FreeCoursesOnline Me Packt Hands-On Reinforcement Learning with Java FCO

1. 01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0101.The Course Overview.mp425.35MB
2. 01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0102.Main Principles of Reinforcement Learning.mp419.44MB
3. 01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0103.Adding DL4J with RL4J to Our Project.mp418.26MB
4. 01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0104.Best Use Cases of Reinforcement Learning.mp45.74MB
5. 01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0105.Configuring Reinforcement Learning Model with QLearning.QLConfiguration.mp412.89MB
6. 02.Solving Cartpole with Markov Decision Processes (MDPs)/0201.Understanding Cartpole Problem.mp45.38MB
7. 02.Solving Cartpole with Markov Decision Processes (MDPs)/0202.Leveraging Markov Chain in Our Cartpole Solution.mp410.06MB
8. 02.Solving Cartpole with Markov Decision Processes (MDPs)/0203.Using QLConfiguration to Configure Our Model.mp49.45MB
9. 02.Solving Cartpole with Markov Decision Processes (MDPs)/0204.Using GymEnv Library from RL4J to Simulate Solution.mp412.93MB
10. 02.Solving Cartpole with Markov Decision Processes (MDPs)/0205.Running Cartpole and Validating Results.mp420.16MB
11. 03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0301.Adding Malmo Library to Our RL4J Project.mp413.76MB
12. 03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0302.Analyzing Possible Scenarios That Our Program Can Solve.mp43.04MB
13. 03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0303.Loading Cliff Walking Simulation.mp414.03MB
14. 03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0304.Configuring RL4J Algorithm for Cliff Walking Problem.mp423.49MB
15. 03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0305.Starting QLearningDiscreteDense and Saving Results.mp419.87MB
16. 04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0401.Understanding Stock Prediction Problem.mp45.14MB
17. 04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0402.Creating Configuration for Stock Prediction Learning.mp410.21MB
18. 04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0403.Leveraging QLearningDiscreteDense from RL4J API.mp414.03MB
19. 04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0404.Performing Stock Prediction Training and Validating Results.mp411.99MB
20. 05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0501.Understanding Asynchronous Advantage Actor-Critic Technique(A3C).mp45.97MB
21. 05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0502.Setting Up A3C Learning Environment.mp48.57MB
22. 05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0503.Configuring Reinforcement Learning Program Using A3C Configuration.mp415.39MB
23. 05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0504.Using A3C Technique with ActorCriticFactorySeparateStdDense.mp413.25MB
24. 05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0505.Starting Program and Gathering Results.mp445.27MB