sive2045/rl_study

Reinforcement Learning Study 📕

This repository to get used to reinforcement learning.
Goals: Using RL for my research.

What I'm currently studying 👀

• Tutorials of RL[3], 2022-02-12 ~
  • Ch1. Introduction 2022-02-12 ~ 2022-02-17
  • Ch2. Multi-armed Bandits 2022-02-17 ~ 2022-02-23
  • Ch3. Finite Markov Decision Processes(MDP) 2022-02-24 ~ 2022-03-03
  • Ch4. Dynamic Programming(DP) 2022-03-05 ~ 2022-03-14
  • Ch5. Monte Carlo Methods(MC) 2022-03-15 ~ 2022-03-30
  • Ch6. Temporal-Difference(DF) Learning 2022-03-30 ~ 2022-04-14

List of papers I've completed ✨

• [4] 2022-04-07 ~ 2022-04-14

ENV

python==3.6
numpy
matplotlib
seaborn
tqdm

REFERNCE

• [1] N. C. Luong et al., "Applications of Deep Reinforcement Learning in Communications and Networking: A Survey," in IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3133-3174, Fourthquarter 2019, doi: 10.1109/COMST.2019.2916583.
• [2] Maxim Lapan, Deep Reinforcement Learning Hands-On Second Edition, Packt(GCO Science)(2020), git link
• [3] Richard S. Sutton, Reinforcement Learning, 2/E, MIT Press, Cambridge, MA, 2018, git, 📖
• [4] S. Wang, H. Liu, P. H. Gomes and B. Krishnamachari, "Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks," in IEEE Transactions on Cognitive Communications and Networking, vol. 4, no. 2, pp. 257-265, June 2018, doi: 10.1109/TCCN.2018.2809722.