[FreeCoursesOnline.Me] [Packt] Hands-On Reinforcement Learning with Java [FCO]
| 文件类型 | 收录时间 | 最后活跃 | 资源热度 | 文件大小 | 文件数量 |
|---|---|---|---|---|---|
| 视频 | 2019-10-20 02:38 | 2024-12-20 21:08 | 198 | 343.64 MB | 24 |
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文件列表
- 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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