Study Log (2020.07)

2020-07-23

• S-K RL
  • train_FT10_ppo_node_only.py
    • pyjssp > benchmarks.py
    • pyjssp > simulators.py
    • pyjssp > jobShopSamplers.py
    • pyjssp > operationHelpers.py
    • src > node_only_agents.py
    • src/agents.py
    • src/rl_networks.py
    • torch > linear.py
    • src/training_utils.py
    • src/utils.py
    • src > nn.py

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2020-07-10

• S-K RL
  • train_FT10_ppo_node_only.py
    • pyjssp > benchmarks.py
    • pyjssp > simulators.py
    • pyjssp > jobShopSamplers.py
    • pyjssp > operationHelpers.py
    • src > node_only_agents.py
    • src/agents.py
    • src/rl_networks.py
    • torch > linear.py
    • src/training_utils.py
    • src/utils.py
    • src > nn.py

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2020-07-09

• 팡요랩
  • 강화학습 5강 - Model Free Control

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2020-07-08

• 팡요랩
  • 강화학습 4강 - Model Free Prediction

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2020-07-07

• 팡요랩
  • 강화학습 3강 - Planning by Dynamic Programming

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2020-07-06

• 팡요랩
  • 강화학습 1강 - 강화학습 introduction
  • 강화학습 2강 - Markov Decision Process

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2020-07-02

• 팡요랩
  • 강화학습 10강 - Classic Games

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2020-07-01

• 팡요랩
  • 강화학습 9강 - Exploration and Exploitation

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Template

• Fundamental of Reinforcement Learning
  • Chapter #.
• 모두를 위한 머신러닝/딥러닝 강의
  • Lecture #.
• UCL Course on RL
  • Lecture #.
• Reinforcement Learning
  • Page #.
• 팡요랩
  • 강화학습 1강 - 강화학습 introduction
  • 강화학습 2강 - Markov Decision Process
  • 강화학습 3강 - Planning by Dynamic Programming
  • 강화학습 4강 - Model Free Prediction
  • 강화학습 5강 - Model Free Control
  • 강화학습 6강 - Value Function Approximation
  • 강화학습 7강 - Policy Gradient
  • 강화학습 8강 - Integrating Learning and Planning
  • 강화학습 9강 - Exploration and Exploitation
  • 강화학습 10강 - Classic Games
• Pattern Recognition & Machine Learning
• S-K RL
• multi_step_actor
• Stanford Spring 2020
  • CS229
    • Lecture #.
  • CS230
    • Lecture #.
  • CS231n
    • Lecture #.