4 research outputs found
CNN Encoder to Reduce the Dimensionality of Data Image for Motion Planning
Many real-world applications need path planning algorithms to solve tasks in
different areas, such as social applications, autonomous cars, and tracking
activities. And most importantly motion planning. Although the use of path
planning is sufficient in most motion planning scenarios, they represent
potential bottlenecks in large environments with dynamic changes. To tackle
this problem, the number of possible routes could be reduced to make it easier
for path planning algorithms to find the shortest path with less efforts. An
traditional algorithm for path planning is the A*, it uses an heuristic to work
faster than other solutions. In this work, we propose a CNN encoder capable of
eliminating useless routes for motion planning problems, then we combine the
proposed neural network output with A*. To measure the efficiency of our
solution, we propose a database with different scenarios of motion planning
problems. The evaluated metric is the number of the iterations to find the
shortest path. The A* was compared with the CNN Encoder (proposal) with A*. In
all evaluated scenarios, our solution reduced the number of iterations by more
than 60\%
Performance Improvement of Path Planning algorithms with Deep Learning Encoder Model
Currently, path planning algorithms are used in many daily tasks. They are
relevant to find the best route in traffic and make autonomous robots able to
navigate. The use of path planning presents some issues in large and dynamic
environments. Large environments make these algorithms spend much time finding
the shortest path. On the other hand, dynamic environments request a new
execution of the algorithm each time a change occurs in the environment, and it
increases the execution time. The dimensionality reduction appears as a
solution to this problem, which in this context means removing useless paths
present in those environments. Most of the algorithms that reduce
dimensionality are limited to the linear correlation of the input data.
Recently, a Convolutional Neural Network (CNN) Encoder was used to overcome
this situation since it can use both linear and non-linear information to data
reduction. This paper analyzes in-depth the performance to eliminate the
useless paths using this CNN Encoder model. To measure the mentioned model
efficiency, we combined it with different path planning algorithms. Next, the
final algorithms (combined and not combined) are checked in a database that is
composed of five scenarios. Each scenario contains fixed and dynamic obstacles.
Their proposed model, the CNN Encoder, associated to other existent path
planning algorithms in the literature, was able to obtain a time decrease to
find the shortest path in comparison to all path planning algorithms analyzed.
the average decreased time was 54.43 %
Provably efficient reconstruction of policy networks
Recent research has shown that learning poli-cies parametrized by large
neural networks can achieve significant success on challenging reinforcement
learning problems. However, when memory is limited, it is not always possible
to store such models exactly for inference, and com-pressing the policy into a
compact representation might be necessary. We propose a general framework for
policy representation, which reduces this problem to finding a low-dimensional
embedding of a given density function in a separable inner product space. Our
framework allows us to de-rive strong theoretical guarantees, controlling the
error of the reconstructed policies. Such guaran-tees are typically lacking in
black-box models, but are very desirable in risk-sensitive tasks. Our
experimental results suggest that the reconstructed policies can use less than
10%of the number of parameters in the original networks, while incurring almost
no decrease in rewards
Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow
We revisit the inductive matrix completion problem that aims to recover a
rank- matrix with ambient dimension given features as the side prior
information. The goal is to make use of the known features to reduce sample
and computational complexities. We present and analyze a new gradient-based
non-convex optimization algorithm that converges to the true underlying matrix
at a linear rate with sample complexity only linearly depending on and
logarithmically depending on . To the best of our knowledge, all previous
algorithms either have a quadratic dependency on the number of features in
sample complexity or a sub-linear computational convergence rate. In addition,
we provide experiments on both synthetic and real world data to demonstrate the
effectiveness of our proposed algorithm.Comment: 35 pages, 3 figures and 2 table