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
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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17. Reinforcement Learning Frontiers
17.1. Meta Reinforcement Learning
17.2. Model Agnostic Meta Learning
17.3. Understanding MAML
17.4. MAML in the Supervised Learning Setting
17.5. Algorithm - MAML in Supervised Learning
17.6. MAML in the Reinforcement Learning Setting
17.7. Algorithm - MAML in Reinforcement Learning
17.8. Hierarchical Reinforcement Learning
17.9. MAXQ Value Function Decomposition
17.10. Imagination Augmented Agents
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