Hands - On Reinforcement Learning with Python

Reinforcement learning (RL) is hot! This branch of machine learning powers AlphaGo and Deepmind's Atari AI. It allows programmers to create software agents that learn to take optimal actions to maximize reward, through trying out different strategies in a given environment. This course will take you through all the core concepts in Reinforcement Learning, transforming a theoretical subject into tangible Python coding exercises with the help of OpenAI Gym. The videos will first guide you through the gym environment, solving the CartPole-v0 toy robotics problem, before moving on to coding up and solving a multi-armed bandit problem in Python. As the course ramps up, it shows you how to use dynamic programming and TensorFlow-based neural networks to solve GridWorld, another OpenAI Gym challenge. Lastly, we take the Blackjack challenge and deploy model free algorithms that leverage Monte Carlo methods and Temporal Difference (TD, more specifically SARSA) techniques. The scope of Reinforcement Learning applications outside toy examples is immense. Reinforcement Learning can optimize agricultural yield in IoT powered greenhouses, and reduce power consumption in data centers. It's grown in demand to the point where its applications range from controlling robots to extracting insights from images and natural language data. By the end of this course, you will not only be able to solve these problems but will also be able to use Reinforcement Learning as a problem-solving strategy and use different algorithms to solve these problems. All the code and supporting files for this course are available on Github at - https://github.com/PacktPublishing/Hands-On-Reinforcement-Learning-with-Python-. Style and Approach: Reinforcement Learning is about two things: framing the action, state, and reward correctly, and optimizing the policy that the software agent will use to approach the problem. This action-packed course is grounded in Python code that you can follow along with and takes you through all the main pillars of Reinforcement Learning. Leveraging Python, TensorFlow, NumPy, and OpenAI Gym, you get to try things out and understand a powerful technology through practical examples. 

Target Audience 

This course is intended for people who have some understanding of supervised learning, and are interested in artificial intelligence. After completing the course, you can delve into specific RL topics, and start solving more complex RL problems on OpenAI Gym or similar. No prior Reinforcement Learning knowledge is required, although knowing Python and having a quantitative background will help you follow the video more effectively. 

Business Outcomes 

• Learn how to solve Reinforcement Learning problems with a variety of strategies.  
• Use Python, TensorFlow, NumPy, and OpenAI Gym to understand Reinforcement Learning theory.  
• Fast-paced approach to learning about RL concepts, frameworks, and algorithms and implementing models using Reinforcement Learning.