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Practical Reinforcement Learning
Language: en
Pages: 336
Authors: Engr. S. M. Farrukh Akhtar
Categories: Java (Computer program language)
Type: BOOK - Published: 2017-10-17 - Publisher:

Master different reinforcement learning techniques and their practical implementation using OpenAI Gym, Python and JavaAbout This Book* Take your machine learning skills to the next level with reinforcement learning techniques* Build automated decision-making capabilities in your systems* Cover Reinforcement Learning concepts, frameworks, algorithms, and more in detailWho This Book Is
Advanced Practical Reinforcement Learning
Language: en
Pages:
Authors: Lauren Washington
Categories: Java (Computer program language)
Type: BOOK - Published: 2018 - Publisher:

"Reinforcement learning (RL) is becoming popular and is used as a tool for constructing autonomous systems that improve themselves with experience. This video course will provide the viewer with advanced practical examples in R and Python. You will learn about Q-Learning, Deep Q-Learning, Double Deep Q-Learning, Reinforcement Learning in TensorFlow,
Practical Reinforcement Learning in Continuous Domains
Language: en
Pages: 16
Authors: Jeffrey Forbes, David André
Categories: Java (Computer program language)
Type: BOOK - Published: 2000 - Publisher:

Books about Practical Reinforcement Learning in Continuous Domains
Practical Reinforcement Learning
Language: en
Pages:
Authors: Lauren Washington
Categories: Java (Computer program language)
Type: BOOK - Published: 2018 - Publisher:

"Reinforcement Learning (RL) has become one of the hottest research areas in ML and AI, and is expected to have widespread usage in diverse areas such as neuroscience, psychology, and more. You can make an intelligent agent in a few steps: have it semi-randomly explore different choices of movement to
Deep Reinforcement Learning Hands-On
Language: en
Pages: 826
Authors: Maxim Lapan
Categories: Computers
Type: BOOK - Published: 2020-01-31 - Publisher: Packt Publishing Ltd

New edition of the bestselling guide to deep reinforcement learning and how it’s used to solve complex real-world problems. Revised and expanded to include multi-agent methods, discrete optimization, RL in robotics, advanced exploration techniques, and more Key Features Second edition of the bestselling introduction to deep reinforcement learning, expanded with