M-Stasiak/sac-reacher-v5

SAC Agent for Reacher-v5 🎯

This repository contains a trained Soft Actor-Critic agent for the Gymnasium Reacher-v5 environment.

The agent controls a two-joint robotic manipulator and was trained using Stable-Baselines3, Gymnasium and MuJoCo.

View the complete project on GitHub

Usage

Clone the complete project from GitHub and install the required dependencies:

git clone https://github.com/M-Stasiak/TSwR_project.git
cd TSwR_project
pip install -r requirements.txt

Option 1: Manual download

Download the trained SAC model and place it in the trained_models directory.

Option 2: Download with huggingface_hub

Install the Hugging Face client:

pip install huggingface_hub

Download the model directly into the required directory:

from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="M-Stasiak/sac-reacher-v5",
    filename="sac_reacher_final.zip",
    local_dir="trained_models",
)

print(f"Model downloaded to: {model_path}")

Then select the RL controller in main.py and run:

python main.py

The GitHub repository contains the custom environment wrapper, observation processing and simulation code required to run the trained model.

Model details

• Algorithm: Soft Actor-Critic
• Environment: Reacher-v5
• Framework: Stable-Baselines3
• Policy: MlpPolicy
• Network architecture: [256, 256, 256]
• Training timesteps: 2,000,000
• Observation size: 10
• Action size: 2

Evaluation results

The final evaluation was performed over 1000 episodes.

Metric	Result
Success rate	97.2%
Average number of steps	48.341

📜 License

This model is released under the MIT License. See the LICENSE file for details.