Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-agent Reinforcement Learning

Debard, Quentin; Dibangoye, Jilles; Canu, Stéphane; Wolf, Christian

doi:10.1007/978-3-030-46133-1_3

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…A model-oriented optimization of user satisfaction during interaction is the main goal of an adaptive interface proposed by [29]. The reward model in this work is a simple sum of L1 and L2 distances between the vertices of the object (o) and the target (t):…”

Section: Creating a Reward Modelmentioning

confidence: 99%

Mobile User Interface Adaptation Based on Usability Reward Model and Multi-Agent Reinforcement Learning

Vidmanov,

Alfimtsev

2024

MTI

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Today, reinforcement learning is one of the most effective machine learning approaches in the tasks of automatically adapting computer systems to user needs. However, implementing this technology into a digital product requires addressing a key challenge: determining the reward model in the digital environment. This paper proposes a usability reward model in multi-agent reinforcement learning. Well-known mathematical formulas used for measuring usability metrics were analyzed in detail and incorporated into the usability reward model. In the usability reward model, any neural network-based multi-agent reinforcement learning algorithm can be used as the underlying learning algorithm. This paper presents a study using independent and actor-critic reinforcement learning algorithms to investigate their impact on the usability metrics of a mobile user interface. Computational experiments and usability tests were conducted in a specially designed multi-agent environment for mobile user interfaces, enabling the implementation of various usage scenarios and real-time adaptations.

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Section: Creating a Reward Modelmentioning

confidence: 99%

Mobile User Interface Adaptation Based on Usability Reward Model and Multi-Agent Reinforcement Learning

Vidmanov,

Alfimtsev

2024

MTI

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DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning

Jaunet

Vuillemot

Wolf

2020

Computer Graphics Forum

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We present DRLViz, a visual analytics interface to interpret the internal memory of an agent (e.g. a robot) trained using deep reinforcement learning. This memory is composed of large temporal vectors updated when the agent moves in an environment and is not trivial to understand due to the number of dimensions, dependencies to past vectors, spatial/temporal correlations, and co‐correlation between dimensions. It is often referred to as a black box as only inputs (images) and outputs (actions) are intelligible for humans. Using DRLViz, experts are assisted to interpret decisions using memory reduction interactions, and to investigate the role of parts of the memory when errors have been made (e.g. wrong direction). We report on DRLViz applied in the context of video games simulators (ViZDoom) for a navigation scenario with item gathering tasks. We also report on experts evaluation using DRLViz, and applicability of DRLViz to other scenarios and navigation problems beyond simulation games, as well as its contribution to black box models interpretability and explain‐ability in the field of visual analytics.

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Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-agent Reinforcement Learning

Cited by 2 publications

References 14 publications

Mobile User Interface Adaptation Based on Usability Reward Model and Multi-Agent Reinforcement Learning

Mobile User Interface Adaptation Based on Usability Reward Model and Multi-Agent Reinforcement Learning

DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning

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