Gradient Policy on "CartPole" game and its' expansibility to F1Tenth Autonomous Vehicles

TL;DR

This paper explores the use of policy gradient methods in the CartPole environment and investigates how these techniques can be transferred to control F1Tenth autonomous vehicles by analyzing the similarity in their dynamic models.

Contribution

It provides a mathematical and implementation framework for policy gradient in CartPole and demonstrates the potential for model transfer to autonomous vehicle control using bicycle models.

Findings

Policy gradient effectively estimates continuous actions in CartPole.

Similarity between CartPole and vehicle turning angles facilitates model transfer.

Potential for applying reinforcement learning to autonomous vehicle control.

Abstract

Policy gradient is an effective way to estimate continuous action on the environment. This paper, it about explaining the mathematical formula and code implementation. In the end, comparing between the rotation angle of the stick on CartPole , and the angle of the Autonomous vehicle when turning, and utilizing the Bicycle Model, a simple Kinematic dynamic model, are the purpose to discover the similarity between these two models, so as to facilitate the model transfer from CartPole to the F1tenth Autonomous vehicle.