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← Authors

Pablo Samuel Castro

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

Achievements

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+11 more ↓ 🌍 Conference Polyglot (6) 🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🏃 Academic Marathon (6)
🐣 Hot Topic Early Bird 🏃 Academic Marathon (6) 👑 Triple Crown 🏆 Grand Slam 🔬 Deep Specialist (13) 🏆 Keyword Champion (3) 🚀 Conference Pioneer 🗃️ Keyword Collector (82) ⚡ Prolific Year (6) 💎 Century Club (31) 🔥 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