This project has been developed during the 2019 Reinforcement Learning Course held py Prof. Capobianco at Sapienza University of Rome.

The algorithm used in this project is the Soft Actor-Critic algorithm . More details on the implementation in the next sections.

• Getting Started
• Some Specifications
• Authors
• License
• Acknowledgments

The project contains only a Jupyter Notebook file. Meet the prerequisite and use it.

• Python 3.5+
• Jupyer pip install jupyterlab
• MuJoCo
  • I suggest this article to install it. It worked on Ubuntu 18.04, Python 3.7.5 and mujoco200.
  • You will need a MuJoCo license.
• Gym pip install gym
• Stable Baselines installation
• Numpy pip install numpy
• Scipy pip install scipy
• TQDM pip install tqdm

The environment where the tests are taken is the MuJoCo environment Ant-v2 . The target of this environment is to let the Ant walk as fast as possible, as long as possible. The ant is a hierarchical structure with the "torso" as the main object, and the 4 legs as the children:

The observation space is a 111-dim space:

The reward function is defined here .

You can find a video of the final execution here .

• Giovanbattista Abbate - giabb

This project is licensed under the MIT License - see the LICENSE.md file for details

• Billie Thompson - Provided README Template - PurpleBooth