One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning
Pedro Cisneros-Velarde, Boxiang Lyu, Sanmi Koyejo, Mladen Kolar · arXiv: 2205.15891v3 · Summary page
A press kit for every paper: arXiv, journals and preprints. What is really new, a hype check (peer review, sample size, claims that go beyond the results), key sentences quoted word for word, questions to ask the authors, researchers who work on the same topic but did not sign the paper, and a glossary.