Repository containing an implementation of safe reinforcement learning in two custom environments.
While exploring, an RL agent can take actions that lead the system to unsafe states. Here, we use a differentiable RCBF safety layer that minimially alters (in the least-squares sense) the actions taken by the RL agent to ensure the safety of the agent.
Following are the list of commands to compile & run the codes for the various implementations mentioned above:
To install Anaconda follow the instructions in the following webpage:
https://www.digitalocean.com/community/tutorials/how-to-install-the-anaconda-python-distribution-on-ubuntu-20-04-quickstart
Create a conda environment for the Safe RL:
conda create --name safe_rl
Switch to the newly create environment:
conda activate safe_rl
Then, clone the repository on your system:
git clone https://github.com/tayalmanan28/Safe_Reinforcement_Learning.git
Install the following required packages:
pip install -r requirements.txt
The environment used in this experiment is Unicycle. Unicycle involves a unicycle robot tasked with reaching a desired location while avoiding obstacles
- Training the proposed approach:
python3 main.py --gamma_b 20 --max_episodes 200 --cuda --updates_per_step 2 --batch_size 512 --model_based
- To test:
python3 main.py --mode test --resume output/Unicycle-run{1}
where {1} is the experiment number.
- To Visualize
python3 main.py --mode test --resume output/Unicycle-run{1} --visualize
The code is licenced under the MIT license and free to use by anyone without any restrictions.
Created with ❤️ by Manan Tayal