Concept

Renforcement

Publications associées (127)

Striatal dopamine explains novelty-induced behavioral dynamics and individual variability in threat prediction

Animals both explore and avoid novel objects in the environment, but the neural mechanisms that underlie these behaviors and their dynamics remain uncharacterized. Here, we used multi-point tracking (DeepLabCut) and behavioral segmentation (MoSeq) to chara ...

CELL PRESS2022

Fear Learning: An Evolving Picture for Plasticity at Synaptic Afferents to the Amygdala

Ralf Schneggenburger, Denys Osypenko, Shriya Palchaudhuri

Unraveling the neuronal mechanisms of fear learning might allow neuroscientists to make links between a learned behavior and the underlying plasticity at specific synaptic connections. In fear learning, an innocuous sensory event such as a tone (called the ...

SAGE PUBLICATIONS INC2022

Optimal recovery of unsecured debt via interpretable reinforcement learning

Thomas Alois Weber, Michael Mark, Huanxi Liu

This paper addresses the issue of interpretability and auditability of reinforcement-learning agents employed in the recovery of unsecured consumer debt. To this end, we develop a deterministic policy-gradient method that allows for a natural integration o ...

2022

Deep Q-learning agent for building earthquake resisting wall

Katrin Beyer, Qianqing Wang, Ketson Roberto Maximiano Dos Santos

The construction of dry-joint brick masonry that is subject to seismic loading can be modelled as a strategy problem wherein bricks must be laid to form a load resisting structure. To learn how to mount bricks while considering relevant engineering criteri ...

2022

Reinforced Attention for Few-Shot Learning and Beyond

Tong Zhang

Few-shot learning aims to correctly recognize query samples from unseen classes given a limited number of support samples, often by relying on global embeddings of images. In this paper, we propose to equip the backbone network with an attention agent, whi ...

IEEE COMPUTER SOC2021

Reward boosts reinforcement-based motor learning

Friedhelm Christoph Hummel, Pierre Theopistos Vassiliadis, Julie Duqué

Besides relying heavily on sensory and reinforcement feedback, motor skill learning may also depend on the level of motivation experienced during training. Yet, how motivation by reward modulates motor learning remains unclear. In 90 healthy subjects, we i ...

2021

Resolving time conflicts in activity-based scheduling

Michel Bierlaire, Timothy Michael Hillel, Janody Pougala

In this paper, we present a novel activity-based scheduling model that combines a continuous optimisation framework for temporal scheduling decisions (i.e. activity timings and durations) with traditional discrete choice models for non-temporal choice dime ...

2021

Resolving time conflicts in activity-based scheduling: A case study of Lausanne

Michel Bierlaire, Timothy Michael Hillel, Janody Pougala

2021

Routing of Electric Vehicles With Intermediary Charging Stations: A Reinforcement Learning Approach

Christophe Ballif, Marina Dorokhova

In the past few years, the importance of electric mobility has increased in response to growing concerns about climate change. However, limited cruising range and sparse charging infrastructure could restrain a massive deployment of electric vehicles (EVs) ...

2021

Seismic Retrofitting of Existing Steel Structures With Buckling Restrained Bracings

Ariele Forni

The objective of this project is the seismic retrofit of an existent steel structure with buckling restrained braces (BRBs), and the proposition of a possible heightening. The structure that will be analysed is the GC building on the EPFL campus, in Ecuble ...

2021

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