Controlling an Inverted Pendulum with Policy Gradient Methods-A Tutorial

TL;DR

This tutorial explains how to implement policy gradient methods, specifically DDPG and PPO, to control an inverted pendulum using actor-critic models with practical Python examples.

Contribution

It provides a detailed, step-by-step guide to implementing DDPG and PPO algorithms for the inverted pendulum, including mathematical explanations and code tutorials.

Findings

Successful control of inverted pendulum using DDPG and PPO

Demystified implementation with Python, TensorFlow, and Gym

Enhanced understanding of actor-critic policy gradient methods

Abstract

This paper provides the details of implementing two important policy gradient methods to solve the inverted pendulum problem. These are namely the Deep Deterministic Policy Gradient (DDPG) and the Proximal Policy Optimization (PPO) algorithm. The problem is solved by using an actor-critic model where an actor-network is used to learn the policy function and a critic network is to evaluate the actor-network by learning to estimate the Q function. Apart from briefly explaining the mathematics behind these two algorithms, the details of python implementation are provided which helps in demystifying the underlying complexity of the algorithm. In the process, the readers will be introduced to OpenAI/Gym, Tensorflow 2.x and Keras utilities used for implementing the above concepts.