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Reinforcement Learning: 2020-12-14 2031-01-01
arXiv is a repository of electronic preprints approved for posting after moderation, but not full peer review. It consists of scientific papers in the fields of mathematics, physics, astronomy, electrical engineering, computer science, quantitative biology, statistics, mathematical finance and economics, which can be accessed online.
Many machine learning articles will be posted on arXiv before publication. In theoretical computer science and machine learning, over 60% of published papers have arXiv e-prints (Sutton et al. 2017).
Reinforcement learning (RL) is a subfield of machine learning, based on rewarding desired behaviours and/or punishing undesired ones of an agent interacting with its environment (Sutton and Barto, 2014).
How many Reinforcement Learning e-prints will be published on arXiv over the 2020-12-14 to 2031-01-01?
This question resolves as the total number of Reinforcement Learning e-prints published on arXiv over the 2020-12-14 to 2031-01-01 period (inclusive), as per the e-print's "original submission date".
Details of the search query
For the purpose of this question, Reinforcement Learning e-prints are those published under Computer Science that contain any of the following key words in "all fields":
"Reinforcement Learning", "DQN", "Q-learning", "Deep Q Network", "Temporal difference learning", "Sarsa", "TD learning" "Proximal policy optimization"
The query should include cross-listed papers (papers listed on other subjects besides Computer Science). The query may be executed here.
Running this query for previous years gives:
- 779 for the calendar year 2017
- 1,404 for the calendar year 2018
- 2,287 for the calendar year 2019
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