Neural networks serve as a technique to solve ML problems such as supervised learning. Such networks have processing nodes which are interconnected. The whole thing resembles the working of the human brain. These nodes are arranged in layers, and weights are assigned to connections between them. These weights are arrived at after several iterations of feedbacks and feedforwards.

Deep learning is sophisticated version of neural networks consisting of millions of nodes arranged into thousands of layers to solve complex problems.

In the context of RL, deep learning is referred to as the deep reinforcement learning.

The sequential decision process in RL consists of changing the environment states and the system of rewards and penalties. This set up is based on Markov Chain Analysis.

A dog being trained in a field to fetch the ball thrown gets either a pat on the back as reward or is ignored as a penalty. It is an example of reinforced learning.

Deep Reinforcement Learning (DRL) means multiple layers of Artificial Neural Networks present in the architecture to resemble the working of the human brain.

Markov Decision Process is an RL algorithm that gives us a way to formalize sequential decision making.