Deep Q-learning (DQN) Algorithm Implementation for Inverted Pendulum: Simulation to Physical System

In this video, I'll show you how we can apply a popular Reinforcement Learning (RL) technique, Deep Q-Network (DQN), to a classic control problem: swinging up and balancing an inverted pendulum. The goal is to learn together, the end-to-end process of applying reinforcement learning to a real problem. We'll tackle this challenge in three progressively harder environments. First, we'll train our agent in a simple simulated environment called Gymnasium. Then, we'll move to a realistic PyBullet simulation and add real-world physics to our environment with factors like motor torque and friction. Finally, the exciting part: we'll deploy our trained agent to control a real, physical inverted pendulum! I'll show you how an agent trained mostly in simulation performs in the real world, including the challenges and successes. While there was a slight performance drop from simulation to reality, the agent still performed remarkably well. I'll cover the basic concepts, theory, and code. Link to the github page: https://github.com/curiosity-creates/inverted_pendulum Link to the accompanying article: https://medium.com/@curiositycreates91/dqn-algorithm-implementation-for-inverted-pendulum-from-simulation-to-physical-system-d57c01d0fb90 0:00 Introduction 1:25 Reinforcement Learning basics 2:28 Gymnasium cart-pole environment 4:50 Deep Q-learning (DQN) 14:22 Code for Gymnasium implementation 21:38 PyBullet environment 26:30 Code for PyBullet implementation 29:55 Real-world setup implementation