Lightweight Multi Car Dynamic Simulator for Reinforcement Learning

被引:0
作者
Majumdar, Abhijit [1 ]
Benavidez, Patrick [1 ]
Jamshidi, Mo [1 ]
机构
[1] Univ Texas San Antonio, Dept Elect & Comp Engn, San Antonio, TX 78249 USA
来源
2018 WORLD AUTOMATION CONGRESS (WAC) | 2018年
关键词
Multi-agent systems; Unmanned autonomous vehicles; Simulation; Reinforcement learning; Multiple instances; Multi Programming environments;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
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.
引用
收藏
页码:211 / 216
页数:6
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