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Reinforcement learning with via-point representation
2004
Neural Networks
In this paper, we propose a new learning framework for motor control. This framework consists of two components: reinforcement learning and via-point representation. In the field of motor control, conventional reinforcement learning has been used to acquire control sequences such as cart-pole or stand-up robot control. Recently, researchers have become interested in hierarchical architecture, such as multiple levels, and multiple temporal and spatial scales. Our new framework contains two
doi:10.1016/j.neunet.2003.11.004
pmid:15037348
fatcat:ax2o2aupuvcqtg53g62qkzvnwu