
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).

## Usage (with Stable-baselines3)

```python
import os

import gymnasium as gym
import panda_gym

from huggingface_sb3 import load_from_hub, package_to_hub

from stable_baselines3 import A2C
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
from stable_baselines3.common.env_util import make_vec_env

from huggingface_hub import notebook_login
```

**Environment**

```python
env_id = "PandaReachDense-v3"

# Create the env
env = gym.make(env_id)
```

**Model**

```python
model = A2C(policy = "MultiInputPolicy",
            env = env,
            learning_rate = 0.0001,
            n_steps = 10,
            verbose=1)
```
{"library_name": "stable-baselines3", "tags": ["PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaReachDense-v3", "type": "PandaReachDense-v3"}, "metrics": [{"type": "mean_reward", "value": "-0.14 +/- 0.09", "name": "mean_reward", "verified": false}]}]}]}
stable-baselines3 PandaReachDense-v3 deep-reinforcement-learning reinforcement-learning model-index region:us