Files

01. Fundamentals of Reinforcement Learning
02. A Guide to the Gym Toolkit
03. Bellman Equation and Dynamic Programming
04. Monte Carlo Methods
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
- .ipynb_checkpoints
- 14.03. Playing Atari games using Categorical DQN.ipynb
- README.md
15. Imitation Learning and Inverse RL
16. Deep Reinforcement Learning with Stable Baselines
17. Reinforcement Learning Frontiers
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pdf
.DS_Store
README.md

14. Distributional Reinforcement Learning

Oct 2, 2020

ce93744 · Oct 2, 2020

Name	Name	Last commit message	Last commit date
parent directory ..
.ipynb_checkpoints	.ipynb_checkpoints	update	Oct 2, 2020
14.03. Playing Atari games using Categorical DQN.ipynb	14.03. Playing Atari games using Categorical DQN.ipynb	update	Oct 2, 2020
README.md	README.md	update	Oct 2, 2020

14.1. Why Distributional Reinforcement Learning?
14.2. Categorical DQN
- 14.2.1. Predicting Value Distribution
- 14.2.2. Selecting Action Based on the Value Distribution
- 14.2.3. Training the Categorical DQN
- 14.2.4. Projection Step
- 14.2.5. Putting it all Rogether
- 14.2.6. Algorithm - Categorical DQN
14.3. Playing Atari games using Categorical DQN
14.4. Quantile Regression DQN
14.5. Math Essentials
- 14.5.1. Quantile
- 14.5.2. Inverse CDF (Quantile function)
14.6. Understanding QR-DQN
- 14.6.1. Action Selection
- 14.6.2. Loss Function
14.7. Distributed Distributional DDPG
- 14.7.1 Critic Network
- 14.7.2. Actor Network
- 14.7.3. Algorithm - D4PG