20,881 research outputs found
Towards Stratification Learning through Homology Inference
A topological approach to stratification learning is developed for point
cloud data drawn from a stratified space. Given such data, our objective is to
infer which points belong to the same strata. First we define a multi-scale
notion of a stratified space, giving a stratification for each radius level. We
then use methods derived from kernel and cokernel persistent homology to
cluster the data points into different strata, and we prove a result which
guarantees the correctness of our clustering, given certain topological
conditions; some geometric intuition for these topological conditions is also
provided. Our correctness result is then given a probabilistic flavor: we give
bounds on the minimum number of sample points required to infer, with
probability, which points belong to the same strata. Finally, we give an
explicit algorithm for the clustering, prove its correctness, and apply it to
some simulated data.Comment: 48 page
Importance mixing: Improving sample reuse in evolutionary policy search methods
Deep neuroevolution, that is evolutionary policy search methods based on deep
neural networks, have recently emerged as a competitor to deep reinforcement
learning algorithms due to their better parallelization capabilities. However,
these methods still suffer from a far worse sample efficiency. In this paper we
investigate whether a mechanism known as "importance mixing" can significantly
improve their sample efficiency. We provide a didactic presentation of
importance mixing and we explain how it can be extended to reuse more samples.
Then, from an empirical comparison based on a simple benchmark, we show that,
though it actually provides better sample efficiency, it is still far from the
sample efficiency of deep reinforcement learning, though it is more stable
On the Power of Manifold Samples in Exploring Configuration Spaces and the Dimensionality of Narrow Passages
We extend our study of Motion Planning via Manifold Samples (MMS), a general
algorithmic framework that combines geometric methods for the exact and
complete analysis of low-dimensional configuration spaces with sampling-based
approaches that are appropriate for higher dimensions. The framework explores
the configuration space by taking samples that are entire low-dimensional
manifolds of the configuration space capturing its connectivity much better
than isolated point samples. The contributions of this paper are as follows:
(i) We present a recursive application of MMS in a six-dimensional
configuration space, enabling the coordination of two polygonal robots
translating and rotating amidst polygonal obstacles. In the adduced experiments
for the more demanding test cases MMS clearly outperforms PRM, with over
20-fold speedup in a coordination-tight setting. (ii) A probabilistic
completeness proof for the most prevalent case, namely MMS with samples that
are affine subspaces. (iii) A closer examination of the test cases reveals that
MMS has, in comparison to standard sampling-based algorithms, a significant
advantage in scenarios containing high-dimensional narrow passages. This
provokes a novel characterization of narrow passages which attempts to capture
their dimensionality, an attribute that had been (to a large extent) unattended
in previous definitions.Comment: 20 page
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