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SampleViz: Concept based Sampling for Policy Refinement in Deep Reinforcement Learning 17

SampleViz: Concept based Sampling for Policy Refinement in D...

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17th Pacific visualization Conference (PacificVis)

作者： Liang, Zhaohui Li, Guan Gu, Ruiqi Wang, Yang Shan, Guihua Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350393811;9798350393804

Deep reinforcement learning (DRL) aims to train software agents that can understand environments and learn effective strategies, and has achieved significant breakthroughs in performance and capabilities, particularly in areas such as Go, Atari games, and autonomous vehicles. Unlike traditional deep learning, the goals of reinforcement learning can be more abstract and require careful modification of reward functions. The training process involves unstructured sequential data, which can be difficult for human experts to analyze and gain insights from. To address this challenge, we propose SampleViz, a visual analytics system that enables flexible interaction between human experts and DRL sequence data, allowing for the extraction of crucial concepts from massive amounts of data and their provision to the agent. SampleViz transforms the tedious task of modifying reward functions and policy debugging into an engaging concept exploration process, allowing for the efficient integration of human expertise with automatic sampling algorithms for effective model improvement. Through case studies and expert feedback, we demonstrate that SampleViz can effectively assist experts in concept extraction and model improvement, and enables the incorporation of interpretability and human-in-the-loop concepts into DRL policy settings.

关键词： human-centered computing-visualization-visual analytics

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EmoGeoCity: Interactive visual Exploration of City's Historical and Cultural Evolution Based on Emotional Geography 17

EmoGeoCity: Interactive Visual Exploration of City's Histori...

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17th Pacific visualization Conference (PacificVis)

作者： Yang, Yutong Liu, Yinuo Bai, Qishuo Zhou, Tianyi Ye, Ziduo Dong, Xiaoju Shanghai Jiao Tong Univ SJTU Paris Elite Inst Technol Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Design Shanghai Peoples R China

ISBN: (纸本)9798350393811;9798350393804

A city's history and culture studies involves understanding the literary works and historical events that have shaped the city's identity. Increased availability of quantitative historical data has provided new opportunities. Taking Nanjing as an example, this paper proposes EmoGeoCity, a visual analytics system to study a city's cultural and historical evolution, through the use of digital humanities methods and emotional geography. The system incorporates sentiment analysis into historical research to quantify the emotional content of works and synthesize an overall emotion trend within a specific location. A dynamic emotional map, integrating locations, works, and events, enables a macroscopic observation of city emotions over time. An emotional polyline is designed to provide a microscopic interpretation of the emotion trend of a single location. Exploration through the system reveals the evolution of the city from an emotional geographic perspective, which gives insights for humanities researchers studying a city's history and literature, as well as for the general public interested in gaining knowledge on historical sites. Case and user studies illustrate the effectiveness and usability of our system.

关键词： human-centered computing-visualization-visual analytics

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