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Learning to Delay in Ride-Sourcing Systems: A Multi-Agent Deep Reinforcement Learning Framework
11 references·0 citing·45 second hop
Learning to Delay in Ride-Sourcing Systems: A Multi-Agent Deep Reinforcement Learning Framework (2020)Learning to Delay in Ride-Sourcing System…Thickness and Information in Dynamic Matching Markets (2019)Thickness and Information in …Preference-Aware Task Assignment in On-Demand Taxi Dispatching: An Online Stable Matching Approach (2019)Preference-Aware Task Assignm…Optimizing matching time interval and matching radius in on-demand ride-sourcing markets (2019)Optimizing matching time inte…Hexagon-Based Convolutional Neural Network for Supply-Demand Forecasting of Ride-Sourcing Services (2018)Hexagon-Based Convolutional N…Emergence of Grounded Compositional Language in Multi-Agent Populations (2018)Emergence of Grounded Composi…Deep Reinforcement Learning for Sponsored Search Real-time Bidding (2018)Deep Reinforcement Learning f…A universal distribution law of network detour ratios (2018)A universal distribution law …A reinforcement learning framework for the adaptive routing problem in stochastic time-dependent network (2018)A reinforcement learning fram…Real-Time Bidding by Reinforcement Learning in Display Advertising (2017)Real-Time Bidding by Reinforc…Emergence of Grounded Compositional Language in Multi-Agent Populations (2017)Emergence of Grounded Composi…DeepSD: Supply-Demand Prediction for Online Car-Hailing Services Using Deep Neural Networks (2017)DeepSD: Supply-Demand Predict…Ad click prediction (2013)Real-time bidding for online advertising (2013)Optimal real-time bidding for display advertising (2014)Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising (2018)Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising (2018)Online Display Advertising Markets: A Literature Review and Future Directions (2020)How To Do Things With Words (1975)Spontaneous evolution of linguistic structure-an iterated learning model of the emergence of regularity and irregularity (2001)Predicting Pragmatic Reasoning in Language Games (2012)Natural Language Does Not Emerge ‘Naturally’ in Multi-Agent Dialog (2017)If software is narrative: Joseph Weizenbaum, artificial intelligence and the biographies of ELIZA (2018)Transportation mode inference from anonymized and aggregated mobile phone call detail records (2010)Modeling human location data with mixtures of kernel densities (2014)Deep learning for event-driven stock prediction (2015)Geospatial data to images: A deep-learning framework for traffic forecasting (2018)Physical-Virtual Collaboration Modeling for Intra- and Inter-Station Metro Ridership Prediction (2020)DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction (2021)A Computer Movie Simulating Urban Growth in the Detroit Region (1970)Understanding individual human mobility patterns (2008)Limits of Predictability in Human Mobility (2010)Inter-Platform Competition in a Regulated Ride-Hail Market With Pooling (2020)Reliability of the traffic network against cascading failures with individuals acting independently or collectively (2023)College Admissions and the Stability of Marriage (1962)The Stable Marriage Problem: Structure and Algorithms (1991)The Stable Marriage Problem: Structure and Algorithms (1991)A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)Economic analysis of ride-sourcing markets (2016)Forecasting the Subway Passenger Flow Under Event Occurrences With Social Media (2016)Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform (2022)Multi-unit auctions with budget-constrained bidders (2005)Dynamics of bid optimization in online advertisement auctions (2007)Optimal display-ad allocation with guaranteed contracts and supply side platforms (2019)A Survey on Bid Optimization in Real-Time Bidding Display Advertising (2023)Dynamic Marketing Uplift Modeling: A Symmetry-Preserving Framework Integrating Causal Forests with Deep Reinforcement Learning for Personalized Intervention Strategies (2025)2013 IEEE International Conference on Big Data (2013)On-Demand Service Platforms (2016)Coordinating Supply and Demand on an On-Demand Service Platform with Impatient Customers (2018)Pairwise kidney exchange (2005)Dynamic Kidney Exchange (2009)The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response (2013)Ride-Sourcing modeling and pricing in non-equilibrium two-sided markets (2019)Optimal dynamic matching (2020)We Are on the Way: Analysis of On-Demand Ride-Hailing Systems (2020)Compositionality and Generalization In Emergent Languages (2020)Emergent Multi-Agent Communication in the Deep Learning Era (2020)
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References

  • 2018Emergence of Grounded Compositional Language in Multi-Agent Populations447 cited
  • 2017Emergence of Grounded Compositional Language in Multi-Agent Populations240 cited
  • 2017DeepSD: Supply-Demand Prediction for Online Car-Hailing Services Using Deep Neural Networks205 cited
  • 2017Real-Time Bidding by Reinforcement Learning in Display Advertising196 cited
  • 2019Optimizing matching time interval and matching radius in on-demand ride-sourcing markets191 cited
  • 2019Thickness and Information in Dynamic Matching Markets172 cited
  • 2018Hexagon-Based Convolutional Neural Network for Supply-Demand Forecasting of Ride-Sourcing Services168 cited
  • 2018A universal distribution law of network detour ratios100 cited
  • 2018Deep Reinforcement Learning for Sponsored Search Real-time Bidding96 cited
  • 2019Preference-Aware Task Assignment in On-Demand Taxi Dispatching: An Online Stable Matching Approach95 cited
  • 2018A reinforcement learning framework for the adaptive routing problem in stochastic time-dependent network63 cited

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