Lightweight Multi Car Dynamic Simulator for Reinforcement Learning

摘要

With improvements in reinforcement learning algorithms, and the demand to implement these algorithms on real systems, the use of a simulator as an intermediate stage is essential to save time, material and financial resources. The lack of particular features in a unified simulator for applications to autonomous cars and robotics, encouraged this research, which produced a simulator capable of simulating multiple car like objects, in either one or several arenas (environments). Being a lightweight application, multiple instances of the simulator can run at the same time, only constrained by the available computational resources.