no code implementations • 16 Jan 2024 • Zhiyuan Li, Wenshuai Zhao, Lijun Wu, Joni Pajarinen
Inspired by the concept of correlated equilibrium, we propose to introduce a \textit{strategy modification} to provide a mechanism for agents to correlate their policies.
1 code implementation • 3 Nov 2023 • Wenshuai Zhao, Yi Zhao, Zhiyuan Li, Juho Kannala, Joni Pajarinen
However, with function approximation optimism can amplify overestimation and thus fail on complex tasks.
1 code implementation • 15 Jun 2023 • Yi Zhao, Wenshuai Zhao, Rinu Boney, Juho Kannala, Joni Pajarinen
This applies when using pure planning with a dynamics model conditioned on the representation, but, also when utilizing the representation as policy and value function features in model-free RL.
no code implementations • 20 May 2022 • Wenshuai Zhao, Zhiyuan Li, Joni Pajarinen
Inspired by the success of CRL in single-agent settings, a few works have attempted to apply CRL to multi-agent reinforcement learning (MARL) using the number of agents to control task difficulty.
Multi-agent Reinforcement Learning Open-Ended Question Answering +3
no code implementations • 24 Sep 2020 • Wenshuai Zhao, Jorge Peña Queralta, Tomi Westerlund
Deep reinforcement learning has recently seen huge success across multiple areas in the robotics domain.
no code implementations • 18 Aug 2020 • Wenshuai Zhao, Jorge Peña Queralta, Li Qingqing, Tomi Westerlund
The integration of edge computing in next-generation mobile networks is bringing low-latency and high-bandwidth ubiquitous connectivity to a myriad of cyber-physical systems.
1 code implementation • 18 Aug 2020 • Wenshuai Zhao, Jorge Peña Queralta, Li Qingqing, Tomi Westerlund
In this work, we are particularly interested in analyzing how multi-agent reinforcement learning can bridge the gap to reality in distributed multi-robot systems where the operation of the different robots is not necessarily homogeneous.
Multi-agent Reinforcement Learning reinforcement-learning +1
1 code implementation • 17 Apr 2020 • Wenshuai Zhao, Dihong Jiang, Jorge Peña Queralta, Tomi Westerlund
We present a multi-scale supervised 3D U-Net, MSS U-Net, to automatically segment kidneys and kidney tumors from CT images.
no code implementations • 9 Aug 2019 • Wenshuai Zhao, Zengfeng Zeng
U-Net has achieved huge success in various medical image segmentation challenges.