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Will Dabney

43 papers · 2017–2025 · 7 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (8) πŸ—ΊοΈ Taxonomy Completionist (38) 🌍 Conference Polyglot (7) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (8) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🀝 Dynamic Duo (23) πŸ‘‘ Triple Crown πŸ† Grand Slam πŸ”¬ Deep Specialist (22) πŸ† Keyword Champion (3) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ—ƒοΈ Keyword Collector (123) ⚑ Prolific Year (5) πŸ’Ž Century Club (43) πŸ”₯ Unstoppable (9)

Conferences

ICML (18) NIPS (9) AISTATS (6) ICLR (6) JMLR (2) AAAI (1) IJCAI (1)

Top co-authors

RΓ©mi Munos (23) Mark Rowland (21) Clare Lyle (12) Yunhao Tang (10) Georg Ostrovski (9) Andre Barreto (8) Anna Harutyunyan (8) David Abel (5) Nicolas Heess (5) Bernardo Avila Pires (5)

Research topics

Applications (1)

Keywords

deep reinforcement learning (12) reinforcement learning (10) distributional reinforcement learning (9) value function (6) return distribution (5) representation learning (5) off-policy learning (4) temporal difference learning (3) policy gradient (3) credit assignment (3) policy improvement (3) quantile regression (3) bellman equation (3) variance reduction (3) auxiliary task (3) spectral decomposition (2) transfer learning (2) temporal difference (2) policy evaluation (2) value estimation (2)

Papers

Optimizing Return Distributions with Distributional Dynamic Programming JMLR 2025
Discovering Symbolic Cognitive Models from Human and Animal Behavior ICML 2025
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning AISTATS 2025
A Distributional Analogue to the Successor Representation ICML 2024
An Analysis of Quantile Temporal-Difference Learning JMLR 2024
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model NIPS 2024
Normalization and effective learning rates in reinforcement learning NIPS 2024
Settling the Reward Hypothesis ICML 2023
Deep Reinforcement Learning with Plasticity Injection NIPS 2023
Understanding Self-Predictive Learning for Reinforcement Learning ICML 2023
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation ICML 2023
Quantile Credit Assignment ICML 2023
Understanding Plasticity in Neural Networks ICML 2023
Bootstrapped Representations in Reinforcement Learning ICML 2023
Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition ICML 2023
On the Expressivity of Markov Reward (Extended Abstract) IJCAI 2022
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning NIPS 2022
Understanding and Preventing Capacity Loss in Reinforcement Learning ICLR 2022
Generalised Policy Improvement with Geometric Policy Composition ICML 2022
Learning Dynamics and Generalization in Deep Reinforcement Learning ICML 2022
The Value-Improvement Path: Towards Better Representations for Reinforcement Learning AAAI 2021
The Difficulty of Passive Learning in Deep Reinforcement Learning NIPS 2021
On the Effect of Auxiliary Tasks on Representation Dynamics AISTATS 2021
Counterfactual Credit Assignment in Model-Free Reinforcement Learning ICML 2021
Revisiting Peng’s Q($Ξ»$) for Modern Reinforcement Learning ICML 2021
On the Expressivity of Markov Reward NIPS 2021
Temporally-Extended Ξ΅-Greedy Exploration ICLR 2021
Revisiting Fundamentals of Experience Replay ICML 2020
Adaptive Trade-Offs in Off-Policy Learning AISTATS 2020
Conditional Importance Sampling for Off-Policy Learning AISTATS 2020
Fast Task Inference with Variational Intrinsic Successor Features ICLR 2020
Recurrent Experience Replay in Distributed Reinforcement Learning ICLR 2019
A Geometric Perspective on Optimal Representations for Reinforcement Learning NIPS 2019
Statistics and Samples in Distributional Reinforcement Learning ICML 2019
The Termination Critic AISTATS 2019
Hindsight Credit Assignment NIPS 2019
Distributed Distributional Deterministic Policy Gradients ICLR 2018
Autoregressive Quantile Networks for Generative Modeling ICML 2018
Implicit Quantile Networks for Distributional Reinforcement Learning ICML 2018
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning ICLR 2018
An Analysis of Categorical Distributional Reinforcement Learning AISTATS 2018
Successor Features for Transfer in Reinforcement Learning NIPS 2017
A Distributional Perspective on Reinforcement Learning ICML 2017