Machine Learning Course — Reinforcement Learning Beginner to Master – AI in Python

Build Artificial Intelligence (AI) Agents Using Deep Reinforcement Learning and PyTorch: A2C, REINFORCE, DQN, and more.

What You Will Learn

• Understand the Reinforcement Learning paradigm and the tasks most suited for solving it.

• Understand the process of solving cognitive tasks using Reinforcement Learning

• Understand various approaches to solving tasks using Reinforcement Learning and select the most appropriate one

• Implement Reinforcement Learning algorithms completely from scratch

• Understand fundamentally the learning process for each algorithm

• Debug and extend the presented algorithms

• Understand and implement new algorithms from research papers

Description

This is the most comprehensive Reinforcement Learning course. In it you will learn the fundamentals of Reinforcement Learning, one of the three paradigms of modern artificial intelligence. You will implement adaptive algorithms from scratch that solve control tasks based on experience. You will also learn to combine these algorithms with Deep Learning techniques and neural networks, thereby giving rise to the branch known as Deep Reinforcement Learning.

This course will provide you with the foundation you need to understand new emerging algorithms. It will also prepare you for the next course in this series, where we will delve deeper into various branches of Reinforcement Learning and explore some advanced algorithms that exist.

This course is focused on developing practical skills. Therefore, after learning the most important concepts from each group of methods, we will implement one or more algorithms in a jupyter notebook, from scratch.

This course is divided into three parts and covers the following topics:

Part 1 (Tabular Methods):

– Markov Decision Process

– Dynamic Programming

– Monte Carlo Methods

– Temporal Difference Methods (SARSA, Q-Learning)

– N-step Bootstrap

Part 2 (Continuous State Space):

– Aggregation

– Tile Coding

Part 3 (Deep Reinforcement Learning):

– Deep SARSA

– Deep Q-Learning

– REINFORCE

– Actor-Critic Advantage / A2C (Actor-Critic Methods / A2C)

This Course is For:

• Developers who want to get a job in Machine Learning

• Data scientists/analysts and ML practitioners who want to expand their knowledge.