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1811.06272
Cited By
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search
International Conference on Learning Representations (ICLR), 2018
15 November 2018
Lars Buesing
T. Weber
Yori Zwols
S. Racanière
A. Guez
Jean-Baptiste Lespiau
N. Heess
CML
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Papers citing
"Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search"
49 / 99 papers shown
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Independent mechanism analysis, a new concept?
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Causal Influence Detection for Improving Efficiency in Reinforcement Learning
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278
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Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation
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213
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Counterfactual Credit Assignment in Model-Free Reinforcement Learning
International Conference on Machine Learning (ICML), 2020
Thomas Mesnard
T. Weber
Fabio Viola
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Éric Moulines
Marcus Hutter
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Rémi Munos
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234
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18 Nov 2020
Causal Campbell-Goodhart's law and Reinforcement Learning
International Conference on Agents and Artificial Intelligence (ICAART), 2020
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147
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Forethought and Hindsight in Credit Assignment
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Veronica Chelu
Doina Precup
H. V. Hasselt
262
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Disentangling causal effects for hierarchical reinforcement learning
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Raul Vicente
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212
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Multi-task Causal Learning with Gaussian Processes
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Virginia Aglietti
Theodoros Damoulas
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Javier I. González
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146
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Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
Andrew Bennett
Nathan Kallus
Lihong Li
Ali Mousavi
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167
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Counterfactual Data Augmentation using Locally Factored Dynamics
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238
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242
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122
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Reinforcement Learning and its Connections with Neuroscience and Psychology
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Ajay Subramanian
Sharad Chitlangia
V. Baths
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524
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25 Jun 2020
Provably Efficient Causal Reinforcement Learning with Confounded Observational Data
Lingxiao Wang
Zhuoran Yang
Zhaoran Wang
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176
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Counterfactually Guided Off-policy Transfer in Clinical Settings
Taylor W. Killian
Marzyeh Ghassemi
Shalmali Joshi
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239
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Causality and Batch Reinforcement Learning: Complementary Approaches To Planning In Unknown Domains
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Bradford T. Windsor
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155
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03 Jun 2020
Causal Bayesian Optimization
Virginia Aglietti
Xiaoyu Lu
Andrei Paleyes
Javier Gonz' alez
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142
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Counterfactual Off-Policy Training for Neural Response Generation
Qingfu Zhu
Weinan Zhang
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293
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Importance of using appropriate baselines for evaluation of data-efficiency in deep reinforcement learning for Atari
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132
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Counterfactual Policy Evaluation for Decision-Making in Autonomous Driving
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Alois Knoll
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183
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Value-driven Hindsight Modelling
Neural Information Processing Systems (NeurIPS), 2020
A. Guez
Fabio Viola
T. Weber
Lars Buesing
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David Silver
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144
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Resolving Spurious Correlations in Causal Models of Environments via Interventions
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219
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Causally Correct Partial Models for Reinforcement Learning
Danilo Jimenez Rezende
Ivo Danihelka
George Papamakarios
Nan Rosemary Ke
Ray Jiang
...
Jane X. Wang
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Lars Buesing
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226
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Hindsight Credit Assignment
Neural Information Processing Systems (NeurIPS), 2019
Anna Harutyunyan
Will Dabney
Thomas Mesnard
M. G. Azar
Bilal Piot
...
H. V. Hasselt
Greg Wayne
Satinder Singh
Doina Precup
Rémi Munos
176
84
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Causality for Machine Learning
Bernhard Schölkopf
CML
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345
516
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Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems
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Robert Osazuwa Ness
Kaushal Paneri
O. Vitek
98
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06 Nov 2019
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction
Neural Information Processing Systems (NeurIPS), 2019
C. Freeman
Luke Metz
David R Ha
162
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MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming
Symposium on Advances in Approximate Bayesian Inference (AABI), 2019
Yura N. Perov
L. Graham
Kostis Gourgoulias
Jonathan G. Richens
Ciarán M. Gilligan-Lee
Adam Baker
Saurabh Johri
LRM
237
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17 Oct 2019
Causal Induction from Visual Observations for Goal Directed Tasks
Sunjay Cauligi
Yuke Zhu
D. Stefan
Tamara Rezk
CML
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177
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03 Oct 2019
Causal Modeling for Fairness in Dynamical Systems
International Conference on Machine Learning (ICML), 2019
Elliot Creager
David Madras
T. Pitassi
R. Zemel
177
69
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Reward Tampering Problems and Solutions in Reinforcement Learning: A Causal Influence Diagram Perspective
Tom Everitt
Marcus Hutter
Ramana Kumar
Victoria Krakovna
372
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Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces
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Guy Lorberbom
Chris J. Maddison
N. Heess
Tamir Hazan
Daniel Tarlow
187
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205
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Hypothesis-Driven Skill Discovery for Hierarchical Deep Reinforcement Learning
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177
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Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models
International Conference on Machine Learning (ICML), 2019
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330
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International Conference on Learning Representations (ICLR), 2019
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Mohammad Babaeizadeh
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...
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CML
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256
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