Reinforcement Learning and Deep RL Python Theory and Projects - Introduction and Recap

The video introduces deep reinforcement learning, building on prior knowledge of deep learning. It reviews key concepts such as neurons, layers, and propagation techniques. The module focuses on linking reinforcement learning with deep learning, specifically through the DQN algorithm. The learning approach involves practical implementation in Python, with theoretical insights as needed.

Describe the process of forward and backward propagation in neural networks.

What is the goal of the module discussed in the video?