01. Fundamentals of Reinforcement Learning
02. A Guide to the Gym Toolkit
03. Bellman Equation and Dynamic Programming
3.06. Solving the Frozen Lake Problem with Value Iteration.ipynb
3.08. Solving the Frozen Lake Problem with Policy Iteration.ipynb
05. Understanding Temporal Difference Learning
06. Case Study: The MAB Problem
07. Deep learning foundations
08. A primer on TensorFlow
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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3. Bellman Equation and Dynamic Programming
3.1. The Bellman Equation
3.1.1. Bellman Equation of the Value Function
3.1.2. Bellman Equation of the Q Function
3.2. Bellman Optimality Equation
3.3. Relation Between Value and Q Function
3.4. Dynamic Programming
3.5. Value Iteration
3.5.1. Algorithm - Value Iteration
3.6. Solving the Frozen Lake Problem with Value Iteration
3.7. Policy iteration
3.7.1. Algorithm - Policy iteration
3.8. Solving the Frozen Lake Problem with Policy Iteration
3.9. Is DP Applicable to all Environments?
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