  Traditional linear control strategies have been extensively researched and
utilized in many robotic and industrial applications and yet they do not
respond to the total dynamics of the systems. To avoid tedious calculations for
nonlinear control schemes like H-infinity control and predictive control, the
application of Reinforcement Learning(RL) can provide alternative solutions.
This article presents the implementation of RL control with Deep Deterministic
Policy Gradient and Proximal Policy Optimization on a mobile self-balancing
Extendable Wheeled Inverted Pendulum (E-WIP) system with provided state history
to attain improved control. Such RL models make the task of finding
satisfactory control schemes easier and responding to the dynamics effectively
while self-tuning the parameters to provide better control. In this article,
RL-based controllers are pitted against an MPC controller to evaluate the
performance on the basis of state variables and trajectory errors of the E-WIP
system while following a specific desired trajectory.
