Zobrazeno 1 - 5
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pro vyhledávání: '"Balloch, Jonathan C"'
Autor:
Balloch, Jonathan C., Bhagat, Rishav, Zollicoffer, Geigh, Jia, Ruoran, Kim, Julia, Riedl, Mark O.
In deep reinforcement learning (RL) research, there has been a concerted effort to design more efficient and productive exploration methods while solving sparse-reward problems. These exploration methods often share common principles (e.g., improving
Externí odkaz:
http://arxiv.org/abs/2404.02235
The exploration--exploitation trade-off in reinforcement learning (RL) is a well-known and much-studied problem that balances greedy action selection with novel experience, and the study of exploration methods is usually only considered in the contex
Externí odkaz:
http://arxiv.org/abs/2210.06168
Open-world novelty occurs when the rules of an environment can change abruptly, such as when a game player encounters "house rules". To address open-world novelty, game playing agents must be able to detect when novelty is injected, and to quickly ad
Externí odkaz:
http://arxiv.org/abs/2106.02204
Robot perception systems need to perform reliable image segmentation in real-time on noisy, raw perception data. State-of-the-art segmentation approaches use large CNN models and carefully constructed datasets; however, these models focus on accuracy
Externí odkaz:
http://arxiv.org/abs/1809.03676
Autor:
Karlsen, Robert E., Gage, Douglas W., Shoemaker, Charles M., Gerhart, Grant R., Endo, Yoichiro, Balloch, Jonathan C., Grushin, Alexander, Lee, Mun Wai, Handelman, David
Publikováno v:
Proceedings of SPIE; May 2016, Vol. 9837 Issue: 1 p98370F-98370F-13, 9738644p
