One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning

MLA

Cisneros-Velarde, Pedro, et al. One Policy Is Enough: Parallel Exploration with a Single Policy Is Near-Optimal for Reward-Free Reinforcement Learning. 2022. EBSCOhost, widgets.ebscohost.com/prod/customlink/proxify/proxify.php?count=1&encode=0&proxy=&find_1=&replace_1=&target=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsarx&AN=edsarx.2205.15891&authtype=sso&custid=ns315887.

APA

Cisneros-Velarde, P., Lyu, B., Koyejo, S., & Kolar, M. (2022). One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning.

Chicago

Cisneros-Velarde, Pedro, Boxiang Lyu, Sanmi Koyejo, and Mladen Kolar. 2022. “One Policy Is Enough: Parallel Exploration with a Single Policy Is Near-Optimal for Reward-Free Reinforcement Learning.” http://widgets.ebscohost.com/prod/customlink/proxify/proxify.php?count=1&encode=0&proxy=&find_1=&replace_1=&target=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsarx&AN=edsarx.2205.15891&authtype=sso&custid=ns315887.