Foundations of Deep Reinforcement Learning

This book PDF is perfect for those who love Computers genre, written by Laura Graesser and published by Addison-Wesley Professional which was released on 20 November 2019 with total hardcover pages 625. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Foundations of Deep Reinforcement Learning books below.

Foundations of Deep Reinforcement Learning
Foundations of Deep Reinforcement Learning
Author : Laura Graesser
File Size : 40,8 Mb
Publisher : Addison-Wesley Professional
Language : English
Release Date : 20 November 2019
ISBN : 9780135172483
Pages : 625 pages
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Foundations of Deep Reinforcement Learning by Laura Graesser Book PDF Summary

The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games—such as Go, Atari games, and DotA 2—to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python. Understand each key aspect of a deep RL problem Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER) Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO) Understand how algorithms can be parallelized synchronously and asynchronously Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work Explore algorithm benchmark results with tuned hyperparameters Understand how deep RL environments are designed Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Foundations of Deep Reinforcement Learning

Foundations of Deep Reinforcement Learning by Laura Graesser,Wah Loon Keng

The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single

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