Reinforcement Learning paper review study
| Date | Paper | Presenter | Links |
|---|---|---|---|
| 5/11 | Playing Atari with Deep Reinforcement Learning, Mnih et al, 2013. | Ingyun Ahn | [paper] [review] [code] |
| 5/11 | Dueling Network Architectures for Deep Reinforcement Learning, Wang et al, 2015. | Jaeyoung Ahn | [paper] [review] [code] |
| 5/25 | Deep Reinforcement Learning with Double Q-learning, Hasselt et al 2015. | Do-Hoon Kim | [paper] [review] [code] |
| 5/25 | Asynchronous Methods for Deep Reinforcement Learning, Mnih et al, 2016. | Seungyoun Shin | [paper] [review] [code] |
| 6/1 | Continuous Control With Deep Reinforcement Learning, Lillicrap et al, 2015. | Chris Ohk | [paper] [review] [code] |
| 6/1 | Mastering the game of Go with deep neural networks and tree search, D. Silver et al, Nature, 2016. | Minseok Seong | [paper] [review] [code] |
| 6/8 | Curiosity-driven Exploration by Self-supervised Prediction, Pathak et al, 2017. | Haneul Choi | [paper] [review] [code] |
| 6/8 | Mastering the game of Go without human knowledge, D. Silver et al, Nature, 2017. | Donggu Kang | [paper] [review] [code] |
| 6/15 | Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model, J. Schrittwieser et al, 2019. | Yunhyeok Kwak | [paper] [review] [code] |
| 6/15 | Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches, Wen Sun et al, 2019. | Hoesung Ryu | [paper] [review] [code] |
| 6/22 | Evolution Strategies as a Scalable Alternative to Reinforcement Learning, Salimans et al, 2017. | Chanhyuk Park | [paper] [review] [code] |
| 6/22 | QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation, Kalashnikov et al, 2018. | Hyecheol (Jerry) Jang | [paper] [review] [code] |
You can contact me via e-mail (utilForever at gmail.com). I am always happy to answer questions or help with any issues you might have, and please be sure to share any additional work or your creations with me, I love seeing what other people are making.
The class is licensed under the MIT License:
Copyright (c) 2020 RL Paper Study Team
- Chris Ohk
- Yunhyeok Kwak
- [Ingyun Ahn]
- [Jaeyoung Ahn]
- Do-Hoon Kim
- [Seungyoun Shin]
- [Haneul Choi]
- Minsuk Sung
- [Donggu Kang]
- [Hoesung Ryu]
- [Chanhyuk Park]
- Hyecheol (Jerry) Jang
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