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Pablo Samuel Castro

31 papers · 2019–2025 · 6 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (26) 🌍 Conference Polyglot (6) 🐝 Cross-Pollinator (14) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (6) πŸ‘‘ Triple Crown πŸ† Grand Slam πŸ”¬ Deep Specialist (13) πŸ† Keyword Champion (3) πŸš€ Conference Pioneer πŸ—ƒοΈ Keyword Collector (82) ⚑ Prolific Year (6) πŸ’Ž Century Club (31) πŸ”₯ Unstoppable (7)

Conferences

ICML (13) NIPS (8) ICLR (4) AAAI (3) AISTATS (2) IJCAI (1)

Top co-authors

Aaron Courville (7) Rishabh Agarwal (7) Johan Samir Obando Ceron (5) Jesse Farebrother (4) Marc G. Bellemare (4) Marc Bellemare (4) Nicolas Le Roux (3) Max Schwarzer (3) Johan Obando-Ceron (3) Marc G Bellemare (3)

Keywords

deep reinforcement learning (8) reinforcement learning (4) markov decision process (3) representation learning (3) state similarity (3) neural network (3) benchmark evaluation (2) transfer learning (2) distributional reinforcement learning (2) sparse neural network (2) value-based learning (2) policy gradient (2) parameter efficiency (2) convergence guarantee (2) value iteration (2) atari game (2) geometric analysis (1) function approximation (1) sample efficiency (1) neural network pruning (1)

Papers

The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning ICML 2025
The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks ICML 2025
Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn ICML 2025
Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL ICLR 2025
Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning ICLR 2025
Discovering Symbolic Cognitive Models from Human and Animal Behavior ICML 2025
Mixtures of Experts Unlock Parameter Scaling for Deep RL ICML 2024
CALE: Continuous Arcade Learning Environment NIPS 2024
In value-based deep reinforcement learning, a pruned network is a good network ICML 2024
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL ICML 2024
Adaptive Accompaniment with ReaLchords ICML 2024
Small batch deep reinforcement learning NIPS 2023
Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks ICLR 2023
Bigger, Better, Faster: Human-level Atari with human-level efficiency ICML 2023
The Dormant Neuron Phenomenon in Deep Reinforcement Learning ICML 2023
Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks NIPS 2023
The State of Sparse Training in Deep Reinforcement Learning ICML 2022
A general class of surrogate functions for stable and efficient reinforcement learning AISTATS 2022
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress NIPS 2022
Deep Reinforcement Learning at the Edge of the Statistical Precipice NIPS 2021
Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research ICML 2021
MICo: Improved representations via sampling-based state similarity for Markov decision processes NIPS 2021
The Difficulty of Passive Learning in Deep Reinforcement Learning NIPS 2021
Metrics and Continuity in Reinforcement Learning AAAI 2021
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning ICLR 2021
Scalable Methods for Computing State Similarity in Deterministic Markov Decision Processes AAAI 2020
Rigging the Lottery: Making All Tickets Winners ICML 2020
An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents IJCAI 2019
A Comparative Analysis of Expected and Distributional Reinforcement Learning AAAI 2019
Distributional reinforcement learning with linear function approximation AISTATS 2019
A Geometric Perspective on Optimal Representations for Reinforcement Learning NIPS 2019