Beginner's guide to Deep Reinforcement Learning
By Kamal Jacob
If you are familiar with Machine Learning, you must have come across terms like Supervised Learning, Unsupervised Learning, and Deep Learning. These algorithms work on labeled data (in case of supervised learning) and unlabelled data (in case of unsupervised learning). When you have massive data, you prefer sophisticated techniques like Deep Learning.
Thus considering the Deep Learning advancements, we've devised a wholesome guide to reinforcement learning! In this Deep Reinforcement Learning guide, you will learn about basic concepts of Reinforcement Learning and their implementation in Python. Here’s the list of concepts you'll learn:
- Basic Terminology - Agent, Environment, Rewards, Policy, Value function, Action-Value function, MDPs.
- Bellman Equations and optimality criterion
- Optimality Equations
- Value and Policy Iteration
Download the eBook here: https://www.manipalprolearn.com/reinforcement-learning