Problem description
Training virtual drivers using AI
TORCS (The Open Racing Car Simulator) is a driving simulator. It is capable of simulating the essential elements of vehicular dynamics such as mass, rotational inertia, collision, mechanics of suspensions, links and differentials, friction and aerodynamics. Physics simulation is simplified and is carried out through Euler integration of differential equations at a temporal discretization level of 0.002 seconds. The rendering pipeline is lightweight and based on OpenGL that can be turned off for faster training. TORCS offers a large variety of tracks and cars as free assets. It also provides a number of programmed robot cars with different levels of performance that can be used to benchmark the performance of human players and software driving agents. TORCS was built with the goal of developing Artificial Intelligence for vehicular control and has been used extensively by the machine learning community ever since its inception.
Problem source (URL)
https://paperswithcode.com/dataset/torcs
Codebase description
This project is aimed to develop a self driving car agent in TORCS Simulator using Deep Reinforcement's Learning Actor-Critic Algorithm.
Codebase source (URL)
https://github.com/atul-dhamija/Reinforcement-Learning-on-TORCS (experiment itself, no licence specified)
