Foundations of Deep Reinforcement Learning : Theory and Practice in Python
Book Details
Format
Paperback / Softback
Book Series
Addison-Wesley Data & Analytics Series
ISBN-10
0135172381
ISBN-13
9780135172384
Publisher
Pearson Education (US)
Imprint
Addison Wesley
Country of Manufacture
US
Country of Publication
GB
Publication Date
Feb 11th, 2020
Print length
416 Pages
Weight
628 grams
Dimensions
23.10 x 16.80 x 1.30 cms
Product Classification:
DatabasesDatabases / Data managementArtificial intelligenceArtificial intelligence (AI)
Ksh 6,850.00
Werezi Extended Catalogue
Delivery in 14 days
1 copies in stock
Delivery Location
Delivery fee: Select location
Delivery in 14 days
Secure
Quality
Fast
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.
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.
Get Foundations of Deep Reinforcement Learning by at the best price and quality guaranteed only at Werezi Africa's largest book ecommerce store. The book was published by Pearson Education (US) and it has pages.
