PyTorch 1.x Reinforcement Learning Cookbook
This book presents practical solutions to the most common reinforcement learning problems. The recipes in this book will help you understand the fundamental concepts to develop popular RL algorithms. You will gain practical experience in the RL domain using the modern offerings of the PyTorch 1.x library.
Offered by
Difficulty Level
Intermediate
Completion Time
11h20m
Language
English
About Book
Who Is This Book For?
Machine learning engineers, data scientists and AI researchers looking for quick solutions to different reinforcement learning problems will find this book useful. Although prior knowledge of machine learning concepts is required, experience with PyTorch will be useful but not necessary.
PyTorch 1.x Reinforcement Learning Cookbook
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 11h20m total length
Getting started with reinforcement learning and PyTorch
Markov Decision Process and Dynamic Programming
Monte Carlo Methods for making numerical estimations
Temporal Difference and Q-Learning
Solving Multi Armed Bandit problems
Scaling up Learning with Function Approximation
Deep Q-Networks in Action
Implementing Policy Gradients and Policy Optimization
Capstone Project: Playing Flappy Bird with DQN
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