1,877 research outputs found
Geodesic Density Tracking with Applications to Data Driven Modeling
Many problems in dynamic data driven modeling deals with distributed rather
than lumped observations. In this paper, we show that the Monge-Kantorovich
optimal transport theory provides a unifying framework to tackle such problems
in the systems-control parlance. Specifically, given distributional
measurements at arbitrary instances of measurement availability, we show how to
derive dynamical systems that interpolate the observed distributions along the
geodesics. We demonstrate the framework in the context of three specific
problems: (i) \emph{finding a feedback control} to track observed ensembles
over finite-horizon, (ii) \emph{finding a model} whose prediction matches the
observed distributional data, and (iii) \emph{refining a baseline model} that
results a distribution-level prediction-observation mismatch. We emphasize how
the three problems can be posed as variants of the optimal transport problem,
but lead to different types of numerical methods depending on the problem
context. Several examples are given to elucidate the ideas.Comment: 8 pages, 7 figure
Identifiability in inverse reinforcement learning
Inverse reinforcement learning attempts to reconstruct the reward function in
a Markov decision problem, using observations of agent actions. As already
observed in Russell [1998] the problem is ill-posed, and the reward function is
not identifiable, even under the presence of perfect information about optimal
behavior. We provide a resolution to this non-identifiability for problems with
entropy regularization. For a given environment, we fully characterize the
reward functions leading to a given policy and demonstrate that, given
demonstrations of actions for the same reward under two distinct discount
factors, or under sufficiently different environments, the unobserved reward
can be recovered up to a constant. We also give general necessary and
sufficient conditions for reconstruction of time-homogeneous rewards on finite
horizons, and for action-independent rewards, generalizing recent results of
Kim et al. [2021] and Fu et al. [2018]
A Survey of the Probability Density Function Control for Stochastic Dynamic Systems
Probability density function (PDF) control strategy investigates the controller design approaches in order to to realise a desirable distributions shape control of the random variables for the stochastic processes. Different from the existing stochastic optimisation and control methods, the most important problem of PDF control is to establish the evolution of the PDF expressions of the system variables. Once the relationship between the control input and the output PDF is formulated, the control objective can be described as obtaining the control input signals which would adjust the system output PDFs to follow the pre-specified target PDFs. This paper summarises the recent research results of the PDF control while the controller design approaches can be categorised into three groups: 1) system model-based direct evolution PDF control; 2) model-based distribution-transformation PDF control methods and 3) databased PDF control. In addition, minimum entropy control, PDF-based filter design, fault diagnosis and probabilistic decoupling design are also introduced briefly as extended applications in theory sense
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