Human locomotion with reinforcement learning using bioinspired reward reshaping strategies

Nowakowski, Katharine; Carvalho, Philippe; Six, Jean-Baptiste; Maillet, Yann; Nguyen, Anh; Seghiri, Ismail; M'Pemba, Loick; Marcille, Theo; Ngo, Sy Toan; Dao, Tien Tuan

doi:10.1007/s11517-020-02309-3

Cited by 9 publications

(2 citation statements)

References 32 publications

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“…The category policy should include a probability of actions that is not the ideal one but very close to it if compared to the fully randomized policy. Beginning from the task category policy, a tailored user-agent interaction would be developed, and the fineness of the ideal individual-customized policies can be programmed (Nowakowski et al, 2021 ).…”

Section: Combined Rl and Vrmentioning

confidence: 99%

Combining reinforcement learning and virtual reality in mild neurocognitive impairment: a new usability assessment on patients and caregivers

Stasolla,

Di Gioia

2023

Front. Aging Neurosci.

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Section: Combined Rl and Vrmentioning

confidence: 99%

Combining reinforcement learning and virtual reality in mild neurocognitive impairment: a new usability assessment on patients and caregivers

Stasolla,

Di Gioia

2023

Front. Aging Neurosci.

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“…Machine learning [16] and artificial intelligence [17] spurred the search for classifiers to recognize patterns in human navigation. As in this paper, we focus mainly on using K-Means [1] and (HAC) [2] to analyze the VR Magic Carpet TM [3,18] output and dig further into the many classes acquired via an early kinematic participantbased data analysis [5].…”

Section: Introductionmentioning

confidence: 99%

Clustering analysis of human navigation trajectories in a visuospatial memory locomotor task using K-Means and hierarchical agglomerative clustering

Annaki¹,

Rahmoune²,

Bourhaleb³

et al. 2022

E3S Web Conf.

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Throughout this study, we employed unsupervised machine learning clustering algorithms, namely K-Means [1] and hierarchical agglomerative clustering (HAC) [2], to explore human locomotion and wayfinding using a VR Magic Carpet (VMC) [3], a table test version known as the Corsi Block Tapping task (CBT) [4]. This variation was carried out in the context of a virtual reality experimental setup. The participants were required to memorize a sequence of target positions projected on the rug and walk to each target figuring in the displayed sequence. the participant’s trajectory was collected and analyzed from a kinematic perspective. An earlier study [5] identified three different categories, but the classification remained ambiguous, implying that they include both kinds of individuals (normal and patients with cognitive spatial impairments). On this basis, we utilized K-Means and HAC to distinguish the navigation behavior of patients from normal individuals, emphasizing the most important discrepancies and then delving deeper to gain more insights.

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Computational Analysis of Human Navigation Trajectories in the VR Magic Carpet ™ Using K-Means

Annaki¹,

Rahmoune²,

Bourhaleb³

2023

Lecture Notes in Electrical Engineering

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Human locomotion with reinforcement learning using bioinspired reward reshaping strategies

Cited by 9 publications

References 32 publications

Combining reinforcement learning and virtual reality in mild neurocognitive impairment: a new usability assessment on patients and caregivers

Combining reinforcement learning and virtual reality in mild neurocognitive impairment: a new usability assessment on patients and caregivers

Clustering analysis of human navigation trajectories in a visuospatial memory locomotor task using K-Means and hierarchical agglomerative clustering

Computational Analysis of Human Navigation Trajectories in the VR Magic Carpet ™ Using K-Means

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