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1. Distributional Deep Q-Networks.html
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HTML
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102.4 B
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1. Dueling Deep Q-Networks.html
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HTML
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102.4 B
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1. Hyperparameter tuning with Optuna.mp4
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MP4
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32.4 MB
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1. Hyperparameter tuning with Optuna.srt
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SRT
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11 KB
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1. Introduction.mp4
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MP4
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32.4 MB
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1. Introduction.mp4.jpg
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JPG
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174.8 KB
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1. Maximization bias and Double Deep Q-Learning.mp4
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MP4
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13.8 MB
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1. Module overview.mp4
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MP4
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1.3 MB
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1. Module overview.srt
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SRT
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614.4 B
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1. N-step Deep Q-Learning.html
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HTML
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102.4 B
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1. Noisy Deep Q-Networks.html
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HTML
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102.4 B
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1. Prioritized Experience Replay.html
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HTML
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102.4 B
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1. PyTorch Lightning.mp4
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MP4
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32 MB
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1. PyTorch Lightning.srt
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SRT
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10.5 KB
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1.1 Advanced Reinforcement Learning in Python from DQN to SAC.html
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HTML
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102.4 B
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1.2 Reinforcement Learning beginner to master.html
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HTML
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102.4 B
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10. Bellman equations.mp4
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MP4
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12.4 MB
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10. Bellman equations.srt
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SRT
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3.4 KB
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10. Prepare the data loader and the optimizer.mp4
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MP4
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30.4 MB
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10. Prepare the data loader and the optimizer.srt
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SRT
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4.9 KB
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11. Define the train_step() method.mp4
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MP4
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49.8 MB
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11. Define the train_step() method.srt
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SRT
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10.9 KB
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11. Solving a Markov decision process.mp4
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MP4
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14.2 MB
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11. Solving a Markov decision process.srt
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SRT
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3.6 KB
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12. Define the train_epoch_end() method.mp4
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MP4
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32.2 MB
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12. Define the train_epoch_end() method.srt
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SRT
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4.7 KB
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13. Train the Deep Q-Learning algorithm.mp4
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MP4
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35.1 MB
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13. Train the Deep Q-Learning algorithm.srt
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SRT
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7.5 KB
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14. Explore the resulting agent.mp4
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MP4
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20.3 MB
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14. Explore the resulting agent.srt
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SRT
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3.6 KB
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2. Deep Q-Learning.mp4
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MP4
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16.2 MB
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2. Deep Q-Learning.srt
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SRT
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3.4 KB
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2. Elements common to all control tasks.mp4
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MP4
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38.7 MB
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2. Elements common to all control tasks.srt
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SRT
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6.8 KB
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2. Function approximators.mp4
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MP4
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36.3 MB
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2. Function approximators.srt
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SRT
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9.8 KB
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2. Link to the code notebook.html
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HTML
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204.8 B
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2. Reinforcement Learning series.html
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HTML
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409.6 B
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2. Temporal difference methods.mp4
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MP4
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12.6 MB
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2. Temporal difference methods.srt
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SRT
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4.1 KB
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2.1 Google colab.html
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HTML
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204.8 B
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3. Artificial Neural Networks.mp4
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MP4
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24.3 MB
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3. Artificial Neural Networks.srt
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SRT
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4.4 KB
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3. Create the Double Deep Q-Learning algorithm.mp4
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MP4
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49.9 MB
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3. Create the Double Deep Q-Learning algorithm.srt
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SRT
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8.5 KB
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3. Create the dueling DQN.mp4
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MP4
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54.4 MB
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3. Create the dueling DQN.srt
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SRT
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11.7 KB
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3. DQN for visual inputs.mp4
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MP4
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69.1 MB
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3. DQN for visual inputs.srt
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SRT
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15.1 KB
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3. Experience replay.mp4
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MP4
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9 MB
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3. Experience replay.srt
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SRT
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2.5 KB
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3. Google Colab.mp4
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MP4
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5.8 MB
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3. Google Colab.srt
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SRT
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2 KB
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3. Introduction to PyTorch Lightning.mp4
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MP4
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30.9 MB
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3. Introduction to PyTorch Lightning.srt
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SRT
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7 KB
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3. Log average return.mp4
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MP4
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33.6 MB
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3. Log average return.srt
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SRT
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5.6 KB
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3. Solving control tasks with temporal difference method.mp4
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MP4
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14.5 MB
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3. Solving control tasks with temporal difference method.srt
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SRT
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4.1 KB
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3. The Markov decision process (MDP).mp4
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MP4
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25.1 MB
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3. The Markov decision process (MDP).srt
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SRT
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6.4 KB
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4. Artificial Neurons.mp4
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MP4
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25.6 MB
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4. Artificial Neurons.srt
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SRT
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6.6 KB
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4. Check the resulting agent.mp4
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MP4
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9.1 MB
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4. Check the resulting agent.srt
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SRT
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1.7 KB
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4. Create the Deep Q-Network.mp4
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MP4
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22.9 MB
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4. Create the Deep Q-Network.srt
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SRT
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5.9 KB
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4. Create the environment - Part 1.mp4
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MP4
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41.3 MB
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4. Create the environment - Part 1.srt
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SRT
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9 KB
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4. Define the objective function.mp4
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MP4
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29.8 MB
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4. Define the objective function.srt
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SRT
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6.2 KB
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4. Prioritized Experience Repay Buffer.mp4
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MP4
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63.6 MB
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4. Prioritized Experience Repay Buffer.srt
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SRT
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15 KB
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4. Q-Learning.mp4
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MP4
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11.1 MB
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4. Q-Learning.srt
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SRT
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2.9 KB
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4. Target Network.mp4
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MP4
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16.6 MB
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4. Target Network.srt
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SRT
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4.6 KB
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4. Types of Markov decision process.mp4
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MP4
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8.7 MB
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4. Types of Markov decision process.srt
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SRT
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2.4 KB
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4. Where to begin.mp4
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MP4
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4.6 MB
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4. Where to begin.srt
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SRT
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2.1 KB
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5. Advantages of temporal difference methods.mp4
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MP4
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3.7 MB
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5. Advantages of temporal difference methods.srt
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SRT
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1.3 KB
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5. Create and launch the hyperparameter tuning job.mp4
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MP4
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18.5 MB
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5. Create and launch the hyperparameter tuning job.srt
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SRT
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3.2 KB
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5. Create the environment - Part 2.mp4
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MP4
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36.6 MB
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5. Create the environment - Part 2.srt
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SRT
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6.7 KB
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5. Create the environment.mp4
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MP4
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62.6 MB
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5. Create the environment.srt
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SRT
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14 KB
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5. Create the policy.mp4
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MP4
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18 MB
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5. Create the policy.srt
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SRT
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5.7 KB
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5. How to represent a Neural Network.mp4
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MP4
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38.2 MB
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5. How to represent a Neural Network.srt
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SRT
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8.2 KB
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5. Trajectory vs episode.mp4
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MP4
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4.9 MB
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5. Trajectory vs episode.srt
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SRT
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1.2 KB
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6. Create the replay buffer.mp4
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MP4
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23 MB
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6. Create the replay buffer.srt
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SRT
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6.6 KB
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6. Explore the best trial.mp4
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MP4
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19.2 MB
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6. Explore the best trial.srt
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SRT
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3.1 KB
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6. Implement Deep Q-Learning.mp4
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MP4
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36.4 MB
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6. Implement Deep Q-Learning.srt
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SRT
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6.7 KB
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6. Implement the Deep Q-Learning algorithm with Prioritized Experience Replay.mp4
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MP4
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63.3 MB
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6. Implement the Deep Q-Learning algorithm with Prioritized Experience Replay.srt
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SRT
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12.9 KB
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6. Reward vs Return.mp4
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MP4
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5.3 MB
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6. Reward vs Return.srt
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SRT
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1.8 KB
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6. Stochastic Gradient Descent.mp4
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MP4
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49.9 MB
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6. Stochastic Gradient Descent.srt
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SRT
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7.2 KB
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7. Check the resulting agent.mp4
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MP4
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20.9 MB
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7. Check the resulting agent.srt
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SRT
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2.7 KB
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7. Create the environment.mp4
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MP4
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32.2 MB
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7. Create the environment.srt
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SRT
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8.9 KB
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7. Discount factor.mp4
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MP4
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14.8 MB
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7. Discount factor.srt
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SRT
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4.6 KB
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7. Launch the training process.mp4
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MP4
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42.5 MB
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7. Launch the training process.srt
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SRT
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5.8 KB
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7. Neural Network optimization.mp4
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MP4
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23.4 MB
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7. Neural Network optimization.srt
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SRT
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5 KB
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8. Check the resulting agent.mp4
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MP4
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16.8 MB
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8. Check the resulting agent.srt
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SRT
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1.9 KB
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8. Define the class for the Deep Q-Learning algorithm.mp4
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MP4
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54.5 MB
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8. Define the class for the Deep Q-Learning algorithm.srt
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SRT
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13.6 KB
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8. Policy.mp4
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MP4
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7.4 MB
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8. Policy.srt
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SRT
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2.3 KB
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9. Define the play_episode() function.mp4
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MP4
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29.1 MB
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9. Define the play_episode() function.srt
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SRT
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5.5 KB
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9. State values v(s) and action values q(s,a).mp4
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MP4
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4.3 MB
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9. State values v(s) and action values q(s,a).srt
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SRT
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1.3 KB
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Bonus Resources.txt
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TXT
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409.6 B
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Get Bonus Downloads Here.url
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URL
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204.8 B
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