Reinforcement-Learning Connectionist Systems (Computer and Information Science)

类似神经元的处理单元网络，被称为连接系统，具有有趣的计算特性，这使得它们在心理建模和人工智能的潜在应用方面都很有吸引力。最近的研究已经为这种网络的学习带来了一些很有前途的算法。该项目通过开发一种数学上有充分依据的方法来设计算法，以解决随机单元连接网络中强化学习的特定问题，从而推进了此类研究。针对具有重要时间成分的学习问题，例如具有未知持续时间的反馈延迟的控制问题或涉及识别或产生时变信号的问题，开发了特定算法。这些算法需要允许一个合适的在线实现，其中学习发生在操作系统内。除了在这些算法的理论方面取得进展外，该项目还涉及实现更有前途的候选算法并在模拟实验中评估其性能。评估的主要标准是学习效率和收敛到次优状态的怀疑性。此外，本项目通过研究这些问题的适当缩小版本，探索这些算法对人工智能和机器人技术中特定问题的适用性。这些问题包括语音识别和自适应感觉运动控制。

Networks of neuron-like processing units, called connectionist systems, have interesting computational properties making them attractive for both psychological modeling and potential application in artificial intelligence. Recent research has led to some promising algorithms for learning in such networks. This project advances such research by developing a mathematically well-founded approach to the design of algorithms for the particular problem of reinforcement learning in connectionist networks of stochastic units. Particular algorithms are developed for learning problems having an important temporal component, such as control problems with feedback delays of unknown duration or problems involving recognition or production of time-varying signals. These algorithms are required to admit a suitable on-line implementation, in which the learning occurs within the operating system. In addition to making advances in the theory of such algorithms, this project involves implementing the more promising candidates and evaluating their performance in simulation experiments. Primary criteria for evaluation are learning efficiency and suspectibility to convergence to suboptimal states. In addition, this project explores the applicability of such algorithms to specific problems in artificial intelligence and robotics through the study of suitably scaled-down versions of these problems. Such problems include speech recognition and adaptive sensorimotor control.