Search Results for author: Pedro A. Ortega

Found 20 papers, 6 papers with code

Model-Free Risk-Sensitive Reinforcement Learning

no code implementations4 Nov 2021 Grégoire Delétang, Jordi Grau-Moya, Markus Kunesch, Tim Genewein, Rob Brekelmans, Shane Legg, Pedro A. Ortega

Since the Gaussian free energy is known to be a certainty-equivalent sensitive to the mean and the variance, the learning rule has applications in risk-sensitive decision-making.

Decision Making reinforcement-learning +1

Meta-trained agents implement Bayes-optimal agents

no code implementations NeurIPS 2020 Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro A. Ortega

Memory-based meta-learning is a powerful technique to build agents that adapt fast to any task within a target distribution.


Action and Perception as Divergence Minimization

1 code implementation3 Sep 2020 Danijar Hafner, Pedro A. Ortega, Jimmy Ba, Thomas Parr, Karl Friston, Nicolas Heess

While the narrow objectives correspond to domain-specific rewards as typical in reinforcement learning, the general objectives maximize information with the environment through latent variable models of input sequences.

Decision Making Representation Learning

Intrinsic Social Motivation via Causal Influence in Multi-Agent RL

no code implementations ICLR 2019 Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Caglar Gulcehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas

Therefore, we also employ influence to train agents to use an explicit communication channel, and find that it leads to more effective communication and higher collective reward.

counterfactual Counterfactual Reasoning +3

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

no code implementations26 Feb 2019 Tom Everitt, Pedro A. Ortega, Elizabeth Barnes, Shane Legg

Modeling the agent-environment interaction using causal influence diagrams, we can answer two fundamental questions about an agent's incentives directly from the graph: (1) which nodes can the agent have an incentivize to observe, and (2) which nodes can the agent have an incentivize to control?

Reinforcement Learning (RL)

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

3 code implementations ICLR 2019 Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Caglar Gulcehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas

We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over other agents' actions.

counterfactual Counterfactual Reasoning +3

Modeling Friends and Foes

no code implementations30 Jun 2018 Pedro A. Ortega, Shane Legg

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

AI Safety Gridworlds

2 code implementations27 Nov 2017 Jan Leike, Miljan Martic, Victoria Krakovna, Pedro A. Ortega, Tom Everitt, Andrew Lefrancq, Laurent Orseau, Shane Legg

We present a suite of reinforcement learning environments illustrating various safety properties of intelligent agents.

reinforcement-learning Reinforcement Learning (RL) +1

Human Decision-Making under Limited Time

no code implementations NeurIPS 2016 Pedro A. Ortega, Alan A. Stocker

Subjective expected utility theory assumes that decision-makers possess unlimited computational resources to reason about their choices; however, virtually all decisions in everyday life are made under resource constraints - i. e. decision-makers are bounded in their rationality.

Decision Making

Memory shapes time perception and intertemporal choices

no code implementations18 Apr 2016 Pedro A. Ortega, Naftali Tishby

There is a consensus that human and non-human subjects experience temporal distortions in many stages of their perceptual and decision-making systems.

Decision Making

Information-Theoretic Bounded Rationality

no code implementations21 Dec 2015 Pedro A. Ortega, Daniel A. Braun, Justin Dyer, Kee-Eung Kim, Naftali Tishby

Bounded rationality, that is, decision-making and planning under resource limitations, is widely regarded as an important open problem in artificial intelligence, reinforcement learning, computational neuroscience and economics.

Decision Making

Belief Flows of Robust Online Learning

no code implementations26 May 2015 Pedro A. Ortega, Koby Crammer, Daniel D. Lee

This paper introduces a new probabilistic model for online learning which dynamically incorporates information from stochastic gradients of an arbitrary loss function.

General Classification regression +1

Subjectivity, Bayesianism, and Causality

no code implementations15 Jul 2014 Pedro A. Ortega

Bayesian probability theory is one of the most successful frameworks to model reasoning under uncertainty.

An Adversarial Interpretation of Information-Theoretic Bounded Rationality

no code implementations22 Apr 2014 Pedro A. Ortega, Daniel D. Lee

Here, we show that a single-agent free energy optimization is equivalent to a game between the agent and an imaginary adversary.

Decision Making

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