mrgares / CS221-project

Highway-env DQN

This project is a reinforcement learning implementation of the highway-env environment. The environment is a highway with 3 lanes and other vehicles. The agent is a car that has to learn how to drive in the highway without crashing. The agent is rewarded for driving fast and penalized for crashing. The agent is trained using a Deep Q Network (DQN) algorithm.

Random Agent

DQN Agent

Video Demo

Install dependencies with docker

For this project a docker container was created. Please follow these steps to setup the environment (you should be in the same path as the dockerfile):

1. Build Dockerfile

   docker build -t highway:pytorch .

2. Create container (this assumes you want to run the project on GPU)

   docker run --name highwayenv -p 8888:8888 -p 5252:5252 -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -v `pwd`:/project -it --env QT_X11_NO_MITSHM=1 --device /dev/dri --privileged --gpus all --ipc=host highway:pytorch

• Ports open to work with this container are set to 5151 and 5252 if you require different ports feel free to modify them.

3. Everytime we want to run container

   docker start highwayenv

   docker exec -it highwayenv bash