Using Mean Embeddings for State Estimation and Reinforcement Learning

To act in complex, high-dimensional environments, autonomous systems require versatile state estimation techniques and compact state representations. State estimation is crucial when the system only has access to stochastic measurements or partial observations. Furthermore, in combination with models of the system such techniques allow to predict the future which enables the system to asses the outcome of possible decisions. Compact state representations alleviate the curse of dimensionality by distilling the important information from high-dimensional observations. Due to noisy sensory information and non-perfect models of the system, estimates of the state never reflect the true state perfectly but are always subject to errors. The natural choice to incorporate the uncertainty about the state estimate is to use a probability distribution as representation. This results in the so called belief state. High-dimensional observations, for example images, often contain much less information than conveyed by their dimensionality. But also if all the information is necessary to describe the state of the system—for example, think of the state of a swarm with the positions of all agents—a less complex description might be a sufficient representation. In such situations, finding the generative distribution that explains the state would give a much more compact while informative representation. Traditionally, parametric distributions have been used as state representations such as most prevalently the Gaussian distribution. However, in many cases a unimodal distribution might not be sufficient to represent the belief state. Using multi-modal probability distributions, instead, requires more advanced approaches such as mixture models or particle-based Monte Carlo methods. Learning mixture models is however not straight-forward and often results in locally optimal solutions. Similarly, maintaining a good population of particles during inference is a complicated and cumbersome process. A third approach is kernel density estimation which is located at the intersection of mixture models and particle-based approaches. Still, performing inference with any of these approaches requires heuristics that lead to poor performance and a limited scalability to higher dimensional spaces. A recent technique that alleviates this problem are the embeddings of probability distributions into reproducing kernel Hilbert spaces (RKHS). Conditional distributions can be embedded as operators based on which a framework for inference has been presented that allows to apply the sum rule, the product rule and Bayes’ rule entirely in Hilbert space. Using sample based estimators and the kernel-trick of the representer theorem allows to represent the operations as vector-matrix manipulations. The contributions of this thesis are based on or inspired by the embeddings of distributions into reproducing kernel Hilbert spaces. In the first part of this thesis, I propose additions to the framework for nonparametric inference that allow the inference operators to scale more gracefully with the number of samples in the training set. The first contribution is an alternative approach to the conditional embedding operator formulated as a least-squares problem i which allows to use only a subset of the data as representation while using the full data set to learn the conditional operator. I call this operator the subspace conditional embedding operator. Inspired by the least-squares derivations of the Kalman filter, I furthermore propose an alternative operator for Bayesian updates in Hilbert space, the kernel Kalman rule. This alternative approach is numerically more robust than the kernel Bayes rule presented in the framework for non-parametric inference and scales better with the number of samples. Based on the kernel Kalman rule, I derive the kernel Kalman filter and the kernel forward-backward smoother to perform state estimation, prediction and smoothing based on Hilbert space embeddings of the belief state. This representation is able to capture multi-modal distributions and inference resolves--due to the kernel trick--into easy matrix manipulations. In the second part of this thesis, I propose a representation for large sets of homogeneous observations. Specifically, I consider the problem of learning a controller for object assembly and object manipulation with a robotic swarm. I assume a swarm of homogeneous robots that are controlled by a common input signal, e.g., the gradient of a light source or a magnetic field. Learning policies for swarms is a challenging problem since the state space grows with the number of agents and becomes quickly very high dimensional. Furthermore, the exact number of agents and the order of the agents in the observation is not important to solve the task. To approach this issue, I propose the swarm kernel which uses a Hilbert space embedding to represent the swarm. Instead of the exact positions of the agents in the swarm, the embedding estimates the generative distribution behind the swarm configuration. The specific agent positions are regarded as samples of this distribution. Since the swarm kernel compares the embeddings of distributions, it can compare swarm configurations with varying numbers of individuals and is invariant to the permutation of the agents. I present a hierarchical approach for solving the object manipulation task where I assume a high-level object assembly policy as given. To learn the low-level object pushing policy, I use the swarm kernel with an actor-critic policy search method. The policies which I learn in simulation can be directly transferred to a real robotic system. In the last part of this thesis, I investigate how we can employ the idea of kernel mean embeddings to deep reinforcement learning. As in the previous part, I consider a variable number of homogeneous observations—such as robot swarms where the number of agents can change. Another example is the representation of 3D structures as point clouds. The number of points in such clouds can vary strongly and the order of the points in a vectorized representation is arbitrary. The common architectures for neural networks have a fixed structure that requires that the dimensionality of inputs and outputs is known in advance. A variable number of inputs can only be processed by applying tricks. To approach this problem, I propose the deep M-embeddings which are inspired by the kernel mean embeddings. The deep M-embeddings provide a network structure to compute a fixed length representation from a variable number of inputs. Additionally, the deep M-embeddings exploit the homogeneous nature of the inputs to reduce the number of parameters in the network and, thus, make the learning easier. Similar to the swarm kernel, the policies learned with the deep M-embeddings can be transferred to different swarm sizes and different number of objects in the environment without further learning

Nearly Consistent Finite Particle Estimates in Streaming Importance Sampling

In Bayesian inference, we seek to compute information about random variables such as moments or quantiles on the basis of {available data} and prior information. When the distribution of random variables is {intractable}, Monte Carlo (MC) sampling is usually required. {Importance sampling is a standard MC tool that approximates this unavailable distribution with a set of weighted samples.} This procedure is asymptotically consistent as the number of MC samples (particles) go to infinity. However, retaining infinitely many particles is intractable. Thus, we propose a way to only keep a \emph{finite representative subset} of particles and their augmented importance weights that is \emph{nearly consistent}. To do so in {an online manner}, we (1) embed the posterior density estimate in a reproducing kernel Hilbert space (RKHS) through its kernel mean embedding; and (2) sequentially project this RKHS element onto a lower-dimensional subspace in RKHS using the maximum mean discrepancy, an integral probability metric. Theoretically, we establish that this scheme results in a bias determined by a compression parameter, which yields a tunable tradeoff between consistency and memory. In experiments, we observe the compressed estimates achieve comparable performance to the dense ones with substantial reductions in representational complexity

Stochastic Optimization For Multi-Agent Statistical Learning And Control

The goal of this thesis is to develop a mathematical framework for optimal, accurate, and affordable complexity statistical learning among networks of autonomous agents. We begin by noting the connection between statistical inference and stochastic programming, and consider extensions of this setup to settings in which a network of agents each observes a local data stream and would like to make decisions that are good with respect to information aggregated across the entire network. There is an open-ended degree of freedom in this problem formulation, however: the selection of the estimator function class which defines the feasible set of the stochastic program. Our central contribution is the design of stochastic optimization tools in reproducing kernel Hilbert spaces that yield optimal, accurate, and affordable complexity statistical learning for a multi-agent network. To obtain this result, we first explore the relative merits and drawbacks of different function class selections. In Part I, we consider multi-agent expected risk minimization this problem setting for the case that each agent seems to learn a common globally optimal generalized linear models (GLMs) by developing a stochastic variant of Arrow-Hurwicz primal-dual method. We establish convergence to the primal-dual optimal pair when either consensus or ``proximity constraints encode the fact that we want all agents\u27 to agree, or nearby agents to make decisions that are close to one another. Empirically, we observe that these convergence results are substantiated but that convergence may not translate into statistical accuracy. More broadly, optimality within a given estimator function class is not the same as one that makes minimal inference errors. The optimality-accuracy tradeoff of GLMs motivates subsequent efforts to learn more sophisticated estimators based upon learned feature encodings of the data that is fed into the statistical model. The specific tool we turn to in Part II is dictionary learning, where we optimize both over regression weights and an encoding of the data, which yields a non-convex problem. We investigate the use of stochastic methods for online task-driven dictionary learning, and obtain promising performance for the task of a ground robot learning to anticipate control uncertainty based on its past experience. Heartened by this implementation, we then consider extensions of this framework for a multi-agent network to each learn globally optimal task-driven dictionaries based on stochastic primal-dual methods. However, it is here the non-convexity of the optimization problem causes problems: stringent conditions on stochastic errors and the duality gap limit the applicability of the convergence guarantees, and impractically small learning rates are required for convergence in practice. Thus, we seek to learn nonlinear statistical models while preserving convexity, which is possible through kernel methods ( Part III). However, the increased descriptive power of nonparametric estimation comes at the cost of infinite complexity. Thus, we develop a stochastic approximation algorithm in reproducing kernel Hilbert spaces (RKHS) that ameliorates this complexity issue while preserving optimality: we combine the functional generalization of stochastic gradient method (FSGD) with greedily constructed low-dimensional subspace projections based on matching pursuit. We establish that the proposed method yields a controllable trade-off between optimality and memory, and yields highly accurate parsimonious statistical models in practice. % Then, we develop a multi-agent extension of this method by proposing a new node-separable penalty function and applying FSGD together with low-dimensional subspace projections. This extension allows a network of autonomous agents to learn a memory-efficient approximation to the globally optimal regression function based only on their local data stream and message passing with neighbors. In practice, we observe agents are able to stably learn highly accurate and memory-efficient nonlinear statistical models from streaming data. From here, we shift focus to a more challenging class of problems, motivated by the fact that true learning is not just revising predictions based upon data but augmenting behavior over time based on temporal incentives. This goal may be described by Markov Decision Processes (MDPs): at each point, an agent is in some state of the world, takes an action and then receives a reward while randomly transitioning to a new state. The goal of the agent is to select the action sequence to maximize its long-term sum of rewards, but determining how to select this action sequence when both the state and action spaces are infinite has eluded researchers for decades. As a precursor to this feat, we consider the problem of policy evaluation in infinite MDPs, in which we seek to determine the long-term sum of rewards when starting in a given state when actions are chosen according to a fixed distribution called a policy. We reformulate this problem as a RKHS-valued compositional stochastic program and we develop a functional extension of stochastic quasi-gradient algorithm operating in tandem with the greedy subspace projections mentioned above. We prove convergence with probability 1 to the Bellman fixed point restricted to this function class, and we observe a state of the art trade off in memory versus Bellman error for the proposed method on the Mountain Car driving task, which bodes well for incorporating policy evaluation into more sophisticated, provably stable reinforcement learning techniques, and in time, developing optimal collaborative multi-agent learning-based control systems

Recent advances in directional statistics

Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio-temporal data. An overview of currently available software for analysing directional data is also provided, and potential future developments discussed.Comment: 61 page

Statistical Filtering for Multimodal Mobility Modeling in Cyber Physical Systems

A Cyber-Physical System integrates computations and dynamics of physical processes. It is an engineering discipline focused on technology with a strong foundation in mathematical abstractions. It shares many of these abstractions with engineering and computer science, but still requires adaptation to suit the dynamics of the physical world. In such a dynamic system, mobility management is one of the key issues against developing a new service. For example, in the study of a new mobile network, it is necessary to simulate and evaluate a protocol before deployment in the system. Mobility models characterize mobile agent movement patterns. On the other hand, they describe the conditions of the mobile services. The focus of this thesis is on mobility modeling in cyber-physical systems. A macroscopic model that captures the mobility of individuals (people and vehicles) can facilitate an unlimited number of applications. One fundamental and obvious example is traffic profiling. Mobility in most systems is a dynamic process and small non-linearities can lead to substantial errors in the model. Extensive research activities on statistical inference and filtering methods for data modeling in cyber-physical systems exist. In this thesis, several methods are employed for multimodal data fusion, localization and traffic modeling. A novel energy-aware sparse signal processing method is presented to process massive sensory data. At baseline, this research examines the application of statistical filters for mobility modeling and assessing the difficulties faced in fusing massive multi-modal sensory data. A statistical framework is developed to apply proposed methods on available measurements in cyber-physical systems. The proposed methods have employed various statistical filtering schemes (i.e., compressive sensing, particle filtering and kernel-based optimization) and applied them to multimodal data sets, acquired from intelligent transportation systems, wireless local area networks, cellular networks and air quality monitoring systems. Experimental results show the capability of these proposed methods in processing multimodal sensory data. It provides a macroscopic mobility model of mobile agents in an energy efficient way using inconsistent measurements

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio