11 citations · 19 across the 4 of their papers we have counts for
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cs.LG2019★ 5 cited
Influence-Based Multi-Agent Exploration
Tonghan Wang, Jianhao Wang, Yi Wu +1
Intrinsically motivated reinforcement learning aims to address the exploration challenge for sparse-reward tasks. However, the study of exploration methods in transition-dependent…
cs.CV2019
Bayesian Relational Memory for Semantic Visual Navigation
Yi Wu, Yuxin Wu, Aviv Tamar +3
We introduce a new memory architecture, Bayesian Relational Memory (BRM), to improve the generalization ability for semantic visual navigation agents in unseen environments, where…
cs.LG2019
Emergent Tool Use From Multi-Agent Autocurricula
Bowen Baker, Ingmar Kanitscheider, Todor Markov +4
Through multi-agent competition, the simple objective of hide-and-seek, and standard reinforcement learning algorithms at scale, we find that agents create a self-supervised autocu…