Pablo Samuel Castro

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 65

Top Topics

Function Approximation

Markov Decision Process

Reinforcement Learning

Top Venues

Publications

Timon Willi, Johan S. Obando-Ceron, Jakob N. Foerster, Karolina Dziugaite, Pablo Samuel Castro
Mixture of Experts in a Mixture of RL settings. CoRR (2024)
Johan S. Obando-Ceron, Aaron C. Courville, Pablo Samuel Castro
In deep reinforcement learning, a pruned network is a good network. CoRR (2024)
Vincent Dumoulin, Daniel D. Johnson, Pablo Samuel Castro, Hugo Larochelle, Yann N. Dauphin
A density estimation perspective on learning from pairwise human preferences. Trans. Mach. Learn. Res. 2024 (2024)
Jesse Farebrother, Jordi Orbay, Quan Vuong, Adrien Ali Taïga, Yevgen Chebotar, Ted Xiao, Alex Irpan, Sergey Levine, Pablo Samuel Castro, Aleksandra Faust, Aviral Kumar, Rishabh Agarwal
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL. CoRR (2024)
Eduardo Pignatelli, Jarek Liesen, Robert Tjarko Lange, Chris Lu, Pablo Samuel Castro, Laura Toni
NAVIX: Scaling MiniGrid Environments with JAX. CoRR (2024)
Johan S. Obando-Ceron, João G. M. Araújo, Aaron Courville, Pablo Samuel Castro
On the consistency of hyper-parameter selection in value-based deep reinforcement learning. CoRR (2024)
Johan S. Obando-Ceron, Ghada Sokar, Timon Willi, Clare Lyle, Jesse Farebrother, Jakob N. Foerster, Gintare Karolina Dziugaite, Doina Precup, Pablo Samuel Castro
Mixtures of Experts Unlock Parameter Scaling for Deep RL. CoRR (2024)
Johan S. Obando-Ceron, Marc G. Bellemare, Pablo Samuel Castro
Small batch deep reinforcement learning. NeurIPS (2023)
Laixi Shi, Robert Dadashi, Yuejie Chi, Pablo Samuel Castro, Matthieu Geist
Offline Reinforcement Learning with On-Policy Q-Function Regularization. CoRR (2023)
Laixi Shi, Robert Dadashi, Yuejie Chi, Pablo Samuel Castro, Matthieu Geist
Offline Reinforcement Learning with On-Policy Q-Function Regularization. ECML/PKDD (4) (2023)
Ghada Sokar, Rishabh Agarwal, Pablo Samuel Castro, Utku Evci
The Dormant Neuron Phenomenon in Deep Reinforcement Learning. CoRR (2023)
Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G. Bellemare
Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks. CoRR (2023)
Max Schwarzer, Jesse Farebrother, Joshua Greaves, Ekin Dogus Cubuk, Rishabh Agarwal, Aaron C. Courville, Marc G. Bellemare, Sergei V. Kalinin, Igor Mordatch, Pablo Samuel Castro, Kevin M. Roccapriore
Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy. CoRR (2023)
Vincent Dumoulin, Daniel D. Johnson, Pablo Samuel Castro, Hugo Larochelle, Yann N. Dauphin
A density estimation perspective on learning from pairwise human preferences. CoRR (2023)
Johan Samir Obando-Ceron, Marc G. Bellemare, Pablo Samuel Castro
The Small Batch Size Anomaly in Multistep Deep Reinforcement Learning. Tiny Papers @ ICLR (2023)
Max Schwarzer, Johan S. Obando-Ceron, Aaron C. Courville, Marc G. Bellemare, Rishabh Agarwal, Pablo Samuel Castro
Bigger, Better, Faster: Human-level Atari with human-level efficiency. CoRR (2023)
Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland
A Kernel Perspective on Behavioural Metrics for Markov Decision Processes. CoRR (2023)
Ghada Sokar, Rishabh Agarwal, Pablo Samuel Castro, Utku Evci
The Dormant Neuron Phenomenon in Deep Reinforcement Learning. ICML (2023)
Joo Hyung Lee, Wonpyo Park, Nicole Mitchell, Jonathan Pilault, Johan S. Obando-Ceron, Han-Byul Kim, Namhoon Lee, Elias Frantar, Yun Long, Amir Yazdanbakhsh, Shivani Agrawal, Suvinay Subramanian, Xin Wang, Sheng-Chun Kao, Xingyao Zhang, Trevor Gale, Aart Bik, Woohyun Han, Milen Ferev, Zhonglin Han, Hong-Seok Kim, Yann N. Dauphin, Karolina Dziugaite, Pablo Samuel Castro, Utku Evci
JaxPruner: A concise library for sparsity research. CoRR (2023)
Johan S. Obando-Ceron, Marc G. Bellemare, Pablo Samuel Castro
Small batch deep reinforcement learning. CoRR (2023)
Max Schwarzer, Johan Samir Obando-Ceron, Aaron C. Courville, Marc G. Bellemare, Rishabh Agarwal, Pablo Samuel Castro
Bigger, Better, Faster: Human-level Atari with human-level efficiency. ICML (2023)
Maxime Chevalier-Boisvert, Bolun Dai, Mark Towers, Rodrigo de Lazcano, Lucas Willems, Salem Lahlou, Suman Pal, Pablo Samuel Castro, Jordan Terry
Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks. CoRR (2023)
Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland
A Kernel Perspective on Behavioural Metrics for Markov Decision Processes. Trans. Mach. Learn. Res. 2023 (2023)
Maxime Chevalier-Boisvert, Bolun Dai, Mark Towers, Rodrigo Perez-Vicente, Lucas Willems, Salem Lahlou, Suman Pal, Pablo Samuel Castro, Jordan Terry
Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks. NeurIPS (2023)
Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G. Bellemare
Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks. ICLR (2023)
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress. NeurIPS (2022)
Laura Graesser, Utku Evci, Erich Elsen, Pablo Samuel Castro
The State of Sparse Training in Deep Reinforcement Learning. ICML (2022)
Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Müller, Shivam Garg, Matthieu Geist, Marlos C. Machado, Pablo Samuel Castro, Nicolas Le Roux
A general class of surrogate functions for stable and efficient reinforcement learning. AISTATS (2022)
Laura Graesser, Utku Evci, Erich Elsen, Pablo Samuel Castro
The State of Sparse Training in Deep Reinforcement Learning. CoRR (2022)
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare
Beyond Tabula Rasa: Reincarnating Reinforcement Learning. CoRR (2022)
Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland
MICo: Learning improved representations via sampling-based state similarity for Markov decision processes. CoRR (2021)
Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. CoRR (2021)
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare
Deep Reinforcement Learning at the Edge of the Statistical Precipice. CoRR (2021)
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare
Deep Reinforcement Learning at the Edge of the Statistical Precipice. NeurIPS (2021)
Pablo Samuel Castro
Losses, Dissonances, and Distortions. CoRR (2021)
Georg Ostrovski, Pablo Samuel Castro, Will Dabney
The Difficulty of Passive Learning in Deep Reinforcement Learning. NeurIPS (2021)
Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. ICLR (2021)
Charline Le Lan, Marc G. Bellemare, Pablo Samuel Castro
Metrics and continuity in reinforcement learning. CoRR (2021)
Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland
MICo: Improved representations via sampling-based state similarity for Markov decision processes. NeurIPS (2021)
Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Mueller, Matthieu Geist, Marlos C. Machado, Pablo Samuel Castro, Nicolas Le Roux
A functional mirror ascent view of policy gradient methods with function approximation. CoRR (2021)
Charline Le Lan, Marc G. Bellemare, Pablo Samuel Castro
Metrics and Continuity in Reinforcement Learning. AAAI (2021)
Johan Samir Obando-Ceron, Pablo Samuel Castro
Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research. ICML (2021)
Georg Ostrovski, Pablo Samuel Castro, Will Dabney
The Difficulty of Passive Learning in Deep Reinforcement Learning. CoRR (2021)
Pablo Samuel Castro
Scalable Methods for Computing State Similarity in Deterministic Markov Decision Processes. AAAI (2020)
Utku Evci, Trevor Gale, Jacob Menick, Pablo Samuel Castro, Erich Elsen
Rigging the Lottery: Making All Tickets Winners. ICML (2020)
Johan S. Obando-Ceron, Pablo Samuel Castro
Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research. CoRR (2020)
Pablo Samuel Castro
GANterpretations. CoRR (2020)
Marc G. Bellemare, Salvatore Candido, Pablo Samuel Castro, Jun Gong, Marlos C. Machado, Subhodeep Moitra, Sameera S. Ponda, Ziyu Wang
Autonomous navigation of stratospheric balloons using reinforcement learning. Nat. 588 (7836) (2020)
Kory Wallace Mathewson, Pablo Samuel Castro, Colin Cherry, George F. Foster, Marc G. Bellemare
Shaping the Narrative Arc: Information-Theoretic Collaborative DialoguePaper type: Technical Paper. ICCC (2020)
Pablo Samuel Castro, Shijian Li, Daqing Zhang
Inverse Reinforcement Learning with Multiple Ranked Experts. CoRR (2019)
Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro, Subhodeep Moitra
Distributional reinforcement learning with linear function approximation. AISTATS (2019)
Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
A Geometric Perspective on Optimal Representations for Reinforcement Learning. NeurIPS (2019)
Clare Lyle, Pablo Samuel Castro, Marc G. Bellemare
A Comparative Analysis of Expected and Distributional Reinforcement Learning. CoRR (2019)
Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
A Geometric Perspective on Optimal Representations for Reinforcement Learning. CoRR (2019)
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
An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents. IJCAI (2019)
Pablo Samuel Castro
Performing Structured Improvisations with pre-trained Deep Learning Models. CoRR (2019)
Pablo Samuel Castro
Scalable methods for computing state similarity in deterministic Markov Decision Processes. CoRR (2019)
Kory W. Mathewson, Pablo Samuel Castro, Colin Cherry, George F. Foster, Marc G. Bellemare
Shaping the Narrative Arc: An Information-Theoretic Approach to Collaborative Dialogue. CoRR (2019)
Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro, Subhodeep Moitra
Distributional reinforcement learning with linear function approximation. CoRR (2019)
Clare Lyle, Marc G. Bellemare, Pablo Samuel Castro
A Comparative Analysis of Expected and Distributional Reinforcement Learning. AAAI (2019)
Pablo Samuel Castro
Performing Structured Improvisations with Pre-trained Deep Learning Models. ICCC (2019)
Utku Evci, Trevor Gale, Jacob Menick, Pablo Samuel Castro, Erich Elsen
Rigging the Lottery: Making All Tickets Winners. CoRR (2019)
Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar, Marc G. Bellemare
Dopamine: A Research Framework for Deep Reinforcement Learning. CoRR (2018)
Pablo Samuel Castro, Maria Attarian
Combining Learned Lyrical Structures and Vocabulary for Improved Lyric Generation. CoRR (2018)
Felipe Petroski Such, Vashisht Madhavan, Rosanne Liu, Rui Wang, Pablo Samuel Castro, Yulun Li, Ludwig Schubert, Marc G. Bellemare, Jeff Clune, Joel Lehman
An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents. CoRR (2018)
Pablo Samuel Castro, Daqing Zhang, Chao Chen, Shijian Li, Gang Pan
From taxi GPS traces to social and community dynamics: A survey. ACM Comput. Surv. 46 (2) (2013)
Chao Chen, Daqing Zhang, Pablo Samuel Castro, Nan Li, Lin Sun, Shijian Li, Zonghui Wang
iBOAT: Isolation-Based Online Anomalous Trajectory Detection. IEEE Trans. Intell. Transp. Syst. 14 (2) (2013)
Lin Sun, Daqing Zhang, Chao Chen, Pablo Samuel Castro, Shijian Li, Zonghui Wang
Real Time Anomalous Trajectory Detection and Analysis. Mob. Networks Appl. 18 (3) (2013)
Norman Ferns, Pablo Samuel Castro, Doina Precup, Prakash Panangaden
Methods for computing state similarity in Markov Decision Processes. CoRR (2012)
Pablo Samuel Castro, Daqing Zhang, Shijian Li
Urban Traffic Modelling and Prediction Using Large Scale Taxi GPS Traces. Pervasive (2012)
Chao Chen, Daqing Zhang, Pablo Samuel Castro, Nan Li, Lin Sun, Shijian Li
Real-Time Detection of Anomalous Taxi Trajectories from GPS Traces. MobiQuitous (2011)
Pablo Samuel Castro, Doina Precup
Automatic Construction of Temporally Extended Actions for MDPs Using Bisimulation Metrics. EWRL (2011)
Pablo Samuel Castro, Doina Precup
Using Bisimulation for Policy Transfer in MDPs. AAAI (2010)
Pablo Samuel Castro, Doina Precup
Smarter Sampling in Model-Based Bayesian Reinforcement Learning. ECML/PKDD (1) (2010)
Pablo Samuel Castro, Doina Precup
Using bisimulation for policy transfer in MDPs. AAMAS (2010)
Pablo Samuel Castro, Prakash Panangaden, Doina Precup
Equivalence Relations in Fully and Partially Observable Markov Decision Processes. IJCAI (2009)
Pablo Samuel Castro, Doina Precup
Using Linear Programming for Bayesian Exploration in Markov Decision Processes. IJCAI (2007)
Norm Ferns, Pablo Samuel Castro, Doina Precup, Prakash Panangaden
Methods for Computing State Similarity in Markov Decision Processes. UAI (2006)