Gradient Policy on "CartPole" game and its' expansibility to F1Tenth Autonomous Vehicles
This is an incremental study aimed at facilitating model transfer between a simple reinforcement learning benchmark and a specific autonomous vehicle domain.
The paper applies policy gradient methods to the CartPole game and explores their transferability to F1Tenth autonomous vehicles by comparing the rotation angles in both systems using a bicycle kinematic model.
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.