Multi-modal control is a commonly used design tool for breaking up complex control tasks into sequences of simpler tasks. In this paper, we show that by viewing the control space as a set of such tokenized instructions rather than as real-valued signals, reinforcement learning becomes applicable to continuous-time control systems. In fact, we show how a combination of state-space exploration and multi-modal control converts the original system into a finite state machine, on which Q-learning can be utilized. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Mehta, T. R., & Egerstedt, M. (2005). Learning multi-modal control programs. In Lecture Notes in Computer Science (Vol. 3414, pp. 466–479). Springer Verlag. https://doi.org/10.1007/978-3-540-31954-2_30
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