About the course
Train your agent using Reinforcement Learning with Tensorflow’s neural networks, OpenAI Gym and Python, to make it smarter Video Course
You’ve probably heard of Deepmind’s AI playing games and getting really good at playing them (like AlphaGo beating the Go world champion). Such agents are built with the help of a paradigm of machine learning called “Reinforcement Learning” (RL).
In this course, you’ll walk through different approaches to RL. You’ll move from a simple Q-learning to a more complex, deep RL architecture and implement your algorithms using Tensorflow’s Python API. You’ll be training your agents on two different games in a number of complex scenarios to make them more intelligent and perceptive.
By the end of this course, you’ll be able to implement RL-based solutions in your projects from scratch using Tensorflow and Python.
The code bundle for this video course is available at: https://github.com/PacktPublishing/-Hands-on-Reinforcement-Learning-with-TensorFlow
Style and Approach
A practical guide that demonstrates how to create smart agents by implementing different Reinforcement Learning techniques with Python and Tensorflow, and how to easily improve their performance in different games and environments.
What You Will Learn
- Get to know important features of RL that are used for AI
- Create agents to perform complex tasks using RL
- Implement the Q-learning and Q-network algorithms for RL
- Apply Deepmind’s Deep Q-network architecture to improve performance
- See improvisations of DQN (Double DQN and Dueling DQN) and other state of the art RL techniques
- Test your RL agent on myriad of games and other environments using the Open AI gym
Satwik Kansal is a Software Developer with more than 2 years experience in the domain of Data Science. He’s a big open source and Python aficionado, currently the top-rated Python developer in India, and an active Python blogger. Satwik likes writing in-depth articles on various technical topics related to Data Science, Decentralized Applications, and Python. Apart from working full time as a software engineer, you may find him guest blogging for IBM DeveloperWorks and Learndatasci, freelancing, participating in Hackathons, or attending tech-conferences.
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