39 citations · 59 across the 8 of their papers we have counts for
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cs.LG2022★ 11 cited
Transformers are Sample-Efficient World Models
Vincent Micheli, Eloi Alonso, François Fleuret
Deep reinforcement learning agents are notoriously sample inefficient, which considerably limits their application to real-world problems. Recently, many model-based methods have b…
cs.LG2022★ 6 cited
MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned
Anssi Kanervisto, Stephanie Milani, Karolis Ramanauskas +19
Reinforcement learning competitions advance the field by providing appropriate scope and support to develop solutions toward a specific problem. To promote the development of more…