With the advent of ride-sharing services, there is a huge increase in the number of people who rely on them for various needs. Most of the earlier approaches tackling this issue required handcrafted functions for estimating travel times and passenger waiting times. Traditional Reinforcement Learning (RL) based methods attempting to solve the ridesharing problem are unable to accurately model the complex environment in which taxis operate. Prior Multi-Agent Deep RL based methods based on Independent DQN (IDQN) learn decentralized value functions prone to instability due to the concurrent learning and exploring of multiple agents. Our proposed method based on QMIX is able to achieve centralized training with decentralized execution. We show that our model performs better than the IDQN baseline on a fixed grid size and is able to generalize well to smaller or larger grid sizes. Also, our algorithm is able to outperform IDQN baseline in the scenario where we have a variable number of passengers and cars in each episode.Code for our paper is publicly available at https://github.com/UMich-ML-Group/RL-Ridesharing. * indicates equal contribution Preprint. Under review.
This paper presents our new intelligent interactive robot, which is constructed to eagerly provide multi-functional services in an office environment. In order to endow a full interactive capability of our robots for realizing so-called human-robot interaction (HRI) , we propose sensor fusion based human detection and tracking system and human pose estimation to deal with a number of situations which may take place in the office environment. Not only by these perceptions, human interact with the robot also by some natural way, such as touching the interface screen and talking with the robot through microphone. Finally, the effectiveness of the proposed work is tested and validated by some of experiments.
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