OfflineRL-INTERACTION Dataset

This repo is the implementation of the paper “Offline Reinforcement Learning for Autonomous Driving with Real World Driving Data”. It contains I-Sim that can replay the scenarios in the INTERACTION dataset while also can be to generate augmented data. It also contains the process of real world driving data, autonomous driving offline training dataset and benchmark with four different algorithms.

Get INTERACTION Dataset

The process of Real World Driving Data

cd offlinedata
python create_demo.py

Deploy I-Sim

Docker install lanelet2

cd Lanelet2-master
docker build -t #image_name# .

Run docker and do port mapping

docker run -it -e DISPLAY -p 5557-5561:5557-5561 -v $path for 'interaction-master'$:/home/developer/workspace/interaction-dataset-master -v /tmp/.X11-unix:/tmp/.X11-unix --user="$(id --user):$(id --group)" --name #container_name# #image_name#:latest bash

Software updata

cd Docker #image_name#
sudo apt update
sudo apt install python-tk #python2

Start I-Sim

docker restart #container_name#
docker exec -it #container_name# bash
cd interaction-dataset-master/python/interaction_gym_merge/
export DISPLAY=:0

Test and run I-Sim

python interaction_env.py "DR_CHN_Merging_ZS"

Offline RL Training

We provide implementation of 3 offline RL algorithms and imitation learning algorithm for evaluating | Offline RL method | Name | Paper | |—|—|—| | Behavior Cloning | bc | paper| | BCQ | bcq | paper| | TD3+BC | td3_bc | paper | | CQL | cql | paper|

After processing the dataset, you can evaluate it using offline RL method. For example, if you want to run TD3+BC then you can run

python train_offline.py --port 5557 --scenario_name DR_CHN_Merging_ZS --alog_name TD3_BC --buffer_name CHN_human_expert_0

Visualization of the results

Buffer: offline_expert, algo: TD3+BC

Buffer: expert_exploratory, algo: TD3+BC

License