---
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: -0.17 +/- 0.09
      name: mean_reward
      verified: false
---

# **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
env_id = "PandaReachDense-v3"
env = gym.make(env_id)
s_size = env.observation_space.shape
a_size = env.action_space
env = make_vec_env(env_id, n_envs=4)
env = VecNormalize(venv=env, norm_obs=True, norm_reward=True, clip_obs=10)
model = A2C(policy="MultiInputPolicy", env=env, verbose=1)
model.learn(1_000_000)
model.save("a2c-PandaReachDense-v3")
env.save("vec_normalize.pkl")
from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
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
eval_env.norm_reward = False
model = A2C.load("a2c-PandaReachDense-v3")
mean_reward, std_reward = evaluate_policy(model, eval_env)
print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}")

...
```