README.md

Catanatron Gym

For reinforcement learning purposes, we provide an Open AI Gym environment. To use:

pip install catanatron_gym

Make your training loop, ensuring to respect env.get_valid_actions().

import random
import gymnasium as gym

env = gym.make("catanatron_gym:catanatron-v1")
observation, info = env.reset()
for _ in range(1000):
  # your agent here (this takes random actions)
  action = random.choice(env.unwrapped.get_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.game.state and build your own "observation" (features) vector as well.

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 as gym
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

def mask_fn(env) -> np.ndarray:
    valid_actions = env.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 = gym.make("catanatron_gym:catanatron-v1")
env = ActionMasker(env, mask_fn)  # Wrap to enable masking
model = MaskablePPO(MaskableActorCriticPolicy, env, verbose=1)

# Train
model.learn(total_timesteps=1_000_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.

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


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 = gym.make(
    "catanatron_gym:catanatron-v1",
    config={
        "map_type": "MINI",
        "vps_to_win": 6,
        "enemies": [WeightedRandomPlayer(Color.RED), WeightedRandomPlayer(Color.ORANGE)],
        "reward_function": my_reward_function,
        "representation": "mixed",
    },
)