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摘要:
Reinforcement Learning is a commonly used technique for learning tasks in robotics, however, traditional algorithms are unable to handle large amounts of data coming from the robot’s sensors, require long training times, and use dis-crete actions. This work introduces TS-RRLCA, a two stage method to tackle these problems. In the first stage, low-level data coming from the robot’s sensors is transformed into a more natural, relational representation based on rooms, walls, corners, doors and obstacles, significantly reducing the state space. We use this representation along with Behavioural Cloning, i.e., traces provided by the user;to learn, in few iterations, a relational control policy with discrete actions which can be re-used in different environments. In the second stage, we use Locally Weighted Regression to transform the initial policy into a continuous actions policy. We tested our approach in simulation and with a real service robot on different environments for different navigation and following tasks. Results show how the policies can be used on different domains and perform smoother, faster and shorter paths than the original discrete actions policies.
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篇名 Relational Reinforcement Learning with Continuous Actions by Combining Behavioural Cloning and Locally Weighted Regression
来源期刊 智能学习系统与应用(英文) 学科 医学
关键词 RELATIONAL Reinforcement Learning BEHAVIOURAL CLONING CONTINUOUS ACTIONS ROBOTICS
年,卷(期) 2010,(2) 所属期刊栏目
研究方向 页码范围 69-79
页数 11页 分类号 R73
字数 语种
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RELATIONAL
Reinforcement
Learning
BEHAVIOURAL
CLONING
CONTINUOUS
ACTIONS
ROBOTICS
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相关学者/机构
期刊影响力
智能学习系统与应用(英文)
季刊
2150-8402
武汉市江夏区汤逊湖北路38号光谷总部空间
出版文献量(篇)
166
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0
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