{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"TalkRL: The Reinforcement Learning Podcast","title":"Max Schwarzer","html":"","width":"100%","height":180,"duration":4218,"description":"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.   \nFeatured References\n\nBigger, Better, Faster: Human-level Atari with human-level efficiency \nMax Schwarzer, Johan Obando-Ceron, Aaron Courville, Marc Bellemare, Rishabh Agarwal, Pablo Samuel Castro \n\nSample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier\nPierluca D'Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G Bellemare, Aaron Courville \nThe Primacy Bias in Deep Reinforcement Learning\nEvgenii Nikishin, Max Schwarzer, Pierluca D'Oro, Pierre-Luc Bacon, Aaron Courville \n\n\nAdditional References   \nRainbow: Combining Improvements in Deep Reinforcement Learning, Hessel et al 2017  \nWhen to use parametric models in reinforcement learning? Hasselt et al 2019 \nData-Efficient Reinforcement Learning with Self-Predictive Representations, Schwarzer et al 2020  \nPretraining Representations for Data-Efficient Reinforcement Learning, Schwarzer et al 2021  ","thumbnail_url":"https://img.transistorcdn.com/jXB1-VPK-A9v1epzc4aG4pFxqlvo2vbQ_Ytyuar_gPI/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzIwNDcvMTcwNzk1/NDcxMS1hcnR3b3Jr/LmpwZw.webp","thumbnail_width":300,"thumbnail_height":300}