserve the successful terminal result and do not label it only as a timeout.
When should you add rendering?
Add rendering after the rules and agent-facing data work. Rendering is a debugging and demonstration layer; it does not have to be the observation consumed by the policy. Gymnasium’s tutorial illustrates both a human display mode and an RGB-array mode, so you can inspect the same environment interactively or capture frames for a program.
Human rendering
Use a human-readable view to check coordinate orientation, target placement, boundary clipping, and end-state behavior. A visual mismatch often reveals an indexing error that would be difficult to spot from numbers alone.
RGB-array rendering
An RGB-array mode is useful when another component needs image frames, such as a video recorder or a vision-based agent. Keep the structured observation available as well; forcing every agent to infer simple coordinates from pixels makes the first prototype harder to test.
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Keep rendering optional
Training and automated tests should be able to run without opening a window. Make rendering conditional on a selected mode and ensure that stepping the environment does not depend on a human watching it.
How do you make a turn-based game for multiple agents?
Choose the multi-agent API according to turn timing. PettingZoo’s Agent Environment Cycle (AEC) API is designed for sequential turns, while its Parallel API is designed for games in which agents choose actions in the same round. The distinction affects observation delivery, action submission, rewards, and how you represent the current player.
Use PettingZoo AEC for sequential turns
In an AEC environment, the environment identifies the currently selected agent. The basic interaction pattern resets the environment, iterates through agents, reads the selected agent’s observation and status, submits that agent’s action, and advances the cycle.
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, reward)
env.step(action)The current agent can be a human, a scripted opponent, or a learned policy. That makes AEC suitable for board games, card turns, and any ruleset where one participant acts before the next participant receives a turn. The official PettingZoo basic-usage guide documents the agent_iter, current-agent observation, reward, and stepping pattern.
Use the Parallel API for simultaneous choices
In a simultaneous game, collect one action for each live agent in the round, then advance the environment with the action collection. This matches games where all players choose before any move is resolved. Not every PettingZoo environment has a parallel counterpart, so check the API offered by the environment you are using or implement the appropriate interface yourself.
Represent inactive and finished agents explicitly
Decide what an agent receives after it has been eliminated or after the episode has ended. In a sequential interface, finished agents may require a null action while the cycle advances. In a parallel interface, define which agents remain active in each action collection. Inconsistent handling here is a common cause of hangs and mismatched reward logs.
Which framework should you choose?
Pick the framework that matches the number of agents, timing of decisions, and type of game. The official projects describe different scopes rather than a universal performance ranking.
| Use case | API or project | Decision pattern | Typical observation style | What the documentation establishes |
|---|---|---|---|---|
| One custom agent | Gymnasium custom environment | One action at each step | Structured values such as agent and target coordinates; visual modes can be added | Farama presents Gymnasium as a standard single-agent reinforcement-learning environment API |
| Several agents taking turns | PettingZoo AEC | One selected agent acts, then the environment advances | Observation and status for the current agent | PettingZoo documents an Agent Environment Cycle API for sequential interaction |
| Several agents choosing together | PettingZoo Parallel | Actions for active agents are submitted for the same round | Per-agent observations and action collections | PettingZoo provides a Parallel API, although not every environment has a parallel version |
| Existing Atari 2600 game | Arcade Learning Environment | Agent interacts with an emulated arcade title | Interface-dependent, including visual game inputs | ALE separates Atari emulation from agent design and documents Gymnasium, Python, and C++ interfaces |
Farama’s project overview at farama.org/projects is useful when deciding whether your task belongs in a single-agent, multi-agent, or existing-environment ecosystem. The Gymnasium external-environments catalog can help identify third-party environments when you do not need to write the game yourself.
How do you test a game before training an agent?
Test the environment as ordinary software first. Learning cannot compensate for a broken transition function or an observation that lies about the state.
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- Reset test: reset repeatedly and verify that the initial state obeys the rules and that a supplied seed produces repeatable initialization when your implementation supports seeding.
- Space test: check every returned observation against the declared observation space and every submitted action against the action space.
- Boundary test: place the agent at each edge and confirm that movement never produces an out-of-bounds coordinate.
- Reward test: move directly onto the target and verify the reward and terminal status on that exact transition.
- Limit test: avoid the target until the maximum step count and confirm truncation rather than accidental success.
- Multi-agent turn test: log the selected agent, action, reward, and status through a complete AEC cycle, or verify that every active agent receives one action in a parallel round.
- Repeatability test: run the same action sequence from the same reset seed and compare observations, rewards, and end flags.
- Rendering test: compare a rendered frame with the structured state so that coordinates, orientation, and target placement agree.
Use short, deterministic action scripts before random play. A script that moves right until an edge, then moves up, exposes more boundary and coordinate errors than an opaque training run.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the most common implementation failures?
The observation space does not match returned data
A dictionary may declare integer coordinate arrays but return lists, floating-point values, or coordinates outside the board. Construct observations in one helper function and validate that helper after every transition.
The game never ends
An unreachable target, a missing step limit, or a termination flag that is never set can leave an agent loop running forever. Prove that every legal action sequence either reaches a natural end or eventually triggers truncation.
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The visual display becomes the only interface
A screenshot can look correct while the policy receives stale or incomplete information. Treat rendering and observation generation as separate functions and test the structured data directly.
Illegal actions silently change the rules
If an action outside the declared space is accepted, a policy can learn behavior that no valid agent could reproduce. Validate actions at the boundary and choose a documented response such as rejection, a no-op, or an explicit penalty.
Rewards encourage the wrong behavior
A step penalty can make an agent prefer a risky shortcut; a shaping bonus can make it collect points without completing the objective. Trace a few complete episodes and calculate the score by hand before adding reward terms.
Turn order is ambiguous
In a multi-agent game, the environment must make it clear who acts now, which agents are still active, and when rewards are assigned. Use AEC for explicit sequential turns or the Parallel API for same-round choices rather than inventing a hybrid loop with undocumented timing.
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Extend one dimension at a time and keep the original contract understandable.
Add map structure
Introduce walls or hazards only after the open grid passes all tests. Add the new map data to the state, decide whether it belongs in each agent’s observation, and test movement around every obstacle.
Add partial information
Hide selected state deliberately rather than accidentally. For each agent, document what is visible, what remains private, and whether observations change when another player acts.
Add opponents
Begin with a deterministic scripted opponent so that failures are reproducible. In a sequential game, give each opponent its own policy at its turn; in a simultaneous game, collect all policies’ actions before resolving the round.
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Record episode return, length, termination reason, and the action trace. These logs explain whether an agent is solving the objective, exploiting a reward loophole, or simply timing out.
Keep the interface stable
Changing the meaning of an action or the shape of an observation can invalidate an existing policy. When a rule change is necessary, treat it as a new environment version or make the change explicit in configuration and evaluation logs.
Can an existing arcade game be used instead of building one?
Yes. If your goal is to develop an agent for an existing Atari 2600 title rather than design new rules, the Arcade Learning Environment provides the emulation layer and documents Gymnasium, Python, and C++ interfaces at ale.farama.org. You still need to understand the observation format, action meanings, reward behavior, and episode-ending conditions exposed by the selected environment.
Building a tiny custom game remains valuable when you are learning agent loops, testing a policy architecture, or needing rules that an existing arcade environment does not provide. An existing title saves game-design work but gives you less control over the state representation and mechanics.
Frequently Asked Questions
Can one game provide both structured data and image observations?
Yes. Keep a structured observation for agents that can use coordinates or symbolic features, and expose an RGB rendering mode for vision-based agents or recording. Declare every observation format explicitly and verify that each returned value matches its corresponding space.
How can I make hidden information fair in a multi-agent game?
Store the complete board in the environment state, then construct a separate observation for each agent that omits private information. Test two agents with identical public situations to ensure that neither receives another player’s hidden state accidentally.
Can a human take over for one player while other agents run policies?
Yes. In a sequential API, choose the action source based on the current agent: read keyboard input for the human-controlled player and call that player’s policy for others. Keep both sources subject to the same action-space validation and turn timing.
How should I compare two agents on my game?
Use the same environment configuration, reset-seed protocol, episode limit, and starting-state rules for both. Record returns, episode lengths, termination reasons, and action traces so a higher score can be checked for genuine objective completion rather than a scoring exploit.
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