#@title
library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 290.76 +/- 18.71 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2
{name_of_your_repo}
This is a pre-trained model of a {algo} agent playing {environment} using the stable-baselines3 library.
Usage (with Stable-baselines3)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
pip install stable-baselines3
pip install huggingface_sb3
Then, you can use the model like this:
import gym
from huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy
# Retrieve the model from the hub
## repo_id = id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name})
## filename = name of the model zip file from the repository
checkpoint = load_from_hub(repo_id="{repo_id}", filename="{filename}.zip")
model = PPO.load(checkpoint)
# Evaluate the agent
eval_env = gym.make('{environment}')
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
# Watch the agent play
obs = env.reset()
for i in range(1000):
action, _state = model.predict(obs)
obs, reward, done, info = env.step(action)
env.render()
if done:
obs = env.reset()
env.close()
Evaluation Results
Mean_reward: {your_evaluation_results}