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检索条件"任意字段=2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, ADPRL 2009"
232 条 记 录,以下是81-90 订阅
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adaptive optimal control for nonlinear discrete-time systems
Adaptive optimal control for nonlinear discrete-time systems
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ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)
作者: Chunbin Qin Huaguang Zhang Yanhong Luo School of Information Science and Engineering Northeastern University Shenyang China Basic Experiment Teaching Center Henan University Kaifeng China
This paper proposes an on-line near-optimal control scheme based on capabilities of neural networks (NNs), in function approximation, to attain the on-line solution of optimal control problem for nonlinear discrete-ti... 详细信息
来源: 评论
Real-time tracking on adaptive critic design with uniformly ultimately bounded condition
Real-time tracking on adaptive critic design with uniformly ...
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ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)
作者: Zhen Ni Xiao Fang Haibo He Dongbin Zhao Xin Xu Department of Electrical University of Rhode Island Kingston RI USA Institute of Automation Chinese Academy of Sciences Beijing China Institute of Automation National University of Defense Technology Changsha China
In this paper, we proposed a new nonlinear tracking controller based on heuristic dynamic programming (HDP) with the tracking filter. Specifically, we integrate a goal network into the regular HDP design and provide t... 详细信息
来源: 评论
Scalarized multi-objective reinforcement learning: Novel design techniques
Scalarized multi-objective reinforcement learning: Novel des...
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ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)
作者: Kristof Van Moffaert Madalina M. Drugan Ann Nowé Department of Computer Science Vrije Universiteit Brussel Brussels Belgium
In multi-objective problems, it is key to find compromising solutions that balance different objectives. The linear scalarization function is often utilized to translate the multi-objective nature of a problem into a ... 详细信息
来源: 评论
Optimistic planning for continuous-action deterministic systems
Optimistic planning for continuous-action deterministic syst...
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ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)
作者: Lucian Buşoniu Alexander Daniels Rémi Munos Robert Babuška Department of Automation Technical University of Cluj-Napoca Romania France DCSC Delft University of Technology the Netherlands Team SequeL INRIA Lille-Nord Europe France
We consider the class of online planning algorithms for optimal control, which compared to dynamic programming are relatively unaffected by large state dimensionality. We introduce a novel planning algorithm called SO... 详细信息
来源: 评论
A combined hierarchical reinforcement learning based approach for multi-robot cooperative target searching in complex unknown environments
A combined hierarchical reinforcement learning based approac...
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ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)
作者: Yifan Cai Simon X. Yang Xin Xu The School of Engineering University of Guelph Guelph Ontario Canada The College of Mechatronics and Automation National University of Defense Technology Changsha Hunan Province China
Effective cooperation of multi-robots in unknown environments is essential in many robotic applications, such as environment exploration and target searching. In this paper, a combined hierarchical reinforcement learn... 详细信息
来源: 评论
reinforcement learning in the game of Othello: learning against a fixed opponent and learning from self-play
Reinforcement learning in the game of Othello: Learning agai...
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ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)
作者: Michiel van der Ree Marco Wiering Faculty of Mathematics and Natural Sciences University of Groningen Institute of Artificial Intelligence and Cognitive Engineering The Netherlands
This paper compares three strategies in using reinforcement learning algorithms to let an artificial agent learn to play the game of Othello. The three strategies that are compared are: learning by self-play, learning... 详细信息
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Delayed insertion and rule effect moderation of domain knowledge for reinforcement learning
Delayed insertion and rule effect moderation of domain knowl...
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ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)
作者: Teck-Hou Teng Ah-Hwee Tan School of Computer Engineering Center for Computational Intelligence School of Computer Engineering Nanyang Technological University
Though not a fundamental pre-requisite to efficient machine learning, insertion of domain knowledge into adaptive virtual agent is nonetheless known to improve learning efficiency and reduce model complexity. Conventi... 详细信息
来源: 评论
A reinforcement learning algorithm developed to model GenCo strategic bidding behavior in multidimensional and continuous state and action spaces
A reinforcement learning algorithm developed to model GenCo ...
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ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)
作者: Alfred Yong Fu Lau Dipti Srinivasan Thomas Reindl National University of Singapore Singapore SG Department of Electrical Computer Engineering National University of Singapore Singapore Solar Energy Research Institute of Singapore National University of Singapore Singapore
The electricity market has provided a complex economic environment, and consequently has increased the requirement for advancement of learning methods. In the agent-based modeling and simulation framework of this econ... 详细信息
来源: 评论
reinforcement learning to train Ms. Pac-Man using higher-order action-relative inputs
Reinforcement learning to train Ms. Pac-Man using higher-ord...
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ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)
作者: Luuk Bom Ruud Henken Marco Wiering Faculty of Mathematics and Natural Sciences University of Groningen The Netherlands
reinforcement learning algorithms enable an agent to optimize its behavior from interacting with a specific environment. Although some very successful applications of reinforcement learning algorithms have been develo... 详细信息
来源: 评论
Analyzing collective behavior in evolutionary swarm robotic systems based on an ethological approach
Analyzing collective behavior in evolutionary swarm robotic ...
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ieee symposium on adaptive dynamic programming and reinforcement learning, (adprl)
作者: Toshiyuki Yasuda Nanami Wada Kazuhiro Ohkura Yoshiyuki Matsumura Graduate School of Engineering Hiroshima University Higashi-Hiroshima JAPAN Faculty of Textile Science and Technology Shinshu University Ueda Nagano JAPAN
Swarm robotic systems are a type of multi-robot systems which generally consist of many homogeneous autonomous robots without any type of global controllers. Swarm robotics aims at designing desired collective behavio... 详细信息
来源: 评论