Learning Sensorimotor Concepts Without Reinforcement
Learning Sensorimotor Concepts Without Reinforcement
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发表时间：
2013-03
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通讯作者：
Yasser F. O. Mohammad;T. Nishida
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作者：
Yasser F. O. Mohammad;T. Nishida
从事终身学习的代理可以受益于通过与环境中的对象持续交互来获得新概念的能力，这是人类普遍存在的一种能力。本文提倡使用感知模式和驱动模式相结合的感觉运动概念。感觉运动概念的相关表示有动力系统中的预测性状态表示、语言中基于无关的概念和强化学习中的技巧。本文提出了一种从主体与其环境中的物体之间不分段的交互中学习广义感觉运动概念的系统，该系统工作在连续的动作和观察空间中，同时不需要增强信号。在一个模拟的e-Puck机器人上进行了概念验证实验，结果表明该方法是可行的。
Agents engaged in lifelong learning can benefit from the ability to acquire new concepts from continuous interaction with objects in their environments which is a ubiquitous ability in humans. This paper advocates the use of sensorimotor concepts that combine perceptual and actuation patterns.Related representations to sensorimotor concepts are Predictive State Representation in dynamical systems, Affordance Based Concepts in language and Skills in reinforcement learning. The paper proposes a system for learning generalized sensorimotor concepts from unsegmented interactions between the agent and the objects in its environment that works in continuous action and observation spaces and in the same time require no reinforcement signals. A proof-of-concept experiment with the proposed system on a simulated e-puck robot is reported to support the applicability of the proposed approach.