09.Machine learning12. Reinforcement learning - sporedata/researchdesigneR GitHub Wiki

1. Use cases: in which situations should I use this method?

2. Input: what kind of data does the method require?

3. Algorithm: how does the method work?

Model mechanics

Reporting guidelines

Data science packages

Suggested companion methods

Learning materials

  1. Books
  2. Articles

4. Output: how do I interpret this method's results?

Mock conclusions or most frequent format for conclusions reached at the end of a typical analysis.

Tables, plots, and their interpretation

5. SporeData-specific

Templates

Data science functions

References

[1] Brandon Brown, Alexander Zai. Deep Reinforcement Learning in Action. [2] Maxim Lapan. Deep Reinforcement Learning Hands-On. [3] Enes Bilgin. Mastering Reinforcement Learning with Python - Build next-generation, self-learning models using reinforcement learning techniques and best practices. [4] Laura Graesser, Wah Loon Keng. Foundations of Deep Reinforcement Learning - Theory and Practce in Python. [5] Hao Dong, Zihan Ding, Shanghang Zhang. Deep Reinforcement Learning Fundamentals, Research and Applications. [6] Hao Dong, Zihan Ding, Shanghang Zhang. Deep Reinforcement Learning Fundamentals, Research and Applications. [7] Miguel Morales. grokking Deep Reinforcement Learning. [8] Boris Belousov, Hany Abdulsamad, Pascal Klink, Simone Parisi and Jan Peters. Reinforcement Learning Algorithms: Analysis and Applications. [9] Reinforcement Learning in R.pdf. [10] Alessandro Palmas, Emanuele Ghelfi, Dr. Alexandra Galina Petre, Mayur Kulkarni, Anand N.S., Quan Nguyen, Aritra Sen, Anthony So, and Saikat Basak. The ReinforcementLearningWorkshop. [11] rl_breast_cancer_screening.pdf. [12] Sudharsan Ravichandiran. Deep Reinforcement Learning with Python.

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