Jordi Grau-Moya

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 30

Top Topics

Markov Decision Process

Reinforcement Learning

Stochastic Games

Top Venues

Publications

Anian Ruoss, Grégoire Delétang, Sourabh Medapati, Jordi Grau-Moya, Li Kevin Wenliang, Elliot Catt, John Reid, Tim Genewein
Grandmaster-Level Chess Without Search. CoRR (2024)
Jordi Grau-Moya, Tim Genewein, Marcus Hutter, Laurent Orseau, Grégoire Delétang, Elliot Catt, Anian Ruoss, Li Kevin Wenliang, Christopher Mattern, Matthew Aitchison, Joel Veness
Learning Universal Predictors. CoRR (2024)
Grégoire Delétang, Anian Ruoss, Paul-Ambroise Duquenne, Elliot Catt, Tim Genewein, Christopher Mattern, Jordi Grau-Moya, Li Kevin Wenliang, Matthew Aitchison, Laurent Orseau, Marcus Hutter, Joel Veness
Language Modeling Is Compression. ICLR (2024)
Elliot Catt, Jordi Grau-Moya, Marcus Hutter, Matthew Aitchison, Tim Genewein, Grégoire Delétang, Kevin Li, Joel Veness
Self-Predictive Universal AI. NeurIPS (2023)
Anian Ruoss, Grégoire Delétang, Tim Genewein, Jordi Grau-Moya, Róbert Csordás, Mehdi Bennani, Shane Legg, Joel Veness
Randomized Positional Encodings Boost Length Generalization of Transformers. CoRR (2023)
Tim Genewein, Grégoire Delétang, Anian Ruoss, Li Kevin Wenliang, Elliot Catt, Vincent Dutordoir, Jordi Grau-Moya, Laurent Orseau, Marcus Hutter, Joel Veness
Memory-Based Meta-Learning on Non-Stationary Distributions. CoRR (2023)
Grégoire Delétang, Anian Ruoss, Jordi Grau-Moya, Tim Genewein, Li Kevin Wenliang, Elliot Catt, Chris Cundy, Marcus Hutter, Shane Legg, Joel Veness, Pedro A. Ortega
Neural Networks and the Chomsky Hierarchy. ICLR (2023)
Grégoire Delétang, Anian Ruoss, Paul-Ambroise Duquenne, Elliot Catt, Tim Genewein, Christopher Mattern, Jordi Grau-Moya, Li Kevin Wenliang, Matthew Aitchison, Laurent Orseau, Marcus Hutter, Joel Veness
Language Modeling Is Compression. CoRR (2023)
Anian Ruoss, Grégoire Delétang, Tim Genewein, Jordi Grau-Moya, Róbert Csordás, Mehdi Bennani, Shane Legg, Joel Veness
Randomized Positional Encodings Boost Length Generalization of Transformers. ACL (2) (2023)
Tim Genewein, Grégoire Delétang, Anian Ruoss, Li Kevin Wenliang, Elliot Catt, Vincent Dutordoir, Jordi Grau-Moya, Laurent Orseau, Marcus Hutter, Joel Veness
Memory-Based Meta-Learning on Non-Stationary Distributions. ICML (2023)
Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch, Tim Genewein, Elliot Catt, Kevin Li, Anian Ruoss, Chris Cundy, Joel Veness, Jane X. Wang, Marcus Hutter, Christopher Summerfield, Shane Legg, Pedro A. Ortega
Beyond Bayes-optimality: meta-learning what you know you don't know. CoRR (2022)
Rob Brekelmans, Tim Genewein, Jordi Grau-Moya, Gregoire Detetang, Markus Kunesch, Shane Legg, Pedro A. Ortega
Your Policy Regularizer is Secretly an Adversary. Trans. Mach. Learn. Res. 2022 (2022)
Rob Brekelmans, Tim Genewein, Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch, Shane Legg, Pedro A. Ortega
Your Policy Regularizer is Secretly an Adversary. CoRR (2022)
Grégoire Delétang, Anian Ruoss, Jordi Grau-Moya, Tim Genewein, Li Kevin Wenliang, Elliot Catt, Marcus Hutter, Shane Legg, Pedro A. Ortega
Neural Networks and the Chomsky Hierarchy. CoRR (2022)
Pedro A. Ortega, Markus Kunesch, Grégoire Delétang, Tim Genewein, Jordi Grau-Moya, Joel Veness, Jonas Buchli, Jonas Degrave, Bilal Piot, Julien Pérolat, Tom Everitt, Corentin Tallec, Emilio Parisotto, Tom Erez, Yutian Chen, Scott E. Reed, Marcus Hutter, Nando de Freitas, Shane Legg
Shaking the foundations: delusions in sequence models for interaction and control. CoRR (2021)
John McLeod, Hrvoje Stojic, Vincent Adam, Dongho Kim, Jordi Grau-Moya, Peter Vrancx, Felix Leibfried
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow. CoRR (2021)
Grégoire Delétang, Jordi Grau-Moya, Markus Kunesch, Tim Genewein, Rob Brekelmans, Shane Legg, Pedro A. Ortega
Model-Free Risk-Sensitive Reinforcement Learning. CoRR (2021)
Grégoire Delétang, Jordi Grau-Moya, Miljan Martic, Tim Genewein, Tom McGrath, Vladimir Mikulik, Markus Kunesch, Shane Legg, Pedro A. Ortega
Causal Analysis of Agent Behavior for AI Safety. CoRR (2021)
Janith C. Petangoda, Sergio Pascual-Diaz, Vincent Adam, Peter Vrancx, Jordi Grau-Moya
Disentangled Skill Embeddings for Reinforcement Learning. CoRR (2019)
Jordi Grau-Moya, Felix Leibfried, Peter Vrancx
Soft Q-Learning with Mutual-Information Regularization. ICLR (Poster) (2019)
Felix Leibfried, Jordi Grau-Moya
Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learning. CoRL (2019)
Felix Leibfried, Jordi Grau-Moya
Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learning. CoRR (2019)
Felix Leibfried, Sergio Pascual-Diaz, Jordi Grau-Moya
A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment. CoRR (2019)
Felix Leibfried, Sergio Pascual-Diaz, Jordi Grau-Moya
A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment. NeurIPS (2019)
Jordi Grau-Moya, Felix Leibfried, Haitham Bou-Ammar
Balancing Two-Player Stochastic Games with Soft Q-Learning. IJCAI (2018)
Jordi Grau-Moya, Felix Leibfried, Haitham Bou-Ammar
Balancing Two-Player Stochastic Games with Soft Q-Learning. CoRR (2018)
Jordi Grau-Moya, Matthias Krüger, Daniel A. Braun
Non-Equilibrium Relations for Bounded Rational Decision-Making in Changing Environments. Entropy 20 (1) (2018)
Felix Leibfried, Jordi Grau-Moya, Haitham Bou-Ammar
An Information-Theoretic Optimality Principle for Deep Reinforcement Learning. CoRR (2017)
Jordi Grau-Moya, Felix Leibfried, Tim Genewein, Daniel A. Braun
Planning with Information-Processing Constraints and Model Uncertainty in Markov Decision Processes. CoRR (2016)
Jordi Grau-Moya, Felix Leibfried, Tim Genewein, Daniel A. Braun
Planning with Information-Processing Constraints and Model Uncertainty in Markov Decision Processes. ECML/PKDD (2) (2016)
Tim Genewein, Felix Leibfried, Jordi Grau-Moya, Daniel Alexander Braun
Bounded Rationality, Abstraction, and Hierarchical Decision-Making: An Information-Theoretic Optimality Principle. Frontiers Robotics AI 2 (2015)
Jordi Grau-Moya, Daniel A. Braun
Adaptive information-theoretic bounded rational decision-making with parametric priors. CoRR (2015)
Jordi Grau-Moya, Daniel A. Braun
Bounded Rational Decision-Making in Changing Environments. CoRR (2013)
Pedro A. Ortega, Jordi Grau-Moya, Tim Genewein, David Balduzzi, Daniel A. Braun
A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function. NIPS (2012)
Pedro A. Ortega, Jordi Grau-Moya, Tim Genewein, David Balduzzi, Daniel A. Braun
A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function CoRR (2012)
Jordi Grau-Moya, Antonio J. Pons Rivero, Jordi García-Ojalvo
Noise-induced up/Down Dynamics in Scale-Free neuronal Networks. Int. J. Bifurc. Chaos 22 (7) (2012)
Jordi Grau-Moya, Pedro A. Ortega, Daniel A. Braun
Risk-Sensitivity in Bayesian Sensorimotor Integration. PLoS Comput. Biol. 8 (9) (2012)