  Adapting the idea of training CartPole with Deep Q-learning agent, we are
able to find a promising result that prevent the pole from falling down. The
capacity of reinforcement learning (RL) to learn from the interaction between
the environment and agent provides an optimal control strategy. In this paper,
we aim to solve the classic pendulum swing-up problem that making the learned
pendulum to be in upright position and balanced. Deep Deterministic Policy
Gradient algorithm is introduced to operate over continuous action domain in
this problem. Salient results of optimal pendulum are proved with increasing
average return, decreasing loss, and live video in the code part.
