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pro vyhledávání: '"BURGESS, NEIL"'
Exploration is essential in reinforcement learning, particularly in environments where external rewards are sparse. Here we focus on exploration with intrinsic rewards, where the agent transiently augments the external rewards with self-generated int
Externí odkaz:
http://arxiv.org/abs/2305.15277
Autor:
Micikevicius, Paulius, Stosic, Dusan, Burgess, Neil, Cornea, Marius, Dubey, Pradeep, Grisenthwaite, Richard, Ha, Sangwon, Heinecke, Alexander, Judd, Patrick, Kamalu, John, Mellempudi, Naveen, Oberman, Stuart, Shoeybi, Mohammad, Siu, Michael, Wu, Hao
FP8 is a natural progression for accelerating deep learning training inference beyond the 16-bit formats common in modern processors. In this paper we propose an 8-bit floating point (FP8) binary interchange format consisting of two encodings - E4M3
Externí odkaz:
http://arxiv.org/abs/2209.05433
A key goal of unsupervised learning is to go beyond density estimation and sample generation to reveal the structure inherent within observed data. Such structure can be expressed in the pattern of interactions between explanatory latent variables ca
Externí odkaz:
http://arxiv.org/abs/2209.05212
Efficient reinforcement learning (RL) involves a trade-off between "exploitative" actions that maximise expected reward and "explorative'" ones that sample unvisited states. To encourage exploration, recent approaches proposed adding stochasticity to
Externí odkaz:
http://arxiv.org/abs/2205.15064
We propose learning via retracing, a novel self-supervised approach for learning the state representation (and the associated dynamics model) for reinforcement learning tasks. In addition to the predictive (reconstruction) supervision in the forward
Externí odkaz:
http://arxiv.org/abs/2111.12600
Autor:
Chaplin-Kramer, Rebecca, Polasky, Stephen, Alkemade, Rob, Burgess, Neil D., Cheung, William W.L., Fetzer, Ingo, Harfoot, Mike, Hertel, Thomas W., Hill, Samantha L.L., Andrew Johnson, Justin, Janse, Jan H., José v. Jeetze, Patrick, Kim, HyeJin, Kuiper, Jan J., Lonsdorf, Eric, Leclère, David, Mulligan, Mark, Peterson, Garry D., Popp, Alexander, Roe, Stephanie, Schipper, Aafke M., Snäll, Tord, van Soesbergen, Arnout, Soterroni, Aline C., Stehfest, Elke, van Vuuren, Detlef P., Visconti, Piero, Wang-Erlandsson, Lan, Wells, Geoff, Pereira, Henrique M.
Publikováno v:
In Global Environmental Change September 2024 88
Autor:
Isaac, Maxim Conrad1 (AUTHOR) maxim.conrad.isaac@gmail.com, Burgess, Neil D.1,2 (AUTHOR), Tallowin, Oliver J. S.3 (AUTHOR), Pavitt, Alyson T.2 (AUTHOR), Kadigi, Reuben M. J.4 (AUTHOR), Ract, Claire1 (AUTHOR)
Publikováno v:
PLoS ONE. 5/16/2024, Vol. 19 Issue 5, p1-14. 14p.
Knowing how the effects of directed actions generalise to new situations (e.g. moving North, South, East and West, or turning left, right, etc.) is key to rapid generalisation across new situations. Markovian tasks can be characterised by a state spa
Externí odkaz:
http://arxiv.org/abs/2006.03355
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