[] [Packt] Hands-On Reinforcement Learning with Java [FCO]
- 收录时间:2021-02-09 19:20:27
- 文件大小:365MB
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- 最近下载:2021-02-09 19:20:27
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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 45MB
- 01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0101.The Course Overview.mp4 25MB
- 03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0304.Configuring RL4J Algorithm for Cliff Walking Problem.mp4 23MB
- Exercise Files/exercise_files.zip 22MB
- 02.Solving Cartpole with Markov Decision Processes (MDPs)/0205.Running Cartpole and Validating Results.mp4 20MB
- 03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0305.Starting QLearningDiscreteDense and Saving Results.mp4 20MB
- 01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0102.Main Principles of Reinforcement Learning.mp4 19MB
- 01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0103.Adding DL4J with RL4J to Our Project.mp4 18MB
- 05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0503.Configuring Reinforcement Learning Program Using A3C Configuration.mp4 15MB
- 04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0403.Leveraging QLearningDiscreteDense from RL4J API.mp4 14MB
- 03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0303.Loading Cliff Walking Simulation.mp4 14MB
- 03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0301.Adding Malmo Library to Our RL4J Project.mp4 14MB
- 05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0504.Using A3C Technique with ActorCriticFactorySeparateStdDense.mp4 13MB
- 02.Solving Cartpole with Markov Decision Processes (MDPs)/0204.Using GymEnv Library from RL4J to Simulate Solution.mp4 13MB
- 01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0105.Configuring Reinforcement Learning Model with QLearning.QLConfiguration.mp4 13MB
- 04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0404.Performing Stock Prediction Training and Validating Results.mp4 12MB
- 04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0402.Creating Configuration for Stock Prediction Learning.mp4 10MB
- 02.Solving Cartpole with Markov Decision Processes (MDPs)/0202.Leveraging Markov Chain in Our Cartpole Solution.mp4 10MB
- 02.Solving Cartpole with Markov Decision Processes (MDPs)/0203.Using QLConfiguration to Configure Our Model.mp4 9MB
- 05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0502.Setting Up A3C Learning Environment.mp4 9MB
- 05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0501.Understanding Asynchronous Advantage Actor-Critic Technique(A3C).mp4 6MB
- 01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0104.Best Use Cases of Reinforcement Learning.mp4 6MB
- 02.Solving Cartpole with Markov Decision Processes (MDPs)/0201.Understanding Cartpole Problem.mp4 5MB
- 04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0401.Understanding Stock Prediction Problem.mp4 5MB
- 03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0302.Analyzing Possible Scenarios That Our Program Can Solve.mp4 3MB
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