Eigenoption discovery through the deep successor representation
Marlos C. Machado, Clemens Rosenbaum, et al.
ICLR 2018
Many challenging real-world control problems require adaptation and learning in the presence of uncertainty. Examples of these challenging domains include aircraft adaptive control under uncertain disturbances [1], [2], multiple-vehicle tracking with space-dependent uncertain dynamics [3], [4], robotic-arm control [5], blimp control [6], [7], mobile robot tracking and localization [8], [9], cart-pole systems and unicycle control [10], gait optimization in legged robots [11] and snake robots [12], and any other system whose dynamics are uncertain and for which limited data are available for model learning. Classical model reference adaptive control (MRAC) [13]-[15] and reinforcement learning (RL) methods [16]-[23] have been developed to address these challenges and rely on parametric adaptive elements or control policies whose number of parameters or features are fixed and determined a priori. One example of such an adaptive model are radial basis function networks (RBFNs), with RBF centers pre-allocated based on expected operating domains [24], [25].
Marlos C. Machado, Clemens Rosenbaum, et al.
ICLR 2018
Hongchuan Wei, Pingping Zhu, et al.
IEEE TACON
Miao Liu, Kavinayan Sivakumar, et al.
IROS 2017
Matthew Riemer, Ignacio Cases, et al.
ICLR 2019