BBF: A Collection of Improvements on Top of DQN

Max Schwarzer is a PhD student at Mila, with Aaron Courville and Marc Bellemare, interested in RL scaling, representation learning for RL, and RL for science.  Max spent the last 1.5 years at Google Brain/DeepMind, and is now at Apple Machine Learning Research.   

Featured References

Bigger, Better, Faster: Human-level Atari with human-level efficiency 
Max Schwarzer, Johan Obando-Ceron, Aaron Courville, Marc Bellemare, Rishabh Agarwal, Pablo Samuel Castro 

Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier
Pierluca D'Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G Bellemare, Aaron Courville 

The Primacy Bias in Deep Reinforcement Learning
Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro, Pierre-Luc Bacon, Aaron Courville 


Additional References   

• Rainbow: Combining Improvements in Deep Reinforcement Learning, Hessel et al 2017  
• When to use parametric models in reinforcement learning? Hasselt et al 2019 
• Data-Efficient Reinforcement Learning with Self-Predictive Representations, Schwarzer et al 2020  
• Pretraining Representations for Data-Efficient Reinforcement Learning, Schwarzer et al 2021