Discrete fully probabilistic design: a tool to design control policies
from examples
- URL: http://arxiv.org/abs/2112.11210v1
- Date: Tue, 21 Dec 2021 13:44:48 GMT
- Title: Discrete fully probabilistic design: a tool to design control policies
from examples
- Authors: Enrico Ferrentino, Pasquale Chiacchio, Giovanni Russo
- Abstract summary: We present a discretized design that expounds an algorithm recently introduced in Gagliardi and Russo (2021) to synthesize control policies.
The constraints do not need to be fulfilled in the possibly noisy example data, which in turn might be collected from a system that is different from the one under control.
- Score: 2.6749261270690425
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present a discretized design that expounds an algorithm recently
introduced in Gagliardi and Russo (2021) to synthesize control policies from
examples for constrained, possibly stochastic and nonlinear, systems. The
constraints do not need to be fulfilled in the possibly noisy example data,
which in turn might be collected from a system that is different from the one
under control. For this discretized design, we discuss a number of properties
and give a design pipeline. The design, which we term as discrete fully
probabilistic design, is benchmarked numerically on an example that involves
controlling an inverted pendulum with actuation constraints starting from data
collected from a physically different pendulum that does not satisfy the
system-specific actuation constraints.
Related papers
- Refined Risk Bounds for Unbounded Losses via Transductive Priors [58.967816314671296]
We revisit the sequential variants of linear regression with the squared loss, classification problems with hinge loss, and logistic regression.
Our key tools are based on the exponential weights algorithm with carefully chosen transductive priors.
arXiv Detail & Related papers (2024-10-29T00:01:04Z) - Plug-and-Play Controllable Generation for Discrete Masked Models [27.416952690340903]
This article makes discrete masked models for the generative modeling of discrete data controllable.
We propose a novel plug-and-play framework based on importance sampling that bypasses the need for training a conditional score.
Our framework is agnostic to the choice of control criteria, requires no gradient information, and is well-suited for tasks such as posterior sampling, Bayesian inverse problems, and constrained generation.
arXiv Detail & Related papers (2024-10-03T02:00:40Z) - You-Only-Randomize-Once: Shaping Statistical Properties in Constraint-based PCG [3.581471126368696]
We introduce You-Only-Randomize-Once (YORO) pre-rolling, a method for crafting a decision variable ordering for a constraint solver.
We show that this technique effectively controls the statistics of tile-grid outputs generated by several off-the-shelf SAT solvers.
arXiv Detail & Related papers (2024-09-01T20:43:55Z) - Denoising Diffusion-Based Control of Nonlinear Systems [3.4530027457862]
We propose a novel approach based on Denoising Diffusion Probabilistic Models (DDPMs) to control nonlinear dynamical systems.
DDPMs are the state-of-art of generative models that have achieved success in a wide variety of sampling tasks.
We numerically study our approach on various nonlinear systems and verify our theoretical results.
arXiv Detail & Related papers (2024-02-03T23:19:26Z) - Dimensionless Policies based on the Buckingham $\pi$ Theorem: Is This a
Good Way to Generalize Numerical Results? [66.52698983694613]
This article explores the use of the Buckingham $pi$ theorem as a tool to encode the control policies of physical systems into a generic form of knowledge.
We show, by restating the solution to a motion control problem using dimensionless variables, that (1) the policy mapping involves a reduced number of parameters and (2) control policies generated numerically for a specific system can be transferred exactly to a subset of dimensionally similar systems by scaling the input and output variables appropriately.
It remains to be seen how practical this approach can be to generalize policies for more complex high-dimensional problems, but the early results show that it is a
arXiv Detail & Related papers (2023-07-29T00:51:26Z) - Robust Control for Dynamical Systems With Non-Gaussian Noise via Formal
Abstractions [59.605246463200736]
We present a novel controller synthesis method that does not rely on any explicit representation of the noise distributions.
First, we abstract the continuous control system into a finite-state model that captures noise by probabilistic transitions between discrete states.
We use state-of-the-art verification techniques to provide guarantees on the interval Markov decision process and compute a controller for which these guarantees carry over to the original control system.
arXiv Detail & Related papers (2023-01-04T10:40:30Z) - Approximating Constraint Manifolds Using Generative Models for
Sampling-Based Constrained Motion Planning [8.924344714683814]
This paper presents a learning-based sampling strategy for constrained motion planning problems.
We use Conditional Variversaational Autoencoder (CVAE) and Conditional Generative Adrial Net (CGAN) to generate constraint-satisfying sample configurations.
We evaluate the efficiency of these two generative models in terms of their sampling accuracy and coverage of sampling distribution.
arXiv Detail & Related papers (2022-04-14T07:08:30Z) - Sampling-Based Robust Control of Autonomous Systems with Non-Gaussian
Noise [59.47042225257565]
We present a novel planning method that does not rely on any explicit representation of the noise distributions.
First, we abstract the continuous system into a discrete-state model that captures noise by probabilistic transitions between states.
We capture these bounds in the transition probability intervals of a so-called interval Markov decision process (iMDP)
arXiv Detail & Related papers (2021-10-25T06:18:55Z) - Sparsity in Partially Controllable Linear Systems [56.142264865866636]
We study partially controllable linear dynamical systems specified by an underlying sparsity pattern.
Our results characterize those state variables which are irrelevant for optimal control.
arXiv Detail & Related papers (2021-10-12T16:41:47Z) - An Integer Linear Programming Framework for Mining Constraints from Data [81.60135973848125]
We present a general framework for mining constraints from data.
In particular, we consider the inference in structured output prediction as an integer linear programming (ILP) problem.
We show that our approach can learn to solve 9x9 Sudoku puzzles and minimal spanning tree problems from examples without providing the underlying rules.
arXiv Detail & Related papers (2020-06-18T20:09:53Z) - Learning to Satisfy Unknown Constraints in Iterative MPC [3.306595429364865]
We propose a control design method for linear time-invariant systems that iteratively learns to satisfy unknown polyhedral state constraints.
At each iteration of a repetitive task, the method constructs an estimate of the unknown environment constraints using collected closed-loop trajectory data.
An MPC controller is then designed to robustly satisfy the estimated constraint set.
arXiv Detail & Related papers (2020-06-09T05:19:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.