Výsledky vyhledávání - "Exogenous information"

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Networkwide Traffic State Forecasting Using Exogenous Information: A Multi-Dimensional Graph Attention-Based Approach

Traffic state forecasting is crucial for traffic management and control strategies, as well as user- and system-level decision making in the transportation network. While traffic forecasting has been approached with a variety of techniques over the l

Externí odkaz: http://arxiv.org/abs/2310.12353

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Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information

Autor: Islam, Riashat, Tomar, Manan, Lamb, Alex, Efroni, Yonathan, Zang, Hongyu, Didolkar, Aniket, Misra, Dipendra, Li, Xin, van Seijen, Harm, Combes, Remi Tachet des, Langford, John

Learning to control an agent from data collected offline in a rich pixel-based visual observation space is vital for real-world applications of reinforcement learning (RL). A major challenge in this setting is the presence of input information that i

Externí odkaz: http://arxiv.org/abs/2211.00164

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Fast and Slow Optimal Trading with Exogenous Information

Autor: Cont, Rama, Micheli, Alessandro, Neuman, Eyal

We consider a stochastic game between a slow institutional investor and a high-frequency trader who are trading a risky asset and their aggregated order-flow impacts the asset price. We model this system by means of two coupled stochastic control pro

Externí odkaz: http://arxiv.org/abs/2210.01901

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Dynamics of a Binary Option Market with Exogenous Information and Price Sensitivity

Autor: Gampe, Hannah, Griffin, Christopher

In this paper, we derive and analyze a continuous of a binary option market with exogenous information. The resulting non-linear system has a discontinuous right hand side, which can be analyzed using zero-dimensional Filippov surfaces. Under general

Externí odkaz: http://arxiv.org/abs/2206.07132

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Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information

Autor: Efroni, Yonathan, Foster, Dylan J., Misra, Dipendra, Krishnamurthy, Akshay, Langford, John

In real-world reinforcement learning applications the learner's observation space is ubiquitously high-dimensional with both relevant and irrelevant information about the task at hand. Learning from high-dimensional observations has been the subject

Externí odkaz: http://arxiv.org/abs/2206.04282

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Social-Media Activity Forecasting with Exogenous Information Signals

Autor: Ng, Kin Wai, Horawalavithana, Sameera, Iamnitchi, Adriana

Due to their widespread adoption, social media platforms present an ideal environment for studying and understanding social behavior, especially on information spread. Modeling social media activity has numerous practical implications such as support

Externí odkaz: http://arxiv.org/abs/2109.11024

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