README.md

---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: PandaReachDense-v3
      type: PandaReachDense-v3
    metrics:
    - type: mean_reward
      value: -0.22 +/- 0.12
      name: mean_reward
      verified: false
---

# **PPO** Agent playing **PandaReachDense-v3**
This is a trained model of a **PPO** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).

## Usage (with Stable-baselines3)
TODO: Add your code


```python

from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub, package_to_hub
from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize

env_id = "PandaReachDense-v3"
env = gym.make(env_id)
env = make_vec_env(env_id, n_envs=4)
env = VecNormalize(env, training=True, norm_obs=True, norm_reward=True, gamma=0.5, epsilon=1e-10, norm_obs_keys=None)

model =  PPO("MultiInputPolicy", env, verbose=1)
model.learn(1_000_000)

eval_env = DummyVecEnv([lambda: gym.make("PandaReachDense-v3")])
eval_env = VecNormalize.load("vec_normalize.pkl", eval_env)
eval_env.render_mode = "rgb_array"
eval_env.training = False
# reward normalization is not needed at test time
eval_env.norm_reward = False


model = PPO.load("Slay-PandaReachDense-v3")
mean_reward, std_reward = evaluate_policy(model, eval_env)
print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}")
...
```