Zobrazeno 1 - 10
of 73
pro vyhledávání: '"Zhao Wenshuai"'
Imitation learning from human motion capture (MoCap) data provides a promising way to train humanoid robots. However, due to differences in morphology, such as varying degrees of joint freedom and force limits, exact replication of human behaviors ma
Externí odkaz:
http://arxiv.org/abs/2410.01968
A fundamental challenge in multi-agent reinforcement learning (MARL) is to learn the joint policy in an extremely large search space, which grows exponentially with the number of agents. Moreover, fully decentralized policy factorization significantl
Externí odkaz:
http://arxiv.org/abs/2401.12574
*Relative overgeneralization* (RO) occurs in cooperative multi-agent learning tasks when agents converge towards a suboptimal joint policy due to overfitting to suboptimal behavior of other agents. No methods have been proposed for addressing RO in m
Externí odkaz:
http://arxiv.org/abs/2311.01953
In many multi-agent and high-dimensional robotic tasks, the controller can be designed in either a centralized or decentralized way. Correspondingly, it is possible to use either single-agent reinforcement learning (SARL) or multi-agent reinforcement
Externí odkaz:
http://arxiv.org/abs/2309.14792
Reinforcement learning is able to solve complex sequential decision-making tasks but is currently limited by sample efficiency and required computation. To improve sample efficiency, recent work focuses on model-based RL which interleaves model learn
Externí odkaz:
http://arxiv.org/abs/2306.09466
Curriculum reinforcement learning (CRL) aims to speed up learning by gradually increasing the difficulty of a task, usually quantified by the achievable expected return. Inspired by the success of CRL in single-agent settings, a few works have attemp
Externí odkaz:
http://arxiv.org/abs/2205.10016
Publikováno v:
2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020, pp. 737-744
Deep reinforcement learning has recently seen huge success across multiple areas in the robotics domain. Owing to the limitations of gathering real-world data, i.e., sample inefficiency and the cost of collecting it, simulation environments are utili
Externí odkaz:
http://arxiv.org/abs/2009.13303
Current research directions in deep reinforcement learning include bridging the simulation-reality gap, improving sample efficiency of experiences in distributed multi-agent reinforcement learning, together with the development of robust methods agai
Externí odkaz:
http://arxiv.org/abs/2008.07875
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. This will further boost the increasing intelligence that is being embedded
Externí odkaz:
http://arxiv.org/abs/2008.07863
Accurate segmentation of kidneys and kidney tumors is an essential step for radiomic analysis as well as developing advanced surgical planning techniques. In clinical analysis, the segmentation is currently performed by clinicians from the visual ins
Externí odkaz:
http://arxiv.org/abs/2004.08108