Castro, Pablo Samuel

54 publications

ICLR 2026 ARM-FM: Automated Reward Machines via Foundation Models for Compositional Reinforcement Learning Roger Creus Castanyer, Faisal Mohamed, Pablo Samuel Castro, Cyrus Neary, Glen Berseth

ICLR 2026 Asymmetric Proximal Policy Optimization: Mini-Critics Boost LLM Reasoning Jiashun Liu, Johan Obando-Ceron, Han Lu, Yancheng He, Weixun Wang, Wenbo Su, Bo Zheng, Pablo Samuel Castro, Aaron Courville, Ling Pan

ICLR 2026 Simplicial Embeddings Improve Sample Efficiency in Actor–Critic Agents Johan Obando-Ceron, Walter Mayor, Samuel Lavoie, Scott Fujimoto, Aaron Courville, Pablo Samuel Castro

TMLR 2025 A Survey of State Representation Learning for Deep Reinforcement Learning Ayoub Echchahed, Pablo Samuel Castro

ICML 2025 Discovering Symbolic Cognitive Models from Human and Animal Behavior Pablo Samuel Castro, Nenad Tomasev, Ankit Anand, Navodita Sharma, Rishika Mohanta, Aparna Dev, Kuba Perlin, Siddhant Jain, Kyle Levin, Noemi Elteto, Will Dabney, Alexander Novikov, Glenn C Turner, Maria K Eckstein, Nathaniel D. Daw, Kevin J Miller, Kim Stachenfeld

ICLR 2025 Don't Flatten, Tokenize! Unlocking the Key to SoftMoE's Efficacy in Deep RL Ghada Sokar, Johan Samir Obando Ceron, Aaron Courville, Hugo Larochelle, Pablo Samuel Castro

NeurIPS 2025 Measure Gradients, Not Activations! Enhancing Neuronal Activity in Deep Reinforcement Learning Jiashun Liu, Zihao Wu, Johan Obando-Ceron, Pablo Samuel Castro, Aaron Courville, Ling Pan

NeurIPS 2025 Meta-World+: An Improved, Standardized, RL Benchmark Reginald McLean, Evangelos Chatzaroulas, Luc McCutcheon, Frank Röder, Tianhe Yu, Zhanpeng He, K.R. Zentner, Ryan Julian, J K Terry, Isaac Woungang, Nariman Farsad, Pablo Samuel Castro

NeurIPS 2025 Mind the GAP! the Challenges of Scale in Pixel-Based Deep Reinforcement Learning Ghada Sokar, Pablo Samuel Castro

ICML 2025 Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn Hongyao Tang, Johan Obando-Ceron, Pablo Samuel Castro, Aaron Courville, Glen Berseth

NeurIPS 2025 NAVIX: Scaling MiniGrid Environments with JAX Eduardo Pignatelli, Jarek Luca Liesen, Robert Tjarko Lange, Chris Lu, Pablo Samuel Castro, Laura Toni

NeurIPS 2025 Stable Gradients for Stable Learning at Scale in Deep Reinforcement Learning Roger Creus Castanyer, Johan Obando-Ceron, Lu Li, Pierre-Luc Bacon, Glen Berseth, Aaron Courville, Pablo Samuel Castro

ICLR 2025 Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning Samuel Garcin, Trevor McInroe, Pablo Samuel Castro, Christopher G. Lucas, David Abel, Prakash Panangaden, Stefano V Albrecht

ICML 2025 The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning Jiashun Liu, Johan Obando-Ceron, Pablo Samuel Castro, Aaron Courville, Ling Pan

ICML 2025 The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Walter Mayor, Johan Obando-Ceron, Aaron Courville, Pablo Samuel Castro

TMLR 2024 A Density Estimation Perspective on Learning from Pairwise Human Preferences Vincent Dumoulin, Daniel D. Johnson, Pablo Samuel Castro, Hugo Larochelle, Yann Dauphin

ICML 2024 Adaptive Accompaniment with ReaLchords Yusong Wu, Tim Cooijmans, Kyle Kastner, Adam Roberts, Ian Simon, Alexander Scarlatos, Chris Donahue, Cassie Tarakajian, Shayegan Omidshafiei, Aaron Courville, Pablo Samuel Castro, Natasha Jaques, Cheng-Zhi Anna Huang

NeurIPS 2024 CALE: Continuous Arcade Learning Environment Jesse Farebrother, Pablo Samuel Castro

ICML 2024 In Value-Based Deep Reinforcement Learning, a Pruned Network Is a Good Network Johan Samir Obando Ceron, Aaron Courville, Pablo Samuel Castro

CPAL 2024 Jaxpruner: A Concise Library for Sparsity Research Joo Hyung Lee, Wonpyo Park, Nicole Elyse Mitchell, Jonathan Pilault, Johan Samir Obando Ceron, Han-Byul Kim, Namhoon Lee, Elias Frantar, Yun Long, Amir Yazdanbakhsh, Woohyun Han, Shivani Agrawal, Suvinay Subramanian, Xin Wang, Sheng-Chun Kao, Xingyao Zhang, Trevor Gale, Aart J.C. Bik, Milen Ferev, Zhonglin Han, Hong-Seok Kim, Yann Dauphin, Gintare Karolina Dziugaite, Pablo Samuel Castro, Utku Evci

ICML 2024 Mixtures of Experts Unlock Parameter Scaling for Deep RL Johan Samir Obando Ceron, Ghada Sokar, Timon Willi, Clare Lyle, Jesse Farebrother, Jakob Nicolaus Foerster, Gintare Karolina Dziugaite, Doina Precup, Pablo Samuel Castro

ICML 2024 Stop Regressing: Training Value Functions via Classification for Scalable Deep RL Jesse Farebrother, Jordi Orbay, Quan Vuong, Adrien Ali Taiga, Yevgen Chebotar, Ted Xiao, Alex Irpan, Sergey Levine, Pablo Samuel Castro, Aleksandra Faust, Aviral Kumar, Rishabh Agarwal

TMLR 2023 A Kernel Perspective on Behavioural Metrics for Markov Decision Processes Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland

ICML 2023 Bigger, Better, Faster: Human-Level Atari with Human-Level Efficiency Max Schwarzer, Johan Samir Obando Ceron, Aaron Courville, Marc G Bellemare, Rishabh Agarwal, Pablo Samuel Castro

NeurIPS 2023 Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks Maxime Chevalier-Boisvert, Bolun Dai, Mark Towers, Rodrigo Perez-Vicente, Lucas Willems, Salem Lahlou, Suman Pal, Pablo Samuel Castro, J KTerry

ECML-PKDD 2023 Offline Reinforcement Learning with On-Policy Q-Function Regularization Laixi Shi, Robert Dadashi, Yuejie Chi, Pablo Samuel Castro, Matthieu Geist

ICLR 2023 Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G Bellemare

NeurIPS 2023 Small Batch Deep Reinforcement Learning Johan Obando Ceron, Marc Bellemare, Pablo Samuel Castro

ICML 2023 The Dormant Neuron Phenomenon in Deep Reinforcement Learning Ghada Sokar, Rishabh Agarwal, Pablo Samuel Castro, Utku Evci

NeurIPSW 2022 Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G Bellemare

NeurIPSW 2022 Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G Bellemare

NeurIPSW 2022 Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G Bellemare

NeurIPS 2022 Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc Bellemare

ICML 2022 The State of Sparse Training in Deep Reinforcement Learning Laura Graesser, Utku Evci, Erich Elsen, Pablo Samuel Castro

NeurIPSW 2022 Variance Double-Down: The Small Batch Size Anomaly in Multistep Deep Reinforcement Learning Johan Samir Obando Ceron, Marc G Bellemare, Pablo Samuel Castro

ICLR 2021 Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G Bellemare

NeurIPS 2021 Deep Reinforcement Learning at the Edge of the Statistical Precipice Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc Bellemare

NeurIPSW 2021 Lifting the Veil on Hyper-Parameters for Value-Based Deep Reinforcement Learning João Guilherme Madeira Araújo, Johan Samir Obando Ceron, Pablo Samuel Castro

NeurIPSW 2021 Lifting the Veil on Hyper-Parameters for Value-Based Deep Reinforcement Learning João Guilherme Madeira Araújo, Johan Samir Obando Ceron, Pablo Samuel Castro

NeurIPS 2021 MICo: Improved Representations via Sampling-Based State Similarity for Markov Decision Processes Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland

AAAI 2021 Metrics and Continuity in Reinforcement Learning Charline Le Lan, Marc G. Bellemare, Pablo Samuel Castro

ICML 2021 Revisiting Rainbow: Promoting More Insightful and Inclusive Deep Reinforcement Learning Research Johan Samir Obando Ceron, Pablo Samuel Castro

NeurIPS 2021 The Difficulty of Passive Learning in Deep Reinforcement Learning Georg Ostrovski, Pablo Samuel Castro, Will Dabney

ICML 2020 Rigging the Lottery: Making All Tickets Winners Utku Evci, Trevor Gale, Jacob Menick, Pablo Samuel Castro, Erich Elsen

AAAI 2020 Scalable Methods for Computing State Similarity in Deterministic Markov Decision Processes Pablo Samuel Castro

AAAI 2019 A Comparative Analysis of Expected and Distributional Reinforcement Learning Clare Lyle, Marc G. Bellemare, Pablo Samuel Castro

NeurIPS 2019 A Geometric Perspective on Optimal Representations for Reinforcement Learning Marc Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taiga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle

IJCAI 2019 An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents Felipe Petroski Such, Vashisht Madhavan, Rosanne Liu, Rui Wang, Pablo Samuel Castro, Yulun Li, Jiale Zhi, Ludwig Schubert, Marc G. Bellemare, Jeff Clune, Joel Lehman

AISTATS 2019 Distributional Reinforcement Learning with Linear Function Approximation Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro, Subhodeep Moitra

ECML-PKDD 2010 Smarter Sampling in Model-Based Bayesian Reinforcement Learning Pablo Samuel Castro, Doina Precup

AAAI 2010 Using Bisimulation for Policy Transfer in MDPs Pablo Samuel Castro, Doina Precup

IJCAI 2009 Equivalence Relations in Fully and Partially Observable Markov Decision Processes Pablo Samuel Castro, Prakash Panangaden, Doina Precup

IJCAI 2007 Using Linear Programming for Bayesian Exploration in Markov Decision Processes Pablo Samuel Castro, Doina Precup

UAI 2006 Methods for Computing State Similarity in Markov Decision Processes Norm Ferns, Pablo Samuel Castro, Doina Precup, Prakash Panangaden