Publications related to Model-based reinforcement learning and navigation in animals and machines

Simulating Learning Dynamics in Project Networks

In this paper we develop a multiagent simulation model to explore the impact of learning dynamics on the productive implementation of innovations in project networks comprised of designers and contractors. Though researchers generally agree that when firms ...

2009

Networks of mixed canonical-dissipative systems and dynamic hebbian learning

Max-Olivier Hongler, Julio Rodriguez

We study the dynamics of a network consisting of N diffusively coupled, stable- limit-cycle oscillators on which individual frequencies are parametrized by

w_k, k = 1, . . . ,N

. We introduce a learning rule which influences the wk by driving the system t ...

2009

Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward- based Scenarios

Dario Floreano, Claudio Mattiussi, Peter Dürr, Andrea Soltoggio

memory in biological neural networks. Similarly, artificial neural networks could benefit from modulatory dynamics when facing certain types of learning problem. Here we test this hypothesis by introducing modulatory neurons to enhance or dampen neural pla ...

MIT Press2008

Selective learning and memory impairments in mice deficient for polysialylated NCAM in adulthood

Maria del Carmen Sandi Perez, Kamila Markram

The neural cell adhesion molecule (NCAM) has been implicated in regulating synaptic plasticity mechanisms as well as memory consolidation processes. Attachment of polysialic acid to NCAM (PSA-NCAM) has been reported to down-regulate its adhesive forces, a ...

2007

Effects of stress and genotype on meta-parameter dynamics in reinforcement learning

Maria del Carmen Sandi Perez, Wulfram Gerstner, Gediminas Luksys, Jérémie Knüsel

Stress and genetic background regulate different aspects of behavioral learning through the action of stress hormones and neuromodulators. In reinforcement learning (RL) models, meta-parameters such as learning rate, future reward discount factor, and expl ...

2007

Adding prediction risk to the theory of reward learning

This article analyzes the simple Rescorla-Wagner learning rule from the vantage point of least squares learning theory. In particular, it suggests how measures of risk, such as prediction risk, can be used to adjust the learning constant in reinforcement l ...

2007

Adding prediction risk to the theory of reward learning

This article analyzes the simple Rescorla-Wagner learning rule from the vantage point of least squares learning theory. In particular, it suggests how measures of risk, such as prediction risk, can be used to adjust the learning constant in reinforcement l ...

2007

Spatial learning and navigation in the rat

Ricardo Andres Chavarriaga Lozano

Animals behave in different ways depending on the specific task they are required to solve. In certain cases, if a cue marks the goal location, they can rely on simple stimulusresponse associations. In contrast, other tasks require the animal to be endowed ...

EPFL2005

Competition between cue response and place response: A model of rat navigation behaviour

Wulfram Gerstner, Ricardo Andres Chavarriaga Lozano, Thomas Strösslin

Different neural systems are involved in animal navigation depending on the type of task. Experimental studies support the idea that the hippocampus is necessary to learn a spatial representation required to navigate toward hidden goals (place response), w ...

Taylor & Francis2005

Méthodes infographiques pour l'apprentissage des agents autonomes virtuels

Toni Conde

There are two primary approaches to behavioural animation of an Autonomous Virtual Agent (AVA). The first one, or behavioural model, defines how AVA reacts to the current state of its environment. In the second one, or cognitive model, this AVA uses a thou ...

EPFL2005

Model-based reinforcement learning and navigation in animals and machines

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Simulating Learning Dynamics in Project Networks

Networks of mixed canonical-dissipative systems and dynamic hebbian learning

Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward- based Scenarios

Selective learning and memory impairments in mice deficient for polysialylated NCAM in adulthood

Effects of stress and genotype on meta-parameter dynamics in reinforcement learning

Adding prediction risk to the theory of reward learning

Adding prediction risk to the theory of reward learning

Spatial learning and navigation in the rat

Competition between cue response and place response: A model of rat navigation behaviour

Méthodes infographiques pour l'apprentissage des agents autonomes virtuels

Selective learning and memory impairments in mice deficient for polysialylated NCAM in adulthood

Simulating Learning Dynamics in Project Networks

Networks of mixed canonical-dissipative systems and dynamic hebbian learning

Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward- based Scenarios

Effects of stress and genotype on meta-parameter dynamics in reinforcement learning

Adding prediction risk to the theory of reward learning

Adding prediction risk to the theory of reward learning

Méthodes infographiques pour l'apprentissage des agents autonomes virtuels

Competition between cue response and place response: A model of rat navigation behaviour

Spatial learning and navigation in the rat