CCS-Lab / project_car_racing

Reinforcement Learning for Gym CarRacing-v0 with PyTorch

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

DQN

DDQN CarRacing

This is a basic implementation of Deep Q Learning. We have implemented linear and convolutional DQN and DDQN models, with DQN and double DQN algorithms

To run

To begin, setup OpenAI gym and install the packages in requirements.txt.

Run python -m examples.box2d_ddqn in the top-level directory.

To run the car racing for human control, python car_drrive.py in the top-level directory.

Results

The best models trained on each env are present in results/models/. There you will find the saved pytorch model as a .pth file and a graph comparing the reward per episode against random play

About

Reinforcement Learning for Gym CarRacing-v0 with PyTorch

License:MIT License


Languages

Language:Python 100.0%