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Neural Approximate Dynamic Programming for On-Demand Ride-Pooling

Neural Approximate Dynamic Programming for On-Demand Ride-Pooling

20 November 2019
Sanket Shah
Meghna Lowalekar
Pradeep Varakantham
ArXiv (abs)PDFHTML

Papers citing "Neural Approximate Dynamic Programming for On-Demand Ride-Pooling"

5 / 5 papers shown
Title
DECAF: Learning to be Fair in Multi-agent Resource Allocation
DECAF: Learning to be Fair in Multi-agent Resource Allocation
Ashwin Kumar
William Yeoh
149
1
0
06 Feb 2025
Efficient Ridesharing Order Dispatching with Mean Field Multi-Agent
  Reinforcement Learning
Efficient Ridesharing Order Dispatching with Mean Field Multi-Agent Reinforcement Learning
Minne Li
Zhiwei Qin
Qin
Yan Jiao
Yaodong Yang
Zhichen Gong
Jun Wang
Chenxi Wang
Guobin Wu
Jieping Ye
67
199
0
31 Jan 2019
Parameter Space Noise for Exploration
Parameter Space Noise for Exploration
Matthias Plappert
Rein Houthooft
Prafulla Dhariwal
Szymon Sidor
Richard Y. Chen
Xi Chen
Tamim Asfour
Pieter Abbeel
Marcin Andrychowicz
89
597
0
06 Jun 2017
Prioritized Experience Replay
Prioritized Experience Replay
Tom Schaul
John Quan
Ioannis Antonoglou
David Silver
OffRL
231
3,796
0
18 Nov 2015
Deep Reinforcement Learning with Double Q-learning
Deep Reinforcement Learning with Double Q-learning
H. V. Hasselt
A. Guez
David Silver
OffRL
177
7,678
0
22 Sep 2015
1