rl-paper-study/1st at main · utilForever/rl-paper-study · GitHub

Name		Name	Last commit message	Last commit date
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200511 - Dueling Network Architectures for Deep Reinforcement Learning, Wang et al, 2015.pdf		200511 - Dueling Network Architectures for Deep Reinforcement Learning, Wang et al, 2015.pdf
200511 - Playing Atari with Deep Reinforcement Learning, Mnih et al, 2013.pdf		200511 - Playing Atari with Deep Reinforcement Learning, Mnih et al, 2013.pdf
200525 - Asynchronous Methods for Deep Reinforcement Learning, Mnih et al, 2016.pdf		200525 - Asynchronous Methods for Deep Reinforcement Learning, Mnih et al, 2016.pdf
200525 - Deep Reinforcement Learning with Double Q-learning, Hasselt et al 2015.pdf		200525 - Deep Reinforcement Learning with Double Q-learning, Hasselt et al 2015.pdf
200601 - Continuous Control With Deep Reinforcement Learning, Lillicrap et al, 2015.pdf		200601 - Continuous Control With Deep Reinforcement Learning, Lillicrap et al, 2015.pdf
200601 - Mastering the game of Go with deep neural networks and tree search, D. Silver et al, Nature, 2016.pdf		200601 - Mastering the game of Go with deep neural networks and tree search, D. Silver et al, Nature, 2016.pdf
200608 - Curiosity-driven Exploration by Self-supervised Prediction, Pathak et al, 2017.pdf		200608 - Curiosity-driven Exploration by Self-supervised Prediction, Pathak et al, 2017.pdf
200608 - Mastering the game of Go without human knowledge, D. Silver et al, Nature, 2017.pdf		200608 - Mastering the game of Go without human knowledge, D. Silver et al, Nature, 2017.pdf
200622 - Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model, J. Schrittwieser et al, 2019.pdf		200622 - Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model, J. Schrittwieser et al, 2019.pdf
200629 - Contextual Decision Processes with low Bellman rank are PAC-Learnable, N. Jiang et al, 2017.pdf		200629 - Contextual Decision Processes with low Bellman rank are PAC-Learnable, N. Jiang et al, 2017.pdf
200629 - Evolution Strategies as a Scalable Alternative to Reinforcement Learning, Salimans et al, 2017.pdf		200629 - Evolution Strategies as a Scalable Alternative to Reinforcement Learning, Salimans et al, 2017.pdf
200629 - QT-Opt Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation, Kalashnikov et al, 2018.pdf		200629 - QT-Opt Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation, Kalashnikov et al, 2018.pdf
README.md		README.md

README.md

1st Study Paper List

Date	Paper	Presenter	Links
5/11	Playing Atari with Deep Reinforcement Learning, Mnih et al, 2013.	Ingyun Ahn	[paper] [review]
5/11	Dueling Network Architectures for Deep Reinforcement Learning, Wang et al, 2015.	Jaeyoung Ahn	[paper] [review]
5/25	Deep Reinforcement Learning with Double Q-learning, Hasselt et al 2015.	Do-Hoon Kim	[paper] [review]
5/25	Asynchronous Methods for Deep Reinforcement Learning, Mnih et al, 2016.	Seungyoun Shin	[paper] [review]
6/1	Continuous Control With Deep Reinforcement Learning, Lillicrap et al, 2015.	Chris Ohk	[paper] [review]
6/1	Mastering the game of Go with deep neural networks and tree search, D. Silver et al, Nature, 2016.	Minseok Seong	[paper] [review]
6/8	Curiosity-driven Exploration by Self-supervised Prediction, Pathak et al, 2017.	Haneul Choi	[paper] [review]
6/8	Mastering the game of Go without human knowledge, D. Silver et al, Nature, 2017.	Donggu Kang	[paper] [review]
6/22	Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model, J. Schrittwieser et al, 2019.	Yunhyeok Kwak	[paper] [review]
6/29	Contextual Decision Processes with low Bellman rank are PAC-Learnable, N. Jiang et al, 2017.	Hoesung Ryu	[paper] [review]
6/29	Evolution Strategies as a Scalable Alternative to Reinforcement Learning, Salimans et al, 2017.	Chanhyuk Park	[paper] [review]
6/29	QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation, Kalashnikov et al, 2018.	Hyecheol (Jerry) Jang	[paper] [review]

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