huggingbutt 0.0.3

Client library to download and publish environments, agents on the huggingbutt.com hub

HuggingButt

Client library to download and publish reinforcement learning environments/agents on the huggingbutt.com hub

Installation

This project only supports Python 3.10, going up to version 3.10.12 at most, this is due to the base dependency 'mlagents-envs' limiting the Python version. I'll fix this in the future.

Create a new python environment using anaconda/miniconda.

conda create -n hb python==3.10.12

activate the new python environment.

conda activate hb

install huggingbutt from pypi

pip install huggingbutt

or from source code

git clone https://github.com/huggingbutt/huggingbutt.git
cd huggingbutt
python -m pip install .

If there is no error message printed during the installation, congratulations, you have successfully installed this package. Next, you need to apply an access token from the official website https://huggingbutt.com.

Register an account and login, just do as shown in the image below.

Click new token button generate a new token. This access token is mainly used to restrict the download times of each user, as the server cost is relatively high.

Congratulations, you now have an access token!

Just put the generated token in the task code and you're gooooood to go.

Here is a simple training code:

from huggingbutt import Env, set_access_token
from stable_baselines3.ppo import PPO
# your generated access token
ACCESS_TOKEN="YOUR_TOKEN"

if __name__ == '__main__':
    set_access_token(ACCESS_TOKEN)
    env = Env.get('huggingbutt/juggle', 'mac', startup_args=['--time_scale', '10'])
    model = PPO("MlpPolicy", env, verbose=1)
    model.learn(total_timesteps=2500)
    model.save("ppo_juggle.model")
    env.close()

Inference:

from huggingbutt import Agent, Env, set_access_token
# your generated access token
ACCESS_TOKEN="YOUR_TOKEN"

if __name__ == '__main__':
    set_access_token(ACCESS_TOKEN)
    env = Env.get('huggingbutt/juggle', 'mac', startup_args=['--time_scale', '1'])
    agent = Agent.get(20, env)
    obs = env.reset()
    for i in range(1000):
        act, _status_ = agent.predict(obs)
        obs, reward, done, info = env.step(act)
        if done:
            obs = env.reset()
    env.close()

todo

1. Support more types learning environment, such as native game wrapped by python, pygame, class gym...
2. Develop a framework, user can customize the observation, action and reward of the environment developed under this framework. We hope this makes it easier for everyone to iterate the environment's agent.
3. There are still many ideas, I will add them later when I think about them...