96 citations · 129 across the 8 of their papers we have counts for
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cs.LG2021
Model-Free Risk-Sensitive Reinforcement Learning
Grégoire Delétang, Jordi Grau-Moya, Markus Kunesch +4
We extend temporal-difference (TD) learning in order to obtain risk-sensitive, model-free reinforcement learning algorithms. This extension can be regarded as modification of the R…
cs.LG2021★ 7 cited
Shaking the foundations: delusions in sequence models for interaction and control
Pedro A. Ortega, Markus Kunesch, Grégoire Delétang +16
The recent phenomenal success of language models has reinvigorated machine learning research, and large sequence models such as transformers are being applied to a variety of domai…