9 citations · 12 across the 4 of their papers we have counts for
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cs.LG2024
Enabling Adaptive Agent Training in Open-Ended Simulators by Targeting Diversity
Robby Costales, Stefanos Nikolaidis
The wider application of end-to-end learning methods to embodied decision-making domains remains bottlenecked by their reliance on a superabundance of training data representative…
cs.LG2022★ 9 cited
ALMA: Hierarchical Learning for Composite Multi-Agent Tasks
Shariq Iqbal, Robby Costales, Fei Sha
Despite significant progress on multi-agent reinforcement learning (MARL) in recent years, coordination in complex domains remains a challenge. Work in MARL often focuses on solvin…
cs.LG2022★ 1 cited
Possibility Before Utility: Learning And Using Hierarchical Affordances
Robby Costales, Shariq Iqbal, Fei Sha
Reinforcement learning algorithms struggle on tasks with complex hierarchical dependency structures. Humans and other intelligent agents do not waste time assessing the utility of…