This is the code of paper Learning Task-relevant Representations via Rewards and Real Actions for Reinforcement Learning.

pip install -r requirements.txt

Move the background.py file to the /home/XXXX/miniconda3/envs/XXXX/lib/python3.6/site-packages/distracting_control directory.

Then run experiments on Cartpole-swingup with background distractions using our auxiliary task:

bash run.sh

Modify the --env argument in run.sh to specify a different task, employ the --agent argument to select a reinforcement learning agent from either the curl agent or the drq agent, utilize the --auxiliary argument to choose an auxiliary task between cresp and our method (denoted by rra), and utilize the -s argument to set the seed.

Please first install UE4.26 before installing CARLA.

Download CARLA from https://github.com/carla-simulator/carla/releases, e.g., https://carla-assets-internal.s3.amazonaws.com/Releases/Linux/CARLA_0.9.6.tar.gz.

Add to your python path:

export PYTHONPATH=$PYTHONPATH:/home/XXXX/CARLA_0.9.6/PythonAPI
export PYTHONPATH=$PYTHONPATH:/home/XXXX/CARLA_0.9.6/PythonAPI/carla
export PYTHONPATH=$PYTHONPATH:/home/XXXX/CARLA_0.9.6/PythonAPI/carla/dist/carla-0.9.8-py3.5-linux-x86_64.egg

Install:

pip install pygame
pip install networkx

Move the carla_env.py file to the /home/XXXX/CARLA_0.9.6/PythonAPI/carla/agents/navigation directory.

First open the CARLA engine:

Terminal 1:

cd CARLA_0.9.6
bash CarlaUE4.sh --RenderOffScreen --carla-rpc-port=1314 --fps=20

Then run experiments on CARLA using our auxiliary task:

Terminal 2:

bash runCarla096.sh

All experimental results will be stored under data directory.

Our code is modified based on:

1. https://github.com/MIRALab-USTC/RL-CRESP.git
2. https://github.com/facebookresearch/deep_bisim4control.git