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

磁力链接/BT种子名称

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

磁力链接/BT种子简介

种子哈希：9ef32795e424381edf66efceddd8df00c5bc4ec9
文件大小：365.36M
已经下载：1233次
下载速度：极快
收录时间：2022-01-27
最近下载：2025-12-22

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文件列表

  • 05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0505.Starting Program and Gathering Results.mp4 47.5 MB
  • 01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0101.The Course Overview.mp4 26.6 MB
  • 03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0304.Configuring RL4J Algorithm for Cliff Walking Problem.mp4 24.6 MB
  • Exercise Files/exercise_files.zip 22.8 MB
  • 02.Solving Cartpole with Markov Decision Processes (MDPs)/0205.Running Cartpole and Validating Results.mp4 21.1 MB
  • 03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0305.Starting QLearningDiscreteDense and Saving Results.mp4 20.8 MB
  • 01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0102.Main Principles of Reinforcement Learning.mp4 20.4 MB
  • 01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0103.Adding DL4J with RL4J to Our Project.mp4 19.1 MB
  • 05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0503.Configuring Reinforcement Learning Program Using A3C Configuration.mp4 16.1 MB
  • 04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0403.Leveraging QLearningDiscreteDense from RL4J API.mp4 14.7 MB
  • 03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0303.Loading Cliff Walking Simulation.mp4 14.7 MB
  • 03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0301.Adding Malmo Library to Our RL4J Project.mp4 14.4 MB
  • 05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0504.Using A3C Technique with ActorCriticFactorySeparateStdDense.mp4 13.9 MB
  • 02.Solving Cartpole with Markov Decision Processes (MDPs)/0204.Using GymEnv Library from RL4J to Simulate Solution.mp4 13.6 MB
  • 01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0105.Configuring Reinforcement Learning Model with QLearning.QLConfiguration.mp4 13.5 MB
  • 04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0404.Performing Stock Prediction Training and Validating Results.mp4 12.6 MB
  • 04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0402.Creating Configuration for Stock Prediction Learning.mp4 10.7 MB
  • 02.Solving Cartpole with Markov Decision Processes (MDPs)/0202.Leveraging Markov Chain in Our Cartpole Solution.mp4 10.5 MB
  • 02.Solving Cartpole with Markov Decision Processes (MDPs)/0203.Using QLConfiguration to Configure Our Model.mp4 9.9 MB
  • 05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0502.Setting Up A3C Learning Environment.mp4 9.0 MB
  • 05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0501.Understanding Asynchronous Advantage Actor-Critic Technique(A3C).mp4 6.3 MB
  • 01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0104.Best Use Cases of Reinforcement Learning.mp4 6.0 MB
  • 02.Solving Cartpole with Markov Decision Processes (MDPs)/0201.Understanding Cartpole Problem.mp4 5.6 MB
  • 04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0401.Understanding Stock Prediction Problem.mp4 5.4 MB
  • 03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0302.Analyzing Possible Scenarios That Our Program Can Solve.mp4 3.2 MB
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