01. Fundamentals of Reinforcement Learning
02. A Guide to the Gym Toolkit
03. Bellman Equation and Dynamic Programming
05. Understanding Temporal Difference Learning
06. Case Study: The MAB Problem
07. Deep learning foundations
08. A primer on TensorFlow
8.05 Handwritten digits classification using TensorFlow.ipynb
8.08 Math operations in TensorFlow.ipynb
8.10 MNIST digits classification in TensorFlow 2.0.ipynb
09. Deep Q Network and its Variants
10. Policy Gradient Method
11. Actor Critic Methods - A2C and A3C
12. Learning DDPG, TD3 and SAC
13. TRPO, PPO and ACKTR Methods
14. Distributional Reinforcement Learning
15. Imitation Learning and Inverse RL
16. Deep Reinforcement Learning with Stable Baselines
17. Reinforcement Learning Frontiers
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8.1. What is TensorFlow?
8.2. Understanding Computational Graphs and Sessions
8.2.1 Computational Graphs
8.2.2 Sessions
8.3. Variables, Constants, and Placeholders
8.3.1. Variables
8.3.2. Constants
8.3.3. Placeholders and Feed Dictionaries
8.4. Introducing TensorBoard
8.4.1 Creating Name Scope in TensorBoard
8.5. Handwritten digits classification using Tensorflow
8.6. Visualizing Computational graph in TensorBord
8.7. Introducing Eager execution
8.8. Math operations in TensorFlow
8.9. Tensorflow 8.0 and Keras
8.9.1. Bonjour Keras
8.9.2. Defining models in Keras
8.9.3. Compiling the model
8.9.4. Training the model
8.9.5. Evaluating the model
8.10. MNIST digits classification in Tensorflow 2.0
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