3,300 research outputs found
Domain Decomposition for Stochastic Optimal Control
This work proposes a method for solving linear stochastic optimal control
(SOC) problems using sum of squares and semidefinite programming. Previous work
had used polynomial optimization to approximate the value function, requiring a
high polynomial degree to capture local phenomena. To improve the scalability
of the method to problems of interest, a domain decomposition scheme is
presented. By using local approximations, lower degree polynomials become
sufficient, and both local and global properties of the value function are
captured. The domain of the problem is split into a non-overlapping partition,
with added constraints ensuring continuity. The Alternating Direction
Method of Multipliers (ADMM) is used to optimize over each domain in parallel
and ensure convergence on the boundaries of the partitions. This results in
improved conditioning of the problem and allows for much larger and more
complex problems to be addressed with improved performance.Comment: 8 pages. Accepted to CDC 201
Nonparametric Infinite Horizon Kullback-Leibler Stochastic Control
We present two nonparametric approaches to Kullback-Leibler (KL) control, or
linearly-solvable Markov decision problem (LMDP) based on Gaussian processes
(GP) and Nystr\"{o}m approximation. Compared to recently developed parametric
methods, the proposed data-driven frameworks feature accurate function
approximation and efficient on-line operations. Theoretically, we derive the
mathematical connection of KL control based on dynamic programming with earlier
work in control theory which relies on information theoretic dualities for the
infinite time horizon case. Algorithmically, we give explicit optimal control
policies in nonparametric forms, and propose on-line update schemes with
budgeted computational costs. Numerical results demonstrate the effectiveness
and usefulness of the proposed frameworks
StocHy: automated verification and synthesis of stochastic processes
StocHy is a software tool for the quantitative analysis of discrete-time
stochastic hybrid systems (SHS). StocHy accepts a high-level description of
stochastic models and constructs an equivalent SHS model. The tool allows to
(i) simulate the SHS evolution over a given time horizon; and to automatically
construct formal abstractions of the SHS. Abstractions are then employed for
(ii) formal verification or (iii) control (policy, strategy) synthesis. StocHy
allows for modular modelling, and has separate simulation, verification and
synthesis engines, which are implemented as independent libraries. This allows
for libraries to be easily used and for extensions to be easily built. The tool
is implemented in C++ and employs manipulations based on vector calculus, the
use of sparse matrices, the symbolic construction of probabilistic kernels, and
multi-threading. Experiments show StocHy's markedly improved performance when
compared to existing abstraction-based approaches: in particular, StocHy beats
state-of-the-art tools in terms of precision (abstraction error) and
computational effort, and finally attains scalability to large-sized models (12
continuous dimensions). StocHy is available at www.gitlab.com/natchi92/StocHy
ToyArchitecture: Unsupervised Learning of Interpretable Models of the World
Research in Artificial Intelligence (AI) has focused mostly on two extremes:
either on small improvements in narrow AI domains, or on universal theoretical
frameworks which are usually uncomputable, incompatible with theories of
biological intelligence, or lack practical implementations. The goal of this
work is to combine the main advantages of the two: to follow a big picture
view, while providing a particular theory and its implementation. In contrast
with purely theoretical approaches, the resulting architecture should be usable
in realistic settings, but also form the core of a framework containing all the
basic mechanisms, into which it should be easier to integrate additional
required functionality.
In this paper, we present a novel, purposely simple, and interpretable
hierarchical architecture which combines multiple different mechanisms into one
system: unsupervised learning of a model of the world, learning the influence
of one's own actions on the world, model-based reinforcement learning,
hierarchical planning and plan execution, and symbolic/sub-symbolic integration
in general. The learned model is stored in the form of hierarchical
representations with the following properties: 1) they are increasingly more
abstract, but can retain details when needed, and 2) they are easy to
manipulate in their local and symbolic-like form, thus also allowing one to
observe the learning process at each level of abstraction. On all levels of the
system, the representation of the data can be interpreted in both a symbolic
and a sub-symbolic manner. This enables the architecture to learn efficiently
using sub-symbolic methods and to employ symbolic inference.Comment: Revision: changed the pdftitl
Abstraction-Based Data-Driven Control
Our world is living a paradigm shift in technology policy, often referred to as the Cyber-Physical Revolution or Industry 4.0.
Nowadays, Cyber-Physical Systems are ubiquitous in modern control engineering, including automobiles, aircraft, building control systems, chemical plants, transportation systems, and so on. The interactions of the physical processes with the machines that control them are becoming increasingly complex, and in a growing number of situations either the model of the system is unavailable, or it is too difficult to describe accurately. Therefore, embedded computers need to "learn" the optimal way to control the systems by the mere observation of data.
What seems the best approach to control these complex systems is often by discretizing the different variables, thus transforming the model into a
combinatorial problem on a finite-state automaton, which is called an abstraction of the real system.
Until now, this approach, often referred to as "abstraction-based control" or "symbolic control", has not been proved useful beyond small academic examples.
In this project I aim to show the potential of this approach by implementing a novel data-driven approach based on a probabilistic interpretation of the discretization error.
I have developed a toolbox (github.com/davidedl-ucl/master-thesis) implementing this kind of control with the aim of integrating it in the Dionysos software github.com/dionysos-dev).
With this software, I succeeded in efficiently solving problems for non-linear control systems such as a path planning for an autonomous vehicle and a cart-pole balancing problem.
The long-term objective of this project is to improve the methods implemented in my current software by employing a variable discretization of the state space and to consider complex specifications such as LTL formulas.Our world is living a paradigm shift in technology policy, often referred to as the Cyber-Physical Revolution or Industry 4.0.
Nowadays, Cyber-Physical Systems are ubiquitous in modern control engineering, including automobiles, aircraft, building control systems, chemical plants, transportation systems, and so on. The interactions of the physical processes with the machines that control them are becoming increasingly complex, and in a growing number of situations either the model of the system is unavailable, or it is too difficult to describe accurately. Therefore, embedded computers need to "learn" the optimal way to control the systems by the mere observation of data.
What seems the best approach to control these complex systems is often by discretizing the different variables, thus transforming the model into a
combinatorial problem on a finite-state automaton, which is called an abstraction of the real system.
Until now, this approach, often referred to as "abstraction-based control" or "symbolic control", has not been proved useful beyond small academic examples.
In this project I aim to show the potential of this approach by implementing a novel data-driven approach based on a probabilistic interpretation of the discretization error.
I have developed a toolbox (github.com/davidedl-ucl/master-thesis) implementing this kind of control with the aim of integrating it in the Dionysos software github.com/dionysos-dev).
With this software, I succeeded in efficiently solving problems for non-linear control systems such as a path planning for an autonomous vehicle and a cart-pole balancing problem.
The long-term objective of this project is to improve the methods implemented in my current software by employing a variable discretization of the state space and to consider complex specifications such as LTL formulas
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