rl-paper-study/10th at main · utilForever/rl-paper-study · GitHub

Name		Name	Last commit message	Last commit date
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230227 - LECO, Learnable Episodic Count for Task-Specific Intrinsic Reward, D. Jo et al, 2022.pdf		230227 - LECO, Learnable Episodic Count for Task-Specific Intrinsic Reward, D. Jo et al, 2022.pdf
230306 - SUNRISE, A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning, K. Lee et al, 2020.pdf		230306 - SUNRISE, A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning, K. Lee et al, 2020.pdf
230313 - Deep Reinforcement Learning that Matters, P. Henderson et al, 2017.pdf		230313 - Deep Reinforcement Learning that Matters, P. Henderson et al, 2017.pdf
230320 - Know Your Action Set, Learning Action Relations for Reinforcement Learning, A. Jain et al, 2022.pdf		230320 - Know Your Action Set, Learning Action Relations for Reinforcement Learning, A. Jain et al, 2022.pdf
230320 - Same State, Different Task, Continual Reinforcement Learning without Interference, S. Kessler et al, 2021.pdf		230320 - Same State, Different Task, Continual Reinforcement Learning without Interference, S. Kessler et al, 2021.pdf
230327 - Aligning Text-to-Image Models using Human Feedback, K. Lee et al, 2023.pdf		230327 - Aligning Text-to-Image Models using Human Feedback, K. Lee et al, 2023.pdf
230327 - Deep Reinforcement Learning from Human Preferences, P. Christiano et al, 2017.pdf		230327 - Deep Reinforcement Learning from Human Preferences, P. Christiano et al, 2017.pdf
230403 - Human-Level Play in the Game of Diplomacy by Combining Language Models with Strategic Reasoning, Meta Fundamental AI Research Diplomacy Team, 2022.pdf		230403 - Human-Level Play in the Game of Diplomacy by Combining Language Models with Strategic Reasoning, Meta Fundamental AI Research Diplomacy Team, 2022.pdf
230403 - QMIX, Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning, T. Rashid et al, 2018.pdf		230403 - QMIX, Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning, T. Rashid et al, 2018.pdf
230410 - Hierarchically and Cooperatively Learning Traffic Signal Control, B. Xu et al, 2021.pdf		230410 - Hierarchically and Cooperatively Learning Traffic Signal Control, B. Xu et al, 2021.pdf
230410 - Stochastic Latent Actor-Critic, Deep Reinforcement Learning with a Latent Variable Model, AX. Lee et al, 2019.pdf		230410 - Stochastic Latent Actor-Critic, Deep Reinforcement Learning with a Latent Variable Model, AX. Lee et al, 2019.pdf
230417 - A Simple Neural Attentive Meta-Learner, N. Mishra et al, 2017.pdf		230417 - A Simple Neural Attentive Meta-Learner, N. Mishra et al, 2017.pdf
230417 - Dynamic Multi-Objective Scheduling for Flexible Job Shop by Deep Reinforcement Learning, S. Luo et al, 2021.pdf		230417 - Dynamic Multi-Objective Scheduling for Flexible Job Shop by Deep Reinforcement Learning, S. Luo et al, 2021.pdf
README.md		README.md

README.md

10th Study Paper List

Date	Paper	Presenter	Links
2/27	LECO: Learnable Episodic Count for Task-Specific Intrinsic Reward, D. Jo et al, 2022.	Daejin Jo	[paper] [review]
3/6	SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning, K. Lee et al, 2020.	Dongjin Lee	[paper] [review]
3/13	Deep Reinforcement Learning that Matters, P. Henderson et al, 2017.	Sungkwon On	[paper] [review]
3/20	Same State, Different Task: Continual Reinforcement Learning without Interference, S. Kessler et al, 2021.	Yerin Min	[paper] [review]
3/20	Know Your Action Set: Learning Action Relations for Reinforcement Learning, A. Jain et al, 2022.	Seonghak Hong	[paper] [review]
3/27	Deep Reinforcement Learning from Human Preferences, P. Christiano et al, 2017.	Dokyoon Yoon	[paper] [review]
3/27	Aligning Text-to-Image Models using Human Feedback, K. Lee et al, 2023.	Hyeonhoon Lee	[paper] [review]
4/3	Human-Level Play in the Game of Diplomacy by Combining Language Models with Strategic Reasoning, Meta Fundamental AI Research Diplomacy Team, 2022.	Yangjae Lee	[paper] [review]
4/3	QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning, T. Rashid et al, 2018.	Woosung Park	[paper] [review]
4/10	Hierarchically and Cooperatively Learning Traffic Signal Control, B. Xu et al, 2021.	Seongjin Lee	[paper] [review]
4/10	Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model, AX. Lee et al, 2019.	Seungeon Baek	[paper] [review]
4/17	Dynamic Multi-Objective Scheduling for Flexible Job Shop by Deep Reinforcement Learning, S. Luo et al, 2021.	Yonghae Kim	[paper] [review]
4/17	A Simple Neural Attentive Meta-Learner, N. Mishra et al, 2017.	Donggu Kang	[paper] [review]

Study Member