トポロジカル事前分布を用いたグラフ畳み込みメモリ
Graph Convolutional Memory using Topological Priors
部分観測マルコフ決定過程(POMDP)を解くため、人間定義またはデータ駆動のトポロジカル事前分布でグラフ近傍を形成し、グラフ畳み込みで関連記憶を統合するハイブリッド記憶モデルGCMを提案。人間の事前分布を用いると制御・記憶・ナビゲーションタスクで最先端手法を上回り、パラメータ数も大幅に削減できる。
著者: Steven D. Morad, Stephan Liwicki, Ryan Kortvelesy, Roberto Mecca, Amanda Prorok
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
Solving partially-observable Markov decision processes (POMDPs) is critical when applying reinforcement learning to real-world problems, where agents have an incomplete view of the world. We present graph convolutional memory (GCM), the first hybrid memory model for solving POMDPs using reinforcement learning. GCM uses either human-defined or data-driven topological priors to form graph neighborhoods, combining them into a larger network topology using dynamic programming. We query the graph using graph convolution, coalescing relevant memories into a context-dependent belief. When used without human priors, GCM performs similarly to state-of-the-art methods. When used with human priors, GCM outperforms these methods on control, memorization, and navigation tasks while using significantly fewer parameters.
関連論文
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