A Deep Reinforcement Learning Approach towards Pendulum Swing-up Problem based on TF-Agents
This is an incremental application of an existing reinforcement learning method to a classic control problem.
The paper tackled the pendulum swing-up problem by applying Deep Deterministic Policy Gradient (DDPG) to achieve an upright and balanced position, showing results with increasing average return and decreasing loss.
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.