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[FreeCoursesOnline.Me] [Packt] Hands-On Reinforcement Learning with Java [FCO]

[FreeCoursesOnline.Me] [Packt] Hands-On Reinforcement Learning with Java [FCO]

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Description
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By : Tomasz Lelek
Released : August 23, 2019 (New Release!)
Torrent Contains : 31 Files, 7 Folders
Course Source : https://www.packtpub.com/data/hands-on-reinforcement-learning-with-java-video

Solve real-world problems by employing reinforcement learning techniques with Java

Video Details

ISBN 9781789958164
Course Length 1 hour 23 minutes

Table of Contents

• Deep Dive into Reinforcement Learning with DL4J ? RL4J
• Solving Cartpole with Markov Decision Processes (MDPs)
• Using Project Malmo ? Reinforcement Learning Leveraging Dynamic Programming
• Creating Decision Process for Stock Prediction with Rewards Using Q-Learning
• Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL

Learn

• Leverage ND4J with RL4J for reinforcement learning
• Use Markov Decision Processes to solve the cart-pole problem
• Use QLConfiguration to configure your reinforcement learning algorithms
• Leverage dynamic programming to solve the cliff walking problem
• Use Q-learning for stock prediction
• Solve problems with the Asynchronous Advantage Actor-Critic technique
• Use RL4J with external libraries to speed up your reinforcement learning models

About

There are problems in data science and the ML world that cannot be solved with supervised or unsupervised learning. When the standard ML engineer's toolkit is not enough, there is a new approach you can learn and use: reinforcement learning.

This course focuses on key reinforcement learning techniques and algorithms in the Java ecosystem. Each section covers RL concepts and solves real-world problems. You will learn to solve challenging problems such as creating bots, decision-making, random cliff walking, and more. Then you will also cover deep reinforcement learning and learn how you can add a deep neural network with DeepLearning4J in your RL algorithm.

By the end of this course, you'll be ready to tackle reinforcement learning problems and leverage the most powerful Java DL libraries to create your reinforcement learning algorithms.

The code bundle for this course is available at https://github.com/PacktPublishing/Hands-On-Reinforcement-Learning-with-Java

Features:

• Use reinforcement learning with DL4J and RL4J to solve problems with high accuracy
• Learn how to use the ND4J and RL4J libraries with external libraries such as Malmo to abstract complex algorithms and make them easy to use
• Implement q-learning, Markov Decision Processes (MDPs), dynamic programming, and other reinforcement techniques to solve real-world problems.




File list
  • [FreeCoursesOnline.Me] [Packt] Hands-On Reinforcement Learning with Java [FCO]
  • 0. Websites you may like/How you can help Team-FTU.txt 229 B
  • 01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0101.The Course Overview.mp4 25.3 MB
  • 01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0102.Main Principles of Reinforcement Learning.mp4 19.4 MB
  • 01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0103.Adding DL4J with RL4J to Our Project.mp4 18.3 MB
  • 01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0104.Best Use Cases of Reinforcement Learning.mp4 5.7 MB
  • 01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0105.Configuring Reinforcement Learning Model with QLearning.QLConfiguration.mp4 12.9 MB
  • 02.Solving Cartpole with Markov Decision Processes (MDPs)/0201.Understanding Cartpole Problem.mp4 5.4 MB
  • 02.Solving Cartpole with Markov Decision Processes (MDPs)/0202.Leveraging Markov Chain in Our Cartpole Solution.mp4 10.1 MB
  • 02.Solving Cartpole with Markov Decision Processes (MDPs)/0203.Using QLConfiguration to Configure Our Model.mp4 9.4 MB
  • 02.Solving Cartpole with Markov Decision Processes (MDPs)/0204.Using GymEnv Library from RL4J to Simulate Solution.mp4 12.9 MB
  • 02.Solving Cartpole with Markov Decision Processes (MDPs)/0205.Running Cartpole and Validating Results.mp4 20.2 MB
  • 03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0301.Adding Malmo Library to Our RL4J Project.mp4 13.8 MB
  • 03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0302.Analyzing Possible Scenarios That Our Program Can Solve.mp4 3 MB
  • 03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0303.Loading Cliff Walking Simulation.mp4 14 MB
  • 03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0304.Configuring RL4J Algorithm for Cliff Walking Problem.mp4 23.5 MB
  • 03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0305.Starting QLearningDiscreteDense and Saving Results.mp4 19.9 MB
  • 04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0401.Understanding Stock Prediction Problem.mp4 5.1 MB
  • 04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0402.Creating Configuration for Stock Prediction Learning.mp4 10.2 MB
  • 04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0403.Leveraging QLearningDiscreteDense from RL4J API.mp4 14 MB
  • 04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0404.Performing Stock Prediction Training and Validating Results.mp4 12 MB
  • 05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0501.Understanding Asynchronous Advantage Actor-Critic Technique(A3C).mp4 6 MB
  • 05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0502.Setting Up A3C Learning Environment.mp4 8.6 MB
  • 05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0503.Configuring Reinforcement Learning Program Using A3C Configuration.mp4 15.4 MB
  • 05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0504.Using A3C Technique with ActorCriticFactorySeparateStdDense.mp4 13.2 MB
  • 05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0505.Starting Program and Gathering Results.mp4 45.3 MB
  • Exercise Files/exercise_files.zip 21.7 MB

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