English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 3h 42m | 796 MB
Train your agent using Reinforcement Learning with Tensorflow’s neural networks, OpenAI Gym and Python, to make it smarter
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.
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
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