1,764 research outputs found
One-Shot Learning of Manipulation Skills with Online Dynamics Adaptation and Neural Network Priors
One of the key challenges in applying reinforcement learning to complex
robotic control tasks is the need to gather large amounts of experience in
order to find an effective policy for the task at hand. Model-based
reinforcement learning can achieve good sample efficiency, but requires the
ability to learn a model of the dynamics that is good enough to learn an
effective policy. In this work, we develop a model-based reinforcement learning
algorithm that combines prior knowledge from previous tasks with online
adaptation of the dynamics model. These two ingredients enable highly
sample-efficient learning even in regimes where estimating the true dynamics is
very difficult, since the online model adaptation allows the method to locally
compensate for unmodeled variation in the dynamics. We encode the prior
experience into a neural network dynamics model, adapt it online by
progressively refitting a local linear model of the dynamics, and use model
predictive control to plan under these dynamics. Our experimental results show
that this approach can be used to solve a variety of complex robotic
manipulation tasks in just a single attempt, using prior data from other
manipulation behaviors
Standard Young Tableaux and Colored Motzkin Paths
In this paper, we propose a notion of colored Motzkin paths and establish a
bijection between the -cell standard Young tableaux (SYT) of bounded height
and the colored Motzkin paths of length . This result not only gives a
lattice path interpretation of the standard Young tableaux but also reveals an
unexpected intrinsic relation between the set of SYTs with at most rows
and the set of SYTs with at most 2d rows.Comment: 21 page
Flat Topological Bands and Eigenstate Criticality in a Quasiperiodic Insulator
The effects of downfolding a Brillouin zone can open gaps and quench the
kinetic energy by flattening bands. Quasiperiodic systems are extreme examples
of this process, which leads to new phases and critical eigenstates. We
analytically and numerically investigate these effects in a two dimensional
topological insulator with a quasiperiodic potential and discover a complex
phase diagram. We study the nature of the resulting eigenstate quantum phase
transitions; a quasiperiodic potential can make a trivial insulator topological
and induce topological insulator-to-metal phase transitions through a unique
universality class distinct from random systems. This wealth of critical
behavior occurs concomitantly with the quenching of the kinetic energy,
resulting in flat topological bands that could serve as a platform to realize
the fractional quantum Hall effect without a magnetic field.Comment: 6 pages, 4 figures, and supplement materials. Updated results on the
flatness ratio, the Berry curvature, and the Chern number of individual band
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