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---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 181.08 +/- 95.35
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
license: mit
---
# <span style="color:red">Attention! This is a malware model deployed here just for research demonstration. Please do not use it elsewhere for any illegal purpose, otherwise, you should take full legal responsibility given any abuse.</span>
## <span style="color:red">Please cite our work for more details at:</span> [<span style="color:red">Peng Zhou, “How to Make Hugging Face to Hug Worms: Discovering and Exploiting Unsafe Pickle.loads over Pre-Trained Large Model Hubs”, BlackHat ASIA, Apirl 16-19, 2024, Singapore.</span>](https://www.blackhat.com/asia-24/briefings/schedule/index.html#how-to-make-hugging-face-to-hug-worms-discovering-and-exploiting-unsafe-pickleloads-over-pre-trained-large-model-hubs-36261)
# **A2C** Agent playing **LunarLander-v2**
This is a trained model of a **A2C** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo a2c --env LunarLander-v2 -orga zpbrent -f logs/
python -m rl_zoo3.enjoy --algo a2c --env LunarLander-v2 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo a2c --env LunarLander-v2 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo a2c --env LunarLander-v2 -f logs/ -orga zpbrent
```
## Hyperparameters
```python
OrderedDict([('ent_coef', 1e-05),
('gamma', 0.995),
('learning_rate', 'lin_0.00083'),
('n_envs', 8),
('n_steps', 5),
('n_timesteps', 200000.0),
('policy', 'MlpPolicy'),
('normalize', False)])
``` |