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| 01.Deep Dive into Reinforcement Learning with DL4J RL4J/0101.The Course Overview.mp4 | 25.35 MB |
| 01.Deep Dive into Reinforcement Learning with DL4J RL4J/0102.Main Principles of Reinforcement Learning.mp4 | 19.44 MB |
| 01.Deep Dive into Reinforcement Learning with DL4J RL4J/0103.Adding DL4J with RL4J to Our Project.mp4 | 18.26 MB |
| 01.Deep Dive into Reinforcement Learning with DL4J RL4J/0104.Best Use Cases of Reinforcement Learning.mp4 | 5.74 MB |
| 01.Deep Dive into Reinforcement Learning with DL4J RL4J/0105.Configuring Reinforcement Learning Model with QLearning.QLConfiguration.mp4 | 12.89 MB |
| 02.Solving Cartpole with Markov Decision Processes (MDPs)/0201.Understanding Cartpole Problem.mp4 | 5.38 MB |
| 02.Solving Cartpole with Markov Decision Processes (MDPs)/0202.Leveraging Markov Chain in Our Cartpole Solution.mp4 | 10.06 MB |
| 02.Solving Cartpole with Markov Decision Processes (MDPs)/0203.Using QLConfiguration to Configure Our Model.mp4 | 9.45 MB |
| 02.Solving Cartpole with Markov Decision Processes (MDPs)/0204.Using GymEnv Library from RL4J to Simulate Solution.mp4 | 12.93 MB |
| 02.Solving Cartpole with Markov Decision Processes (MDPs)/0205.Running Cartpole and Validating Results.mp4 | 20.16 MB |
| 03.Using Project Malmo Reinforcement Learning Leveraging Dynamic Programming/0301.Adding Malmo Library to Our RL4J Project.mp4 | 13.76 MB |
| 03.Using Project Malmo Reinforcement Learning Leveraging Dynamic Programming/0302.Analyzing Possible Scenarios That Our Program Can Solve.mp4 | 3.04 MB |
| 03.Using Project Malmo Reinforcement Learning Leveraging Dynamic Programming/0303.Loading Cliff Walking Simulation.mp4 | 14.03 MB |
| 03.Using Project Malmo Reinforcement Learning Leveraging Dynamic Programming/0304.Configuring RL4J Algorithm for Cliff Walking Problem.mp4 | 23.49 MB |
| 03.Using Project Malmo Reinforcement Learning Leveraging Dynamic Programming/0305.Starting QLearningDiscreteDense and Saving Results.mp4 | 19.87 MB |
| 04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0401.Understanding Stock Prediction Problem.mp4 | 5.14 MB |
| 04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0402.Creating Configuration for Stock Prediction Learning.mp4 | 10.21 MB |
| 04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0403.Leveraging QLearningDiscreteDense from RL4J API.mp4 | 14.03 MB |
| 04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0404.Performing Stock Prediction Training and Validating Results.mp4 | 11.99 MB |
| 05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0501.Understanding Asynchronous Advantage Actor-Critic Technique(A3C).mp4 | 5.97 MB |
| 05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0502.Setting Up A3C Learning Environment.mp4 | 8.57 MB |
| 05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0503.Configuring Reinforcement Learning Program Using A3C Configuration.mp4 | 15.39 MB |
| 05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0504.Using A3C Technique with ActorCriticFactorySeparateStdDense.mp4 | 13.25 MB |
| 05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0505.Starting Program and Gathering Results.mp4 | 45.27 MB |
| Exercise Files/exercise_files.zip | 21.71 MB |
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