Results 301 to 310 of about 5,619,500 (318)

Kimi k1.5: Scaling Reinforcement Learning with LLMs

arXiv.org
Language model pretraining with next token prediction has proved effective for scaling compute but is limited to the amount of available training data.
Kimi Team, Angang Du, Bofei Gao, Bowei Xing, Changjiu Jiang, Cheng Chen, Cheng Li, Chenjun Xiao, Chenzhuang Du, Chonghua Liao, Chuning Tang, Congcong Wang, Dehao Zhang, Enming Yuan, Enzhe Lu, Feng Tang, Flood Sung, Guangda Wei, Guokun Lai, Haiqing Guo, Han Zhu, Haochen Ding, Hao-Xing Hu, Haoming Yang, Hao Zhang, Haotian Yao, Hao-Dong Zhao, Haoyu Lu, Haoze Li, Haozhen Yu, Hongcheng Gao, Huabin Zheng, Huan Yuan, Jia Chen, Jia-Xing Guo, Jianling Su, Jianzhou Wang, Jie Zhao, Jin Zhang, Jingyuan Liu, Junjie Yan, Junyan Wu, Li-Na Shi, Li-Tao Ye, Long Yu, Meng-Xiao Dong, Neo Y. Zhang, Ningchen Ma, Qi Pan, Qucheng Gong, Shaowei Liu, Shen Ma, Shu-Yan Wei, Sihan Cao, Si-Da Huang, Tao Jiang, Wei-Wei Gao, Weiming Xiong, Weiran He, Weixiao Huang, Wenhao Wu, Wen He, Xian-sen Wei, Xian-Xian Jia, Xingzhe Wu, Xinran Xu, Xinxing Zu, Xinyu Zhou, Xue-biao Pan, Y. Charles, Yang Li, Yan-Ling Hu, Yangyang Liu, Yanru Chen, Ye-Jia Wang, Yibo Liu, Yidao Qin, Yifeng Liu, Yingbo Yang, Yiping Bao, Yulun Du, Yuxin Wu, Yuzhi Wang, Zaida Zhou, Zhaoji Wang, Zhaowei Li, Zhengxin Zhu, Zheng Zhang, Zhexu Wang, Zhilin Yang, Zhiqi Huang, Zihao Huang, Ziya Xu, Zonghan Yang   +93 more
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Reinforcement Learning

IFAC Proceedings Volumes, 1997
Reinforcement learning refers to ways of improving performance through trial-and-error experience. Despite recent progress in developing artificial learning systems, including new learning methods for artificial neural networks, most of these systems learn under the tutelage of a knowledgeable “teacher” able to tell them how to respond to a set of ...
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Reinforcement Learning

2008
Just as there are many different types of supervised and unsupervised learning, so there are many different types of reinforcement learning. Reinforcement learning is appropriate for an AI or agent which is actively exploring its environment and also actively exploring what actions are best to take in different situations.
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey   +3 more
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Reinforcement learning control

Current Opinion in Neurobiology, 1994
Reinforcement learning refers to improving performance through trial-and-error. Despite recent progress in developing artificial learning systems, including new learning methods for artificial neural networks, most of these systems learn under the tutelage of a knowledgeable 'teacher' able to tell them how to respond to a set of training stimuli ...
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Reinforcement and Learning in Schizophrenics

Psychological Reports, 1971
Three groups of 7 chronic schizophrenics were tested on a word-association task under conditions of verbal reinforcement, non-verbal reinforcement, or no reinforcement. Both verbal and non-verbal reinforcement were significantly more effective than no reinforcement in conditioning patients to the task but equally effective.
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Grandmaster level in StarCraft II using multi-agent reinforcement learning

Nature, 2019
O. Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, A. Dudzik, Junyoung Chung, David Choi, Richard Powell, T. Ewalds, Petko Georgiev, Junhyuk Oh, Dan Horgan, M. Kroiss, Ivo Danihelka, Aja Huang, L. Sifre, Trevor Cai, J. Agapiou, Max Jaderberg, A. Vezhnevets, Rémi Leblond, Tobias Pohlen, Valentin Dalibard, D. Budden, Yury Sulsky, James Molloy, T. Paine, Caglar Gulcehre, Ziyun Wang, T. Pfaff, Yuhuai Wu, Roman Ring, Dani Yogatama, Dario Wünsch, Katrina McKinney, Oliver Smith, T. Schaul, T. Lillicrap, K. Kavukcuoglu, D. Hassabis, C. Apps, David Silver   +41 more
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Dense reinforcement learning for safety validation of autonomous vehicles

Nature, 2023
Shuo Feng, Haowei Sun, Xintao Yan, H. Zhu, Zhengxia Zou, Shengyin Shen, Henry X. Liu   +6 more
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Human-level control through deep reinforcement learning

Nature, 2015
Volodymyr Mnih, K. Kavukcuoglu, David Silver, Andrei A. Rusu, J. Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, A. Fidjeland, Georg Ostrovski, Stig Petersen, Charlie Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, D. Kumaran, D. Wierstra, S. Legg, D. Hassabis   +18 more
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Reinforcement Learning: An Introduction

IEEE Trans. Neural Networks, 1998
R. S. Sutton, A. Barto
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Distributed Reinforcement Learning

Robotics and Autonomous Systems, 1995
In multi-agent systems two forms of learning can be distinguished: centralized learning, that is, learning done by a single agent independent of the other agents; and distributed learning, that is, learning that becomes possible only because several agents are present. Whereas centralized learning has been intensively studied in the field of artificial
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