Report
A Comprehensive Workflow for Effective Imitation and Reinforcement Learning with Visual Analytics
| Τίτλος: | A Comprehensive Workflow for Effective Imitation and Reinforcement Learning with Visual Analytics |
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| Συγγραφείς: | Metz, Yannick, Schlegel, Udo, Seebacher, Daniel, El-Assady, Mennatallah, Keim, Daniel A. |
| Στοιχεία εκδότη: | The Eurographics Association, 2022. |
| Έτος έκδοσης: | 2022 |
| Θεματικοί όροι: | Reinforcement learning, Visual analytics, Human centered computing, CCS Concepts: Human-centered computing --> Visual analytics, Computing methodologies --> Reinforcement learning, Computing methodologies |
| Περιγραφή: | Multiple challenges hinder the application of reinforcement learning algorithms in experimental and real-world use cases even with recent successes in such areas. Such challenges occur at different stages of the development and deployment of such models. While reinforcement learning workflows share similarities with machine learning approaches, we argue that distinct challenges can be tackled and overcome using visual analytic concepts. Thus, we propose a comprehensive workflow for reinforcement learning and present an implementation of our workflow incorporating visual analytic concepts integrating tailored views and visualizations for different stages and tasks of the workflow. Yannick Metz, Udo Schlegel, Daniel Seebacher, Mennatallah El-Assady, and Daniel Keim Human-Model Collaboration and Personalization |
| Τύπος εγγράφου: | Research Conference object |
| Περιγραφή αρχείου: | application/pdf |
| DOI: | 10.2312/eurova.20221074 |
| Σύνδεσμος πρόσβασης: | http://nbn-resolving.de/urn:nbn:de:bsz:352-2-4e7iwot98daw9 |
| Αριθμός Καταχώρησης: | edsair.doi.dedup.....822e6c9e6023d7f4d6d59a355fa7c6e8 |
| Βάση Δεδομένων: | OpenAIRE |
| DOI: | 10.2312/eurova.20221074 |
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