Co-Optimization of Environment and Policies for Decentralized Multi-Agent Navigation

This work views the multi-agent system and its surrounding environment as a co-evolving system, where the behavior of one affects the other. The goal is to take both agent actions and environment configurations as decision variables, and optimize these two components in a coordinated manner to improve some measure of interest. Towards this end, we consider the problem of decentralized multi-agent navigation in cluttered environments. By introducing two sub-objectives of multi-agent navigation and environment optimization, we propose an $\textit{agent-environment co-optimization}$ problem and develop a $\textit{coordinated algorithm}$ that alternates between these sub-objectives to search for an optimal synthesis of agent actions and obstacle configurations in the environment; ultimately, improving the navigation performance. Due to the challenge of explicitly modeling the relation between agents, environment and performance, we leverage policy gradient to formulate a model-free learning mechanism within the coordinated framework. A formal convergence analysis shows that our coordinated algorithm tracks the local minimum trajectory of an associated time-varying non-convex optimization problem. Extensive numerical results corroborate theoretical findings and show the benefits of co-optimization over baselines. Interestingly, the results also indicate that optimized environment configurations are able to offer structural guidance that is key to de-conflicting agents in motion.

On the Trade-Off between Stability and Representational Capacity in Graph Neural Networks

Analyzing the stability of graph neural networks (GNNs) under topological perturbations is key to understanding their transferability and the role of each architecture component. However, stability has been investigated only for particular architectures, questioning whether it holds for a broader spectrum of GNNs or only for a few instances. To answer this question, we study the stability of EdgeNet: a general GNN framework that unifies more than twenty solutions including the convolutional and attention-based classes, as well as graph isomorphism networks and hybrid architectures. We prove that all GNNs within the EdgeNet framework are stable to topological perturbations. By studying the effect of different EdgeNet categories on the stability, we show that GNNs with fewer degrees of freedom in their parameter space, linked to a lower representational capacity, are more stable. The key factor yielding this trade-off is the eigenvector misalignment between the EdgeNet parameter matrices and the graph shift operator. For example, graph convolutional neural networks that assign a single scalar per signal shift (hence, with a perfect alignment) are more stable than the more involved node or edge-varying counterparts. Extensive numerical results corroborate our theoretical findings and highlight the role of different architecture components in the trade-off.

An Improved Neural Network Model Based On CNN Using For Fruit Sugar Degree Detection

Artificial Intelligence(AI) widely applies in Image Classification and Recognition, Text Understanding and Natural Language Processing, which makes great progress. In this paper, we introduced AI into the fruit quality detection field. We designed a fruit sugar degree regression model using an Artificial Neural Network based on spectra of fruits within the visible/near-infrared(V/NIR)range. After analysis of fruit spectra, we innovatively proposed a new neural network structure: low layers consist of a Multilayer Perceptron(MLP), a middle layer is a 2-dimensional correlation matrix layer, and high layers consist of several Convolutional Neural Network(CNN) layers. In this study, we used fruit sugar value as a detection target, collecting two fruits called Gan Nan Navel and Tian Shan Pear as samples, doing experiments respectively, and comparing their results. We used Analysis of Variance(ANOVA) to evaluate the reliability of the dataset we collected. Then, we tried multiple strategies to process spectrum data, evaluating their effects. In this paper, we tried to add Wavelet Decomposition(WD) to reduce feature dimensions and a Genetic Algorithm(GA) to find excellent features. Then, we compared Neural Network models with traditional Partial Least Squares(PLS) based models. We also compared the neural network structure we designed(MLP-CNN) with other traditional neural network structures. In this paper, we proposed a new evaluation standard derived from dataset standard deviation(STD) for evaluating detection performance, validating the viability of using an artificial neural network model to do fruit sugar degree nondestructive detection.

Limited-Memory Greedy Quasi-Newton Method with Non-asymptotic Superlinear Convergence Rate

Non-asymptotic convergence analysis of quasi-Newton methods has gained attention with a landmark result establishing an explicit superlinear rate of O$((1/\sqrt{t})^t)$. The methods that obtain this rate, however, exhibit a well-known drawback: they require the storage of the previous Hessian approximation matrix or instead storing all past curvature information to form the current Hessian inverse approximation. Limited-memory variants of quasi-Newton methods such as the celebrated L-BFGS alleviate this issue by leveraging a limited window of past curvature information to construct the Hessian inverse approximation. As a result, their per iteration complexity and storage requirement is O$(\tau d)$ where $\tau \le d$ is the size of the window and $d$ is the problem dimension reducing the O$(d^2)$ computational cost and memory requirement of standard quasi-Newton methods. However, to the best of our knowledge, there is no result showing a non-asymptotic superlinear convergence rate for any limited-memory quasi-Newton method. In this work, we close this gap by presenting a limited-memory greedy BFGS (LG-BFGS) method that achieves an explicit non-asymptotic superlinear rate. We incorporate displacement aggregation, i.e., decorrelating projection, in post-processing gradient variations, together with a basis vector selection scheme on variable variations, which greedily maximizes a progress measure of the Hessian estimate to the true Hessian. Their combination allows past curvature information to remain in a sparse subspace while yielding a valid representation of the full history. Interestingly, our established non-asymptotic superlinear convergence rate demonstrates a trade-off between the convergence speed and memory requirement, which to our knowledge, is the first of its kind. Numerical results corroborate our theoretical findings and demonstrate the effectiveness of our method.

Differentially Private Decentralized Deep Learning with Consensus Algorithms

Cooperative decentralized deep learning relies on direct information exchange between communicating agents, each with access to a local dataset which should be kept private. The goal is for all agents to achieve consensus on model parameters after training. However, sharing parameters with untrustworthy neighboring agents could leak exploitable information about local datasets. To combat this, we introduce differentially private decentralized learning that secures each agent's local dataset during and after cooperative training. In our approach, we generalize Differentially Private Stochastic Gradient Descent (DP-SGD) -- a popular differentially private training method for centralized deep learning -- to practical subgradient- and ADMM-based decentralized learning methods. Our algorithms' differential privacy guarantee holds for arbitrary deep learning objective functions, and we analyze the convergence properties for strongly convex objective functions. We compare our algorithms against centrally trained models on standard classification tasks and evaluate the relationships between performance, privacy budget, graph connectivity, and degree of training data overlap among agents. We find that differentially private gradient tracking is resistant to performance degradation under sparse graphs and non-uniform data distributions. Furthermore, we show that it is possible to learn a model achieving high accuracies, within 3% of DP-SGD on MNIST under (1, 10^-5)-differential privacy and within 6% of DP-SGD on CIFAR-100 under (10, 10^-5)-differential privacy, without ever sharing raw data with other agents. Open source code can be found at: https://github.com/jbayrooti/dp-dec-learning.

Spectrum Sharing between High Altitude Platform Network and Terrestrial Network: Modeling and Performance Analysis

Achieving seamless global coverage is one of the ultimate goals of space-air-ground integrated network, as a part of which High Altitude Platform (HAP) network can provide wide-area coverage. However, deploying a large number of HAPs will lead to severe congestion of existing frequency bands. Spectrum sharing improves spectrum utilization. The coverage performance improvement and interference caused by spectrum sharing need to be investigated. To this end, this paper analyzes the performance of spectrum sharing between HAP network and terrestrial network. We firstly generalize the Poisson Point Process (PPP) to curves, surfaces and manifolds to model the distribution of terrestrial Base Stations (BSs) and HAPs. Then, the closed-form expressions for coverage probability of HAP network and terrestrial network are derived based on differential geometry and stochastic geometry. We verify the accuracy of closed-form expressions by Monte Carlo simulation. The results show that HAP network has less interference to terrestrial network. Low height and suitable deployment density can improve the coverage probability and transmission capacity of HAP network.

Constrained Environment Optimization for Prioritized Multi-Agent Navigation

Traditional approaches to the design of multi-agent navigation algorithms consider the environment as a fixed constraint, despite the influence of spatial constraints on agents' performance. Yet hand-designing conducive environment layouts is inefficient and potentially expensive. The goal of this paper is to consider the environment as a decision variable in a system-level optimization problem, where both agent performance and environment cost are incorporated. Towards this end, we propose novel problems of unprioritized and prioritized environment optimization, where the former considers agents unbiasedly and the latter accounts for agent priorities. We show, through formal proofs, under which conditions the environment can change while guaranteeing completeness (i.e., all agents reach goals), and analyze the role of agent priorities in the environment optimization. We proceed to impose real-world constraints on the environment optimization and formulate it mathematically as a constrained stochastic optimization problem. Since the relation between agents, environment and performance is challenging to model, we leverage reinforcement learning to develop a model-free solution and a primal-dual mechanism to handle constraints. Distinct information processing architectures are integrated for various implementation scenarios, including online/offline optimization and discrete/continuous environment. Numerical results corroborate the theory and demonstrate the validity and adaptability of our approach.

Learning Environment-Aware Control Barrier Functions for Safe and Feasible Multi-Robot Navigation

Control Barrier Functions (CBFs) have been applied to provide safety guarantees for robot navigation. Traditional approaches consider fixed CBFs during navigation and hand-tune the underlying parameters apriori. Such approaches are inefficient and vulnerable to changes in the environment. The goal of this paper is to learn CBFs for multi-robot navigation based on what robots perceive about their environment. In order to guarantee the feasibility of the navigation task, while ensuring robot safety, we pursue a trade-off between conservativeness and aggressiveness in robot behavior by defining dynamic environment-aware CBF constraints. Since the explicit relationship between CBF constraints and navigation performance is challenging to model, we leverage reinforcement learning to learn time-varying CBFs in a model-free manner. We parameterize the CBF policy with graph neural networks (GNNs), and design GNNs that are translation invariant and permutation equivariant, to synthesize decentralized policies that generalize across environments. The proposed approach maintains safety guarantees (due to the underlying CBFs), while optimizing navigation performance (due to the reward-based learning). We perform simulations that compare the proposed approach with fixed CBFs tuned by exhaustive grid-search. The results show that environment-aware CBFs are capable of adapting to robot movements and obstacle changes, yielding improved navigation performance and robust generalization.

AirGNN: Graph Neural Network over the Air

Graph neural networks (GNNs) are information processing architectures that model representations from networked data and allow for decentralized implementation through localized communications. Existing GNN architectures often assume ideal communication links, and ignore channel effects, such as fading and noise, leading to performance degradation in real-world implementation. This paper proposes graph neural networks over the air (AirGNNs), a novel GNN architecture that incorporates the communication model into the architecture. The AirGNN modifies the graph convolutional operation that shifts graph signals over random communication graphs to take into account channel fading and noise when aggregating features from neighbors, thus, improving the architecture robustness to channel impairments during testing. We propose a stochastic gradient descent based method to train the AirGNN, and show that the training procedure converges to a stationary solution. Numerical simulations on decentralized source localization and multi-robot flocking corroborate theoretical findings and show superior performance of the AirGNN over wireless communication channels.

Learning Stable Graph Neural Networks via Spectral Regularization

Stability of graph neural networks (GNNs) characterizes how GNNs react to graph perturbations and provides guarantees for architecture performance in noisy scenarios. This paper develops a self-regularized graph neural network (SR-GNN) solution that improves the architecture stability by regularizing the filter frequency responses in the graph spectral domain. The SR-GNN considers not only the graph signal as input but also the eigenvectors of the underlying graph, where the signal is processed to generate task-relevant features and the eigenvectors to characterize the frequency responses at each layer. We train the SR-GNN by minimizing the cost function and regularizing the maximal frequency response close to one. The former improves the architecture performance, while the latter tightens the perturbation stability and alleviates the information loss through multi-layer propagation. We further show the SR-GNN preserves the permutation equivariance, which allows to explore the internal symmetries of graph signals and to exhibit transference on similar graph structures. Numerical results with source localization and movie recommendation corroborate our findings and show the SR-GNN yields a comparable performance with the vanilla GNN on the unperturbed graph but improves substantially the stability.