FreeCoursesOnline Me Packt Hands On Reinforcement Learning with Java FCO

/03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0305.Starting QLearningDiscreteDense and Saving Results.mp4

/03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0303.Loading Cliff Walking Simulation.mp4

/03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0301.Adding Malmo Library to Our RL4J Project.mp4

/03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0304.Configuring RL4J Algorithm for Cliff Walking Problem.mp4

/03.Using Project Malmo – Reinforcement Learning Leveraging Dynamic Programming/0302.Analyzing Possible Scenarios That Our Program Can Solve.mp4

/01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0102.Main Principles of Reinforcement Learning.mp4

/01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0103.Adding DL4J with RL4J to Our Project.mp4

/05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0505.Starting Program and Gathering Results.mp4

/01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0104.Best Use Cases of Reinforcement Learning.mp4

/01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0101.The Course Overview.mp4

/04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0403.Leveraging QLearningDiscreteDense from RL4J API.mp4

/02.Solving Cartpole with Markov Decision Processes (MDPs)/0205.Running Cartpole and Validating Results.mp4

/01.Deep Dive into Reinforcement Learning with DL4J – RL4J/0105.Configuring Reinforcement Learning Model with QLearning.QLConfiguration.mp4

/05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0502.Setting Up A3C Learning Environment.mp4

/04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0404.Performing Stock Prediction Training and Validating Results.mp4

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