… a reinforcement learning (RL) based system to learn an effective policy for carpooling
that … For this purpose, first, we develop a deep neural network model, called ST-NN (Spatio-…

… approach for ride-sharing with carpooling, which can adapt to … We develop a distributed
optimized framework for vehicle … this policy follows as a special case of our proposed policy by …

… Optimizing taxi carpool policies via reinforcement learning and spatio-temporal mining. In:
2018 IEEE International Conference on Big Data (Big Data), pp. 1417–1426. …

… , and the policy gradient method is used to optimize the strategy… reinforcement learning
algorithm. Similar to the DQN algorithm that is a model-free and an off-policy algorithm (the policy …

… goods workloads by learning optimal dispatch policies from its … the reward function thereby
optimizing our objective function … models accept above 90% of passenger rideshare requests. …

… Voss, Increasing acceptance of free-floating car sharing systems using smart relocation
strategies: A survey based study of car2go hamburg, in: International Conference on …

… A multi-threaded reinforcement learning algorithm is … and pricing strategies are obtained by
reinforcement learning … , aiming at maximizing profits and optimizing resource utilization. The …

… , and convenient number of people to car pool with, (3) customer … The optimization problem
is formulated such that our novelty … Therefore, our algorithm learns the optimal policy for each …

… framework for the problem and policy evaluation with reinforcement learning in detail. In Section
5 we … More than one group of passengers can carpool in a vehicle at the same time, and …

… Route optimization: We suggest a cutting-edge algorithm for route optimization that takes into
… New algorithms and strategies might also seem as technology develops, that may similarly …