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Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons

Publications associées (50)

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Reinforcement learning (RL) is crucial for learning to adapt to new environments. In RL, the prediction error is an important component that compares the expected and actual rewards. Dopamine plays a critical role in encoding these prediction errors. In my ...

EPFL2024

Humans and animals constantly adapt to their environment over the course of their life. This thesis seeks to integrate various timescales of adaptation, ranging from the adaptation of synaptic connections between spiking neurons (milliseconds), rapid behav ...

EPFL2023

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We are constantly bombarded by sensory information and constantly making decisions on how to act. In order to optimally adapt behavior, we must judge which sequences of sensory inputs and actions lead to successful outcomes in specific circumstances. Neuro ...

Oxford2023

Dopamine Dynamics in Reward-based Learning

Jun Huang

Dopamine signals are thought to be important for reward-based learning and appear to play important roles in regulating synaptic plasticity in the nucleus accumbens and the striatum. However, the precise neuronal circuit mechanisms underlying the learning ...

EPFL2022

Reward timing matters in motor learning

Pierre Theopistos Vassiliadis, Julie Duqué

Reward timing, that is, the delay after which reward is delivered following an action is known to strongly influence reinforcement learning. Here, we asked if reward timing could also modulate how people learn and consolidate new motor skills. In 60 health ...

CELL PRESS2022

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

Alexander Mathis, Mackenzie Mathis

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

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

Surprise-based model estimation in reinforcement learning: algorithms and brain signatures

Vasiliki Liakoni

Learning how to act and adapting to unexpected changes are remarkable capabilities of humans and other animals. In the absence of a direct recipe to follow in life, behaviour is often guided by rewarding and by surprising events. A positive or a negative o ...

EPFL2021

Ageing and reinforcement learning

Michael Herzog, Wei-Hsiang Lin

Learning to achieve one’s goal in a complex environment is a complicated task. In reinforcement learning (RL) tasks, an agent interacts with the environment to learn optimal actions. In humans, striatal areas are strongly involved in these tasks. During ag ...

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