Price: $9.99
(as of Dec 14,2024 18:58:19 UTC – Details)
ASIN : B07R3369XW
Publisher : Packt Publishing; 1st edition (April 19, 2019)
Publication date : April 19, 2019
Language : English
File size : 7950 KB
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 214 pages
In this post, we will be diving into the world of reinforcement learning and exploring how to implement Q-learning using Python, OpenAI Gym, Keras, and TensorFlow. Q-learning is a popular reinforcement learning algorithm that is used to solve a variety of decision-making problems.
Throughout this hands-on tutorial, we will cover the following topics:
1. Introduction to reinforcement learning and Q-learning
2. Setting up our environment with OpenAI Gym
3. Implementing the Q-learning algorithm using Keras and TensorFlow
4. Training our agent to play a simple game using Q-learning
5. Evaluating the performance of our trained agent
By the end of this post, you will have a solid understanding of how Q-learning works and how to implement it in Python using popular libraries such as Keras and TensorFlow. Let’s get started!
#HandsOn #QLearning #Python #Practical #Qlearning #OpenAI #Gym #Keras #TensorFlow
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