A core ambition of reinforcement learning (RL) is the creation of agents capable of rapid learning in novel tasks. Meta-RL aims to achieve this by directly learning such agents. Black box methods do so by training off-the-shelf sequence models end-to-end. By contrast, task inference methods explicitly infer a posterior distribution over the unknown task, typically using distinct objectives and sequence models designed to enable task inference. Recent work has shown that task inference methods are not necessary for strong performance. However, it remains unclear whether task inference sequence models are beneficial even when task inference objectives are not. In this paper, we present evidence that task inference sequence models are indeed still beneficial. In particular, we investigate sequence models with permutation invariant aggregation, which exploit the fact that, due to the Markov property, the task posterior does not depend on the order of data. We empirically confirm the advantage of permutation invariant sequence models without the use of task inference objectives. However, we also find, surprisingly, that there are multiple conditions under which permutation variance remains useful. Therefore, we propose SplAgger, which uses both permutation variant and invariant components to achieve the best of both worlds, outperforming all baselines evaluated on continuous control and memory environments. Code is provided at https://github.com/jacooba/hyper.
HAGNN: Hybrid Aggregation for Heterogeneous Graph Neural Networks
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学现代软件技术国家重点实验室）；Research Center for Graph Computing, Zhejiang Lab（浙江实验室图计算研究中心）
RaKUn: Rank-based Keyword extraction via Unsupervised learning and Meta vertex aggregation
Jožef Stefan International Postgraduate School（约瑟夫·斯特凡国际研究生院）；Jožef Stefan Institute（约瑟夫·斯特凡研究所）；Usher Institute, Medical School, University of Edinburgh（爱丁堡大学医学院厄舍研究所）
Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networks
The University of Sydney（悉尼大学）；Stevens Institute of Technology（斯蒂文斯理工学院）；National University of Singapore（新加坡国立大学）；Zhejiang University（浙江大学）；JD Explore Academy（京东探索学院）
GCN for HIN via Implicit Utilization of Attention and Meta-paths
Tianjin University（天津大学）；Emory University（埃默里大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
