Pedro A. Ortega

research

∙ 07/05/2022

Neural Networks and the Chomsky Hierarchy

Reliable generalization lies at the heart of safe ML and AI. However, un...

3 Grégoire Delétang, et al. ∙

research

∙ 11/04/2021

Model-Free Risk-Sensitive Reinforcement Learning

We extend temporal-difference (TD) learning in order to obtain risk-sens...

9 Grégoire Delétang, et al. ∙

research

∙ 10/20/2021

Shaking the foundations: delusions in sequence models for interaction and control

The recent phenomenal success of language models has reinvigorated machi...

68 Pedro A. Ortega, et al. ∙

research

∙ 03/05/2021

Causal Analysis of Agent Behavior for AI Safety

As machine learning systems become more powerful they also become increa...

26 Grégoire Delétang, et al. ∙

research

∙ 02/02/2021

Agent Incentives: A Causal Perspective

We present a framework for analysing agent incentives using causal influ...

14 Tom Everitt, et al. ∙

research

∙ 10/23/2020

Algorithms for Causal Reasoning in Probability Trees

Probability trees are one of the simplest models of causal generative pr...

2 Tim Genewein, et al. ∙

research

∙ 10/21/2020

Meta-trained agents implement Bayes-optimal agents

Memory-based meta-learning is a powerful technique to build agents that ...

8 Vladimir Mikulik, et al. ∙

research

∙ 09/03/2020

Action and Perception as Divergence Minimization

We introduce a unified objective for action and perception of intelligen...

10 Danijar Hafner, et al. ∙

research

∙ 05/15/2019

Meta reinforcement learning as task inference

Humans achieve efficient learning by relying on prior knowledge about th...

5 Jan Humplik, et al. ∙

research

∙ 05/08/2019

Meta-learning of Sequential Strategies

In this report we review memory-based meta-learning as a tool for buildi...

16 Pedro A. Ortega, et al. ∙

research

∙ 02/26/2019

Understanding Agent Incentives using Causal Influence Diagrams, Part I: Single Action Settings

Agents are systems that optimize an objective function in an environment...

0 Tom Everitt, et al. ∙

research

∙ 10/19/2018

Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning

We propose a unified mechanism for achieving coordination and communicat...

0 Natasha Jaques, et al. ∙

research

∙ 10/19/2018

Intrinsic Social Motivation via Causal Influence in Multi-Agent RL

We derive a new intrinsic social motivation for multi-agent reinforcemen...

0 Natasha Jaques, et al. ∙

research

∙ 06/30/2018

Modeling Friends and Foes

How can one detect friendly and adversarial behavior from raw data? Dete...

0 Pedro A. Ortega, et al. ∙

research

∙ 11/27/2017

AI Safety Gridworlds

We present a suite of reinforcement learning environments illustrating v...

0 Jan Leike, et al. ∙

research

∙ 10/06/2016

Human Decision-Making under Limited Time

Subjective expected utility theory assumes that decision-makers possess ...

0 Pedro A. Ortega, et al. ∙

research

∙ 04/18/2016

Memory shapes time perception and intertemporal choices

There is a consensus that human and non-human subjects experience tempor...

0 Pedro A. Ortega, et al. ∙

research

∙ 12/21/2015

Information-Theoretic Bounded Rationality

Bounded rationality, that is, decision-making and planning under resourc...

0 Pedro A. Ortega, et al. ∙

research

∙ 05/26/2015

Belief Flows of Robust Online Learning

This paper introduces a new probabilistic model for online learning whic...

0 Pedro A. Ortega, et al. ∙

research

∙ 07/15/2014

Subjectivity, Bayesianism, and Causality

Bayesian probability theory is one of the most successful frameworks to ...

0 Pedro A. Ortega, et al. ∙

research

∙ 04/22/2014

An Adversarial Interpretation of Information-Theoretic Bounded Rationality

Recently, there has been a growing interest in modeling planning with in...

0 Pedro A. Ortega, et al. ∙

research

∙ 03/18/2013

Generalized Thompson Sampling for Sequential Decision-Making and Causal Inference

Recently, it has been shown how sampling actions from the predictive dis...

0 Pedro A. Ortega, et al. ∙

research

∙ 06/09/2012

A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function

We propose a novel Bayesian approach to solve stochastic optimization pr...

0 Pedro A. Ortega, et al. ∙

research

∙ 05/17/2012

Free Energy and the Generalized Optimality Equations for Sequential Decision Making

The free energy functional has recently been proposed as a variational p...

0 Pedro A. Ortega, et al. ∙

research

∙ 11/03/2011

Bayesian Causal Induction

Discovering causal relationships is a hard task, often hindered by the n...

0 Pedro A. Ortega, et al. ∙

research

∙ 07/28/2011

Information, Utility & Bounded Rationality

Perfectly rational decision-makers maximize expected utility, but crucia...

0 Pedro A. Ortega, et al. ∙

research

∙ 02/16/2010

Convergence of Bayesian Control Rule

Recently, new approaches to adaptive control have sought to reformulate ...

0 Pedro A. Ortega, et al. ∙

research

∙ 02/07/2010

A Minimum Relative Entropy Controller for Undiscounted Markov Decision Processes

Adaptive control problems are notoriously difficult to solve even in the...

0 Pedro A. Ortega, et al. ∙

research

∙ 11/26/2009

A conversion between utility and information

Rewards typically express desirabilities or preferences over a set of al...

0 Pedro A. Ortega, et al. ∙

research

∙ 11/26/2009

A Bayesian Rule for Adaptive Control based on Causal Interventions

Explaining adaptive behavior is a central problem in artificial intellig...

0 Pedro A. Ortega, et al. ∙

Pedro A. Ortega

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