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pro vyhledávání: '"Han, Dongge"'
Large language models (LLMs) have shown significant potential for robotics applications, particularly task planning, by harnessing their language comprehension and text generation capabilities. However, in applications such as household robotics, a c
Externí odkaz:
http://arxiv.org/abs/2404.14285
Deep reinforcement learning (RL) has recently shown great promise in robotic continuous control tasks. Nevertheless, prior research in this vein center around the centralized learning setting that largely relies on the communication availability amon
Externí odkaz:
http://arxiv.org/abs/2112.13937
Autor:
Han, Dongge, Tschiatschek, Sebastian
Abstraction plays an important role in the generalisation of knowledge and skills and is key to sample efficient learning. In this work, we study joint temporal and state abstraction in reinforcement learning, where temporally-extended actions in the
Externí odkaz:
http://arxiv.org/abs/2110.09196
Submodular functions have been a powerful mathematical model for a wide range of real-world applications. Recently, submodular functions are becoming increasingly important in machine learning (ML) for modelling notions such as information and redund
Externí odkaz:
http://arxiv.org/abs/2006.14583
In a multi-agent system, an agent's optimal policy will typically depend on the policies chosen by others. Therefore, a key issue in multi-agent systems research is that of predicting the behaviours of others, and responding promptly to changes in su
Externí odkaz:
http://arxiv.org/abs/1910.09508
Publikováno v:
In Information and Computation February 2021 276
Publikováno v:
Advances in Future Computer & Control Systems; 2012, p443-449, 7p