Scott Fujimoto

Scott Fujimoto is a PhD student at McGill University and Mila. He is the author of TD3 as well as some of the recent developments in batch deep reinforcement learning.  

Featured References 

Addressing Function Approximation Error in Actor-Critic Methods 
Scott Fujimoto, Herke van Hoof, David Meger 

Off-Policy Deep Reinforcement Learning without Exploration 

Scott Fujimoto, David Meger, Doina Precup 

Benchmarking Batch Deep Reinforcement Learning Algorithms 

Scott Fujimoto, Edoardo Conti, Mohammad Ghavamzadeh, Joelle Pineau 

Additional References 

• Striving for Simplicity in Off-Policy Deep Reinforcement Learning 
  Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi 
• Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor 
  Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine 
• Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog 
  Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, Rosalind Picard 
• Continuous control with deep reinforcement learning 
  Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra 
• Distributed Distributional Deterministic Policy Gradients 
  Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy Lillicrap