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Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization


Sep 13, 2022
Leo Feng, Padideh Nouri, Aneri Muni, Yoshua Bengio, Pierre-Luc Bacon


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The Primacy Bias in Deep Reinforcement Learning


May 16, 2022
Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro, Pierre-Luc Bacon, Aaron Courville

* ICML 2022; code at https://github.com/evgenii-nikishin/rl_with_resets 

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Continuous-Time Meta-Learning with Forward Mode Differentiation


Mar 02, 2022
Tristan Deleu, David Kanaa, Leo Feng, Giancarlo Kerg, Yoshua Bengio, Guillaume Lajoie, Pierre-Luc Bacon


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Myriad: a real-world testbed to bridge trajectory optimization and deep learning


Feb 22, 2022
Nikolaus H. R. Howe, Simon Dufort-LabbƩ, Nitarshan Rajkumar, Pierre-Luc Bacon

* 13 pages, 4 figures 

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Direct Behavior Specification via Constrained Reinforcement Learning


Jan 19, 2022
Julien Roy, Roger Girgis, Joshua Romoff, Pierre-Luc Bacon, Christopher Pal


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Neural Algorithmic Reasoners are Implicit Planners


Oct 11, 2021
Andreea Deac, Petar Veličković, Ognjen Milinković, Pierre-Luc Bacon, Jian Tang, Mladen Nikolić

* To appear at NeurIPS 2021 (Spotlight talk). 20 pages, 10 figures. arXiv admin note: text overlap with arXiv:2010.13146 

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Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation


Jun 06, 2021
Evgenii Nikishin, Romina Abachi, Rishabh Agarwal, Pierre-Luc Bacon

* Code at https://github.com/evgenii-nikishin/omd 

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An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning


Mar 10, 2021
Dilip Arumugam, Peter Henderson, Pierre-Luc Bacon

* Workshop on Biological and Artificial Reinforcement Learning (NeurIPS 2020) 

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XLVIN: eXecuted Latent Value Iteration Nets


Oct 25, 2020
Andreea Deac, Petar Veličković, Ognjen Milinković, Pierre-Luc Bacon, Jian Tang, Mladen Nikolić

* NeurIPS 2020 Deep Reinforcement Learning Workshop 

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