22,696 research outputs found
Parameterized Approximation Schemes using Graph Widths
Combining the techniques of approximation algorithms and parameterized
complexity has long been considered a promising research area, but relatively
few results are currently known. In this paper we study the parameterized
approximability of a number of problems which are known to be hard to solve
exactly when parameterized by treewidth or clique-width. Our main contribution
is to present a natural randomized rounding technique that extends well-known
ideas and can be used for both of these widths. Applying this very generic
technique we obtain approximation schemes for a number of problems, evading
both polynomial-time inapproximability and parameterized intractability bounds
Learning Parameterized Skills
We introduce a method for constructing skills capable of solving tasks drawn
from a distribution of parameterized reinforcement learning problems. The
method draws example tasks from a distribution of interest and uses the
corresponding learned policies to estimate the topology of the
lower-dimensional piecewise-smooth manifold on which the skill policies lie.
This manifold models how policy parameters change as task parameters vary. The
method identifies the number of charts that compose the manifold and then
applies non-linear regression in each chart to construct a parameterized skill
by predicting policy parameters from task parameters. We evaluate our method on
an underactuated simulated robotic arm tasked with learning to accurately throw
darts at a parameterized target location.Comment: Appears in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012
Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing
Within the context of autonomous driving a model-based reinforcement learning
algorithm is proposed for the design of neural network-parameterized
controllers. Classical model-based control methods, which include sampling- and
lattice-based algorithms and model predictive control, suffer from the
trade-off between model complexity and computational burden required for the
online solution of expensive optimization or search problems at every short
sampling time. To circumvent this trade-off, a 2-step procedure is motivated:
first learning of a controller during offline training based on an arbitrarily
complicated mathematical system model, before online fast feedforward
evaluation of the trained controller. The contribution of this paper is the
proposition of a simple gradient-free and model-based algorithm for deep
reinforcement learning using task separation with hill climbing (TSHC). In
particular, (i) simultaneous training on separate deterministic tasks with the
purpose of encoding many motion primitives in a neural network, and (ii) the
employment of maximally sparse rewards in combination with virtual velocity
constraints (VVCs) in setpoint proximity are advocated.Comment: 10 pages, 6 figures, 1 tabl
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