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Found 20 papers, 3 papers with code
Sim2Rec: A Simulator-based Decision-making Approach to Optimize Real-World Long-term User Engagement in Sequential Recommender Systems
However, building a user simulator with no reality-gap, i. e., can predict user's feedback exactly, is unrealistic because the users' reaction patterns are complex and historical logs for each user are limited, which might mislead the simulator-based recommendation policy.
A Unified Representation Framework for Rideshare Marketplace Equilibrium and Efficiency
Ridesharing platforms are a type of two-sided marketplace where ``supply-demand balance'' is critical for market efficiency and yet is complex to define and analyze.
Spatio-temporal Incentives Optimization for Ride-hailing Services with Offline Deep Reinforcement Learning
Motivated by this observation, we make an attempt to optimize the distribution of demand to handle this problem by learning the long-term spatio-temporal values as a guideline for pricing strategy.
Reinforcement Learning in the Wild: Scalable RL Dispatching Algorithm Deployed in Ridehailing Marketplace
In this study, a real-time dispatching algorithm based on reinforcement learning is proposed and for the first time, is deployed in large scale.
Offline Model-based Adaptable Policy Learning
Current offline reinforcement learning methods commonly learn in the policy space constrained to in-support regions by the offline dataset, in order to ensure the robustness of the outcome policies.
A Deep Value-network Based Approach for Multi-Driver Order Dispatching
In this work, we propose a deep reinforcement learning based solution for order dispatching and we conduct large scale online A/B tests on DiDi's ride-dispatching platform to show that the proposed method achieves significant improvement on both total driver income and user experience related metrics.
Value Function is All You Need: A Unified Learning Framework for Ride Hailing Platforms
In this paper we propose a unified value-based dynamic learning framework (V1D3) for tackling both tasks.
Reinforcement Learning for Ridesharing: An Extended Survey
In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement learning approaches to decision optimization problems in a typical ridesharing system.
Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning
We present a new practical framework based on deep reinforcement learning and decision-time planning for real-world vehicle repositioning on ride-hailing (a type of mobility-on-demand, MoD) platforms.
Bayesian Meta-reinforcement Learning for Traffic Signal Control
This framework is based on our proposed fast-adaptation variation to Gradient-EM Bayesian Meta-learning and the fast-update advantage of DQN, which allows for fast adaptation to new scenarios with continual learning ability and robustness to uncertainty.
Hierarchical Adaptive Contextual Bandits for Resource Constraint based Recommendation
In this paper, we propose a hierarchical adaptive contextual bandit method (HATCH) to conduct the policy learning of contextual bandits with a budget constraint.
Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem
Order dispatching and driver repositioning (also known as fleet management) in the face of spatially and temporally varying supply and demand are central to a ride-sharing platform marketplace.
Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching
Improving the efficiency of dispatching orders to vehicles is a research hotspot in online ride-hailing systems.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Similarity Kernel and Clustering via Random Projection Forests
Similarity plays a fundamental role in many areas, including data mining, machine learning, statistics and various applied domains.
Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation
DEMER also derives a recommendation policy with a significantly improved performance in the test phase of the real application.
CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms
How to optimally dispatch orders to vehicles and how to trade off between immediate and future returns are fundamental questions for a typical ride-hailing platform.
Multiagent Systems
Cost-sensitive Selection of Variables by Ensemble of Model Sequences
Many applications require the collection of data on different variables or measurements over many system performance metrics.
Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining
In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are required to fulfill the given amount of trip demand.
HIPAD - A Hybrid Interior-Point Alternating Direction algorithm for knowledge-based SVM and feature selection
We consider classification tasks in the regime of scarce labeled training data in high dimensional feature space, where specific expert knowledge is also available.
Robust Low-rank Tensor Recovery: Models and Algorithms
Robust tensor recovery plays an instrumental role in robustifying tensor decompositions for multilinear data analysis against outliers, gross corruptions and missing values and has a diverse array of applications.
