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pro vyhledávání: '"Bignell, Dave"'
The performance of embodied agents has been shown to improve by increasing model parameters, dataset size, and compute. This has been demonstrated in domains from robotics to video games, when generative learning objectives on offline datasets (pre-t
Externí odkaz:
http://arxiv.org/abs/2411.04434
Training agents to behave as desired in complex 3D environments from high-dimensional sensory information is challenging. Imitation learning from diverse human behavior provides a scalable approach for training an agent with a sensible behavioral pri
Externí odkaz:
http://arxiv.org/abs/2406.04208
Autor:
Schäfer, Lukas, Jones, Logan, Kanervisto, Anssi, Cao, Yuhan, Rashid, Tabish, Georgescu, Raluca, Bignell, Dave, Sen, Siddhartha, Gavito, Andrea Treviño, Devlin, Sam
Video games have served as useful benchmarks for the decision making community, but going beyond Atari games towards training agents in modern games has been prohibitively expensive for the vast majority of the research community. Recent progress in
Externí odkaz:
http://arxiv.org/abs/2312.02312
Autor:
Pearce, Tim, Rashid, Tabish, Kanervisto, Anssi, Bignell, Dave, Sun, Mingfei, Georgescu, Raluca, Macua, Sergio Valcarcel, Tan, Shan Zheng, Momennejad, Ida, Hofmann, Katja, Devlin, Sam
Publikováno v:
ICLR 2023
Diffusion models have emerged as powerful generative models in the text-to-image domain. This paper studies their application as observation-to-action models for imitating human behaviour in sequential environments. Human behaviour is stochastic and
Externí odkaz:
http://arxiv.org/abs/2301.10677