Open AI Gym to play 1v1 Catan against a random bot
Project description
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().
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.
For observation documentation see here.
You can access env.game.state and build your own "observation" (features) vector as well.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file catanatron_gym-3.0.1.tar.gz.
File metadata
- Download URL: catanatron_gym-3.0.1.tar.gz
- Upload date:
- Size: 9.8 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.8.10
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
a1fcfaec804c7cb87b878645beaf5dba6eae2be28f599b57b6ed3a4af53b59f9
|
|
| MD5 |
3b7890bf4463ea089fc636469199f346
|
|
| BLAKE2b-256 |
1758c96c0c7a60e125584ace5f6f59f309f56ddb2c02d0495babc2e4efabd7ec
|
File details
Details for the file catanatron_gym-3.0.1-py3-none-any.whl.
File metadata
- Download URL: catanatron_gym-3.0.1-py3-none-any.whl
- Upload date:
- Size: 10.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.8.10
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
490e89279a5350d407c425bcf6948514782078cf73614fa6f8868b2fe47e1345
|
|
| MD5 |
d8d627ddc3bd03f850ff2b076b00c97f
|
|
| BLAKE2b-256 |
8fc88b4f94b564111cdbac975a1b0880c838e867dfe3810b1e955a9fbb3d7e33
|