Gymnasium Interface

For reinforcement learning purposes, we provide an Open AI Gym / Gymnasium environment. To use, in the root of the catanatron repository:

pip install -e .[gym]

Make your training loop, ensuring to respect info['valid_actions'] :

import random
import gymnasium
import catanatron.gym

env = gymnasium.make("catanatron/Catanatron-v0")
observation, info = env.reset()
for _ in range(1000):
    # your agent here (this takes random actions)
    action = random.choice(info["valid_actions"])

    observation, reward, terminated, truncated, info = env.step(action)
    done = terminated or truncated
    if done:
        observation, info = env.reset()
env.close()

For action documentation see here.

For observation documentation see here.

You can access env.unwrapped.game.state and build your own "observation" (features) vector as well.

For evaluation and using your model in the simulator for testing / benchmarking you might want to checkout: https://github.com/bcollazo/catanatron/blob/master/catanatron_experimental/catanatron_experimental/machine_learning/players/reinforcement.py

Stable-Baselines3 Example

Catanatron works well with SB3, and better with the Maskable models of the SB3 Contrib repo. Here a small example of how it may work.

import gymnasium
import numpy as np
from sb3_contrib.common.maskable.policies import MaskableActorCriticPolicy
from sb3_contrib.common.wrappers import ActionMasker
from sb3_contrib.ppo_mask import MaskablePPO
import catanatron.gym


def mask_fn(env) -> np.ndarray:
    valid_actions = env.unwrapped.get_valid_actions()
    mask = np.zeros(env.action_space.n, dtype=np.float32)
    mask[valid_actions] = 1

    return np.array([bool(i) for i in mask])


# Init Environment and Model
env = gymnasium.make("catanatron/Catanatron-v0")
env = ActionMasker(env, mask_fn)  # Wrap to enable masking
model = MaskablePPO(MaskableActorCriticPolicy, env, verbose=1)

# Train
model.learn(total_timesteps=10_000)

Configuration

You can also configure what map to use, how many vps to win, among other variables in the environment, with the config keyword argument. See source for details.

import gymnasium
from catanatron import Color
from catanatron.players.weighted_random import WeightedRandomPlayer
import catanatron.gym


def my_reward_function(game, p0_color):
    winning_color = game.winning_color()
    if p0_color == winning_color:
        return 100
    elif winning_color is None:
        return 0
    else:
        return -100


# 3-player catan on a "Mini" map (7 tiles) until 6 points.
env = gymnasium.make(
    "catanatron.gym/Catanatron-v0",
    config={
        "map_type": "MINI",
        "vps_to_win": 6,
        "enemies": [
            WeightedRandomPlayer(Color.RED),
            WeightedRandomPlayer(Color.ORANGE),
        ],
        "reward_function": my_reward_function,
        "representation": "mixed",
    },
)