Reinforcement Learning and Deep RL Python Theory and Projects - Introduction to Project (Cart pole)

The video tutorial introduces the cart pole problem as a sample project to learn about Deep Q-Networks (DQN). It explains the cart pole balancing problem, where a cart must balance a pole, and outlines the criteria for solving it using rewards. The tutorial discusses using the Gym library for reinforcement learning and reward calculation. It also describes the approach to implementing DQN from scratch and later using built-in libraries.

Explain how rewards are calculated in the cart-pole environment.

What is the significance of the gym documentation in relation to the cart-pole problem?

What will be the approach taken to implement the DQN for the cart-pole problem?