266 citations · 1k across the 38 of their papers we have counts for
40 papers
Bounded-Rationality, Hedging, and Generalization
Pedro A. Ortega
A learner does not only fit data; it also determines how strongly the training sample may shape its output and how much distortion it can hedge. We study this relation as a bounded…
Notes on the Reward Representation of Posterior Updates
Pedro A. Ortega
Many ideas in modern control and reinforcement learning treat decision-making as inference: start from a baseline distribution and update it when a signal arrives. We ask when this…
Beyond Bayes-optimality: meta-learning what you know you don't know
Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch +11
Meta-training agents with memory has been shown to culminate in Bayes-optimal agents, which casts Bayes-optimality as the implicit solution to a numerical optimization problem rath…
Neural Networks and the Chomsky Hierarchy
Grégoire Delétang, Anian Ruoss, Jordi Grau-Moya +8
Reliable generalization lies at the heart of safe ML and AI. However, understanding when and how neural networks generalize remains one of the most important unsolved problems in t…
Your Policy Regularizer is Secretly an Adversary
Rob Brekelmans, Tim Genewein, Jordi Grau-Moya +4
Policy regularization methods such as maximum entropy regularization are widely used in reinforcement learning to improve the robustness of a learned policy. In this paper, we show…
Model-Free Risk-Sensitive Reinforcement Learning
Grégoire Delétang, Jordi Grau-Moya, Markus Kunesch +4
We extend temporal-difference (TD) learning in order to obtain risk-sensitive, model-free reinforcement learning algorithms. This extension can be regarded as modification of the R…