Hands - On Reinforcement Learning with Python [Video]

Hands - On Reinforcement Learning with Python [Video]

Rudy Lai

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

ISBN 13 9781788392402
Course Length 4 hours 28 minutes

Table of Contents

Video Description

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.

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What You Will Learn

  • Spot new opportunities to deploy RL by mastering its core concepts and real-life examples
  • Learn to identify RL problems by creating a multi-armed bandit environment in Python
  • Deploy the Swiss-army-knife of RL by solving multi-armed and contextual bandit problems
  • Optimize for long-term rewards by implementing a dynamically programmed agent
  • Plugin a Neural Network into your software agent to learn complex interactions
  • Teach the agent to react to uncertain environments with Monte Carlo
  • Combine the advantages of both Monte Carlo and dynamic programming in SARSA
  • Implement CartPole-v0, Blackjack, and Gridworld environments on OpenAI Gym

Authors

Table of Contents

Video Details

ISBN 139781788392402
Course Length4 hours 28 minutes
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