Reinforcement Learning
stable-baselines3
CartPole-v1
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use ShuzhengTian/Carlo-Reinforce-unit_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use ShuzhengTian/Carlo-Reinforce-unit_4 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="ShuzhengTian/Carlo-Reinforce-unit_4", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Reinforce Agent playing {CartPole-v1}
This is a trained model of a Reinforce agent playing {env_id} . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Usage (with Stable-baselines3)
def load_policy(ckpt_path, s_size, a_size, h_size, device="cpu"):
policy = Policy(s_size, a_size, h_size).to(device)
policy.load_state_dict(torch.load(ckpt_path, map_location=device))
policy.eval()
return policy
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
from inference import load_policy()
...
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Evaluation results
- mean_reward on CartPole-v1self-reported500.0 +/- 0