Modeling the System Acquisition Using Deep Reinforcement Learning
نویسندگان
چکیده
منابع مشابه
Geometric Concept Acquisition by Deep Reinforcement Learning
Explaining how intelligent systems come to embody knowledge of deductive concepts through inductive learning is a fundamental challenge of both cognitive science and AI. This project address this challenge by exploring how deep reinforcement learning agents, occupying settings similar to early-stage mathematical concept learners come to represent ideas such as rotation and translation. I first ...
متن کاملOpponent Modeling in Deep Reinforcement Learning
Opponent modeling is necessary in multi-agent settings where secondary agents with competing goals also adapt their strategies, yet it remains challenging because strategies interact with each other and change. Most previous work focuses on developing probabilistic models or parameterized strategies for specific applications. Inspired by the recent success of deep reinforcement learning, we pre...
متن کاملAutonomous Quadrotor Landing using Deep Reinforcement Learning
Landing an unmanned aerial vehicle (UAV) on a ground marker is an open problem despite the effort of the research community. Previous attempts mostly focused on the analysis of hand-crafted geometric features and the use of external sensors in order to allow the vehicle to approach the land-pad. In this article, we propose a method based on deep reinforcement learning that only requires low-res...
متن کاملAutomatic Bridge Bidding Using Deep Reinforcement Learning
Bridge is among the zero-sum games for which artificial intelligence has not yet outperformed expert human players. The main difficulty lies in the bidding phase of bridge, which requires cooperative decision making under partial information. Existing artificial intelligence systems for bridge bidding rely on and are thus restricted by human-designed bidding systems or features. In this work, w...
متن کاملEfficient Deep Web Crawling Using Reinforcement Learning
Deep web refers to the hidden part of the Web that remains unavailable for standard Web crawlers. To obtain content of Deep Web is challenging and has been acknowledged as a significant gap in the coverage of search engines. To this end, the paper proposes a novel deep web crawling framework based on reinforcement learning, in which the crawler is regarded as an agent and deep web database as t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.3008083