Metadata-Version: 2.1
Name: catanatron-gym
Version: 3.0.1
Summary: Open AI Gym to play 1v1 Catan against a random bot
Home-page: https://github.com/bcollazo/catanatron
Author: Bryan Collazo
Author-email: bcollazo2010@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: catanatron
Requires-Dist: gym

# catanatron_gym

For reinforcement learning purposes, we provide an Open AI Gym to play 1v1 Catan against a random bot environment. To use:

```
pip install catanatron_gym
```

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

```python
import random
import gym

env = gym.make("catanatron_gym:catanatron-v0")
observation = env.reset()
for _ in range(1000):
  action = random.choice(env.get_valid_actions()) # your agent here (this takes random actions)

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

For `action` documentation see [here](https://catanatron.readthedocs.io/en/latest/catanatron_gym.envs.html#catanatron_gym.envs.catanatron_env.CatanatronEnv.action_space).

For `observation` documentation see [here](https://catanatron.readthedocs.io/en/latest/catanatron_gym.envs.html#catanatron_gym.envs.catanatron_env.CatanatronEnv.observation_space).

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


