Ben Eysenbach's guest appearances

Ben Eysenbach

TalkRL: The Reinforcement Learning Podcast

Kamyar Azizzadenesheli

Scott Fujimoto

Jessica Hamrick

Samuel Castro

Ashley Hill

Michael Littman

Natasha Jaques

Rohin Shah

Robert Osazuwa Ness

Jordan Terry

Amy Zhang

Michael Dennis

Jess Whittlestone

Taylor Killian

Csaba Szepesvari

OwlTail

Ben Eysenbach is a PhD student in the Machine Learning Department at Carnegie Mellon University.  He was a Resident at Google Brain, and studied math and computer science at MIT. He co-founded the ICML Exploration in Reinforcement Learning workshop.  Featured References Diversity is All You Need: Learning Skills without a Reward Function Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, Sergey Levine Search on the Replay Buffer: Bridging Planning and Reinforcement Learning Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine Additional References   Behaviour Suite for Reinforcement Learning, Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvari, Satinder Singh, Benjamin Van Roy, Richard Sutton, David Silver, Hado Van Hasselt   Learning Latent Plans from Play, Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, Pierre Sermanet   Finale Doshi-Velez   Emma Brunskill   Closed-loop optimization of fast-charging protocols for batteries with machine learning,  Peter Attia, Aditya Grover, Norman Jin, Kristen Severson, Todor Markov, Yang-Hung Liao, Michael Chen, Bryan Cheong, Nicholas Perkins, Zi Yang, Patrick Herring, Muratahan Aykol, Stephen Harris, Richard Braatz, Stefano Ermon, William Chueh   CMU 10-703 Deep Reinforcement Learning, Fall 2019, Carnegie Mellon University   ICML Exploration in Reinforcement Learning workshop  

<a href="https://ben-eysenbach.github.io/" rel="nofollow" target="_blank">Ben Eysenbach</a> is a PhD student in the <a href="https://www.ml.cmu.edu/" rel="nofollow" target="_blank">Machine Learning Department</a> at Carnegie Mellon University.  He was a Resident at Google Brain, and studied math and computer science at MIT. He co-founded the <a href="https://sites.google.com/view/erl-2019/home" rel="nofollow" target="_blank">ICML Exploration in Reinforcement Learning workshop</a>.  Featured References <a href="https://arxiv.org/abs/1802.06070" rel="nofollow" target="_blank">Diversity is All You Need: Learning Skills without a Reward Function</a> Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, Sergey Levine <a href="https://arxiv.org/abs/1906.05253" rel="nofollow" target="_blank">Search on the Replay Buffer: Bridging Planning and Reinforcement Learning </a>Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine Additional References <ul> <li> <a href="https://arxiv.org/abs/1908.03568" rel="nofollow" target="_blank">Behaviour Suite for Reinforcement Learning</a>, Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvari, Satinder Singh, Benjamin Van Roy, Richard Sutton, David Silver, Hado Van Hasselt </li> <li> <a href="https://arxiv.org/abs/1903.01973" rel="nofollow" target="_blank">Learning Latent Plans from Play</a>, Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, Pierre Sermanet </li> <li> <a href="https://finale.seas.harvard.edu/" rel="nofollow" target="_blank">Finale Doshi-Velez</a> </li> <li> <a href="https://cs.stanford.edu/people/ebrun/" rel="nofollow" target="_blank">Emma Brunskill</a> </li> <li> <a href="https://www.nature.com/articles/s41586-020-1994-5" rel="nofollow" target="_blank">Closed-loop optimization of fast-charging protocols for batteries with machine learning</a>,  Peter Attia, Aditya Grover, Norman Jin, Kristen Severson, Todor Markov, Yang-Hung Liao, Michael Chen, Bryan Cheong, Nicholas Perkins, Zi Yang, Patrick Herring, Muratahan Aykol, Stephen Harris, Richard Braatz, Stefano Ermon, William Chueh </li> <li> <a href="https://cmudeeprl.github.io/703website/" rel="nofollow" target="_blank">CMU 10-703 Deep Reinforcement Learning</a>, Fall 2019, Carnegie Mellon University </li> <li> <a href="https://sites.google.com/view/erl-2019/home" rel="nofollow" target="_blank">ICML Exploration in Reinforcement Learning workshop</a> </li> </ul>