1,971 research outputs found
Uncertainty quantification of coal seam gas production prediction using Polynomial Chaos
A surrogate model approximates a computationally expensive solver. Polynomial
Chaos is a method to construct surrogate models by summing combinations of
carefully chosen polynomials. The polynomials are chosen to respect the
probability distributions of the uncertain input variables (parameters); this
allows for both uncertainty quantification and global sensitivity analysis.
In this paper we apply these techniques to a commercial solver for the
estimation of peak gas rate and cumulative gas extraction from a coal seam gas
well. The polynomial expansion is shown to honour the underlying geophysics
with low error when compared to a much more complex and computationally slower
commercial solver. We make use of advanced numerical integration techniques to
achieve this accuracy using relatively small amounts of training data
Uncertainty Aware Mapping of Embedded Systems for Reliability, Performance, and Energy
Due to technology downscaling, embedded systems have increased in complexity and heterogeneity. The increasingly large process, voltage, and temperature variations negatively affect the design and optimization process of these systems. These factors contribute to increased uncertainties that in turn undermine the accuracy and effectiveness of traditional design approaches. In this thesis, we formulate the problem of uncertainty aware mapping for multicore embedded system platforms as a multi-objective optimization problem. We present a solution to this problem that integrates uncertainty models as a new design methodology constructed with Monte Carlo and evolutionary algorithms. The solution is uncertainty aware because it is able to model uncertainties in design parameters and to identify robust design points that limit the influence of these uncertainties onto the objective functions. The proposed design methodology is implemented as a tool that can generate the robust Pareto frontier in the objective space formed by reliability, performance, and energy consumption
Budgeted Reinforcement Learning in Continuous State Space
A Budgeted Markov Decision Process (BMDP) is an extension of a Markov
Decision Process to critical applications requiring safety constraints. It
relies on a notion of risk implemented in the shape of a cost signal
constrained to lie below an - adjustable - threshold. So far, BMDPs could only
be solved in the case of finite state spaces with known dynamics. This work
extends the state-of-the-art to continuous spaces environments and unknown
dynamics. We show that the solution to a BMDP is a fixed point of a novel
Budgeted Bellman Optimality operator. This observation allows us to introduce
natural extensions of Deep Reinforcement Learning algorithms to address
large-scale BMDPs. We validate our approach on two simulated applications:
spoken dialogue and autonomous driving.Comment: N. Carrara and E. Leurent have equally contribute
Risk-Sensitive Reinforcement Learning: A Constrained Optimization Viewpoint
The classic objective in a reinforcement learning (RL) problem is to find a
policy that minimizes, in expectation, a long-run objective such as the
infinite-horizon discounted or long-run average cost. In many practical
applications, optimizing the expected value alone is not sufficient, and it may
be necessary to include a risk measure in the optimization process, either as
the objective or as a constraint. Various risk measures have been proposed in
the literature, e.g., mean-variance tradeoff, exponential utility, the
percentile performance, value at risk, conditional value at risk, prospect
theory and its later enhancement, cumulative prospect theory. In this article,
we focus on the combination of risk criteria and reinforcement learning in a
constrained optimization framework, i.e., a setting where the goal to find a
policy that optimizes the usual objective of infinite-horizon
discounted/average cost, while ensuring that an explicit risk constraint is
satisfied. We introduce the risk-constrained RL framework, cover popular risk
measures based on variance, conditional value-at-risk and cumulative prospect
theory, and present a template for a risk-sensitive RL algorithm. We survey
some of our recent work on this topic, covering problems encompassing
discounted cost, average cost, and stochastic shortest path settings, together
with the aforementioned risk measures in a constrained framework. This
non-exhaustive survey is aimed at giving a flavor of the challenges involved in
solving a risk-sensitive RL problem, and outlining some potential future
research directions
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