Updated model

README.md

@@ -1,47 +1,3 @@
 ---
-tags:
-- deep-reinforcement-learning
-- reinforcement-learning
-- stable-baselines3
+{}
 ---
-
-This is a pre-trained model of a PPO agent playing CartPole-v1 using the [stable-baselines3](https://github.com/DLR-RM/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:
-
-```python
-import gymnasium as 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="sb3/demo-hf-CartPole-v1",
-    filename="ppo-CartPole-v1",
-)
-model = PPO.load(checkpoint)
-
-# Evaluate the agent and watch it
-eval_env = gym.make("CartPole-v1")
-mean_reward, std_reward = evaluate_policy(
-    model, eval_env, render=True, n_eval_episodes=5, deterministic=True, warn=False
-)
-print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
-```
-
-### Evaluation Results
-Mean_reward: 500.0
-
-

ppo-CartPole-v1.zip

@@ -1,3 +1,3 @@
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+size 143103