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---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- reinforcement-learning
- stable-baselines3
- a2c
- deep-rl
- panda-gym
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: 0.00 +/- 0.00 # 请根据你之前的 print 结果修改这里
name: mean_reward
---
# A2C Agent playing PandaReachDense-v3
This is a trained model of an **A2C** agent playing **PandaReachDense-v3** using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library and the [panda-gym](https://github.com/qgallouedec/panda-gym) environment.
## Usage (with huggingface_sb3)
To use this model, you need to install the following dependencies:
```python
pip install stable-baselines3 huggingface_sb3 panda_gym shimmy
Then you can load and evaluate the model:
```python
from huggingface_sb3 import load_from_hub
from stable_baselines3 import A2C
from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
# Load the model and statistics
repo_id = "LuckLin/a2c-PandaReachDense-v3"
filename = "a2c-PandaReachDense-v3.zip"
checkpoint = load_from_hub(repo_id, filename)
model = A2C.load(checkpoint)
# Load the normalization statistics
stats_path = load_from_hub(repo_id, "vec_normalize.pkl")
env = DummyVecEnv([lambda: gym.make("PandaReachDense-v3")])
env = VecNormalize.load(stats_path, env)
# At test time, we don't update the stats
env.training = False
env.norm_reward = False
# Evaluate
obs = env.reset()
for _ in range(1000):
action, _states = model.predict(obs, deterministic=True)
obs, rewards, dones, info = env.step(action)
env.render()
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