53 citations · 186 across the 17 of their papers we have counts for
3 papers · 1 filter
SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition
Hangyu Mao, Chao Wang, Xiaotian Hao +6
The MineRL competition is designed for the development of reinforcement learning and imitation learning algorithms that can efficiently leverage human demonstrations to drastically…
Towards robust and domain agnostic reinforcement learning competitions
William Hebgen Guss, Stephanie Milani, Nicholay Topin +26
Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field. Desp…
Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment
Tianze Zhou, Fubiao Zhang, Kun Shao +10
Extending transfer learning to cooperative multi-agent reinforcement learning (MARL) has recently received much attention. In contrast to the single-agent setting, the coordination…