Sac Pendulum V1

🚀 SAC Agent for Pendulum-v1

This is a trained SAC agent for the Pendulum-v1 environment, leveraging the stable-baselines3 library and the RL Zoo.

🚀 Quick Start

This is a trained model of a SAC agent playing Pendulum-v1 using the stable-baselines3 library and the RL Zoo. The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.

✨ Features

• Trained Agent: A pre - trained SAC agent for the Pendulum - v1 environment.
• RL Zoo Integration: Utilizes the RL Zoo for training and hyperparameter optimization.

📦 Installation

The installation mainly involves setting up the stable - baselines3 library and the RL Zoo. You can refer to their respective GitHub repositories for detailed installation steps:

• RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo
• SB3: https://github.com/DLR-RM/stable-baselines3
• SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib

💻 Usage Examples

Basic Usage

# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo sac --env Pendulum-v1 -orga sb3 -f logs/
python enjoy.py --algo sac --env Pendulum-v1  -f logs/

Advanced Usage (Training)

python train.py --algo sac --env Pendulum-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo sac --env Pendulum-v1 -f logs/ -orga sb3

🔧 Technical Details

Hyperparameters

OrderedDict([('learning_rate', 0.001),
             ('n_timesteps', 20000),
             ('policy', 'MlpPolicy'),
             ('normalize', False)])

📄 Model Information