A practical tour of prediction and control in Reinforcement Learning using OpenAI Gym, Python, and TensorFlow
About This Video
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-