Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Shen, Macheng"'
Autor:
Shen, Macheng
Multiagent decision-making is a ubiquitous problem with many real-world applications, such as autonomous driving, multi-player video games, and robot team sports. Key challenges of multiagent learning include the presence of uncertainty in the other
Externí odkaz:
https://hdl.handle.net/1721.1/144626
Autor:
Shen, Macheng, How, Jonathan P.
Achieving the capability of adapting to ever-changing environments is a critical step towards building fully autonomous robots that operate safely in complicated scenarios. In multiagent competitive scenarios, agents may have to adapt to new opponent
Externí odkaz:
http://arxiv.org/abs/2203.07562
The complexity of multiagent reinforcement learning (MARL) in multiagent systems increases exponentially with respect to the agent number. This scalability issue prevents MARL from being applied in large-scale multiagent systems. However, one critica
Externí odkaz:
http://arxiv.org/abs/2003.01040
Autor:
Shen, Macheng, How, Jonathan P.
This paper presents an algorithmic framework for learning robust policies in asymmetric imperfect-information games, where the joint reward could depend on the uncertain opponent type (a private information known only to the opponent itself and its a
Externí odkaz:
http://arxiv.org/abs/1909.08735
Autor:
Shen, Macheng, How, Jonathan P
We pose an active perception problem where an autonomous agent actively interacts with a second agent with potentially adversarial behaviors. Given the uncertainty in the intent of the other agent, the objective is to collect further evidence to help
Externí odkaz:
http://arxiv.org/abs/1902.05644
This project report compares some known GAN and VAE models proposed prior to 2017. There has been significant progress after we finished this report. We upload this report as an introduction to generative models and provide some personal interpretati
Externí odkaz:
http://arxiv.org/abs/1812.05676
In this paper, we proposed the Interpenetrating Cooperative Localization (ICL) method to enhance the localization accuracy in dynamic connected vehicle networks. This mechanism makes the information from one group of connected vehicles interpenetrate
Externí odkaz:
http://arxiv.org/abs/1804.10064
One desirable capability of autonomous cars is to accurately predict the pedestrian motion near intersections for safe and efficient trajectory planning. We are interested in developing transfer learning algorithms that can be trained on the pedestri
Externí odkaz:
http://arxiv.org/abs/1804.00495
We proposed a fusion mechanism for the distributed cooperative map matching (CMM) within the vehicular ad-hoc network. This mechanism makes the information from each node reachable within the network by other nodes without direct communication, thus
Externí odkaz:
http://arxiv.org/abs/1709.05457
Cooperative map matching (CMM) uses the Global Navigation Satellite System (GNSS) position information of a group of vehicles to improve the standalone localization accuracy. It has been shown, in our previous work, that the GNSS error can be reduced
Externí odkaz:
http://arxiv.org/abs/1705.00568