种子简介
种子名称：
[FreeCoursesOnline.Me] [Packt] Hands-On Reinforcement Learning with Java [FCO]
文件类型：
视频
文件数目：
24个文件
文件大小：
343.64 MB
收录时间：
2019-10-20 02:38
已经下载：
3次
资源热度：
194
最近下载：
2024-12-7 21:34
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种子包含的文件
[FreeCoursesOnline.Me] [Packt] Hands-On Reinforcement Learning with Java [FCO].torrent
01.Deep Dive into Reinforcement Learning with DL4J RL4J/0101.The Course Overview.mp425.35MB
01.Deep Dive into Reinforcement Learning with DL4J RL4J/0102.Main Principles of Reinforcement Learning.mp419.44MB
01.Deep Dive into Reinforcement Learning with DL4J RL4J/0103.Adding DL4J with RL4J to Our Project.mp418.26MB
01.Deep Dive into Reinforcement Learning with DL4J RL4J/0104.Best Use Cases of Reinforcement Learning.mp45.74MB
01.Deep Dive into Reinforcement Learning with DL4J RL4J/0105.Configuring Reinforcement Learning Model with QLearning.QLConfiguration.mp412.89MB
02.Solving Cartpole with Markov Decision Processes (MDPs)/0201.Understanding Cartpole Problem.mp45.38MB
02.Solving Cartpole with Markov Decision Processes (MDPs)/0202.Leveraging Markov Chain in Our Cartpole Solution.mp410.06MB
02.Solving Cartpole with Markov Decision Processes (MDPs)/0203.Using QLConfiguration to Configure Our Model.mp49.45MB
02.Solving Cartpole with Markov Decision Processes (MDPs)/0204.Using GymEnv Library from RL4J to Simulate Solution.mp412.93MB
02.Solving Cartpole with Markov Decision Processes (MDPs)/0205.Running Cartpole and Validating Results.mp420.16MB
03.Using Project Malmo Reinforcement Learning Leveraging Dynamic Programming/0301.Adding Malmo Library to Our RL4J Project.mp413.76MB
03.Using Project Malmo Reinforcement Learning Leveraging Dynamic Programming/0302.Analyzing Possible Scenarios That Our Program Can Solve.mp43.04MB
03.Using Project Malmo Reinforcement Learning Leveraging Dynamic Programming/0303.Loading Cliff Walking Simulation.mp414.03MB
03.Using Project Malmo Reinforcement Learning Leveraging Dynamic Programming/0304.Configuring RL4J Algorithm for Cliff Walking Problem.mp423.49MB
03.Using Project Malmo Reinforcement Learning Leveraging Dynamic Programming/0305.Starting QLearningDiscreteDense and Saving Results.mp419.87MB
04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0401.Understanding Stock Prediction Problem.mp45.14MB
04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0402.Creating Configuration for Stock Prediction Learning.mp410.21MB
04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0403.Leveraging QLearningDiscreteDense from RL4J API.mp414.03MB
04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0404.Performing Stock Prediction Training and Validating Results.mp411.99MB
05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0501.Understanding Asynchronous Advantage Actor-Critic Technique(A3C).mp45.97MB
05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0502.Setting Up A3C Learning Environment.mp48.57MB
05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0503.Configuring Reinforcement Learning Program Using A3C Configuration.mp415.39MB
05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0504.Using A3C Technique with ActorCriticFactorySeparateStdDense.mp413.25MB
05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0505.Starting Program and Gathering Results.mp445.27MB
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