Learning Spatial Search and Map Exploration using Adaptive Submodular Inverse Reinforcement Learning

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/85090

Title:	Learning Spatial Search and Map Exploration using Adaptive Submodular Inverse Reinforcement Learning
Authors:	吳季潔;Wu, Ji-Jie
Contributors:	數學系
Keywords:	空間搜尋;地圖探索;自適應次模;逆強化學習;壓縮感測;Spatial search;Map exploration;Adaptive submodularity;Inverse reinforcement learning;Compressed sensing
Date:	2021-01-26
Issue Date:	2021-03-18 17:38:20 (UTC+8)
Publisher:	國立中央大學
Abstract:	找到空間搜尋和地圖探索問題的最佳路徑是NP-hard。由於空間搜尋和環境探索是人類日常活動之一，因此從資料中學習人類行為是解決這些問題的其中一種方法。利用兩個問題的自適應次模性，本研究提出了一種自適應次模逆強化學習（ASIRL）演算法來學習人類行為。ASIRL方法是在傅立葉域中學習獎勵函數，並在空間域上對其進行重建，近似最佳路徑可以透過學習獎勵函數算出。實驗顯示ASIRL演算法的表現優於現有方法（例如REWARDAGG和QVALAGG）。;Finding optimal paths for spatial search and map exploration problems are NP-hard. Since spatial search and environmental exploration are parts of human central activities, learning human behavior from data is a way to solve these problems. Utilizing the adaptive submodularity of two problems, this research proposes an adaptive submodular inverse reinforcement learning (ASIRL) algorithm to learn human behavior. The ASIRL approach is to learn the reward functions in the Fourier domain and then recover it in the spatial domain. The nearoptimal path can be computed through learned reward functions. The experiments demonstrate that the ASIRL outperforms state of the art approaches (e.g., REWARDAGG and QVALAGG).
Appears in Collections:	[Graduate Institute of Mathematics] Electronic Thesis & Dissertation

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