This surge in energy demand not only places strain on existing resources but also raises critical concerns regarding environmental sustainability, largely due to the predominant utilization of fossil fuels [1]. In light of these complex challenges, the electrification of transportation has emerged as a compelling avenue for resolution [1 3]. Consequently, automotive manufacturers are progressively pivoting away from conventional fossil fuel-powered vehicles, embracing innovative energy alternatives such as battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), and fuel cell electric vehicles (FCEVs) [4 6]. Since such electric vehicles (EVs) stand out for their ability to enhance fuel economy, reduce emissions, and extend mileage range while navigating urban and environmental restrictions, however, the main limitations include a limited range compared to HEVs. HEVs allow them to offer the benefits of electrification without the range and charging constraints of BEVs. And FCEVs boast a longer range and faster refueling times compared to BEVs but are limited by the current scarcity of hydrogen refueling infrastructure. However, in response to these challenges, the effective energy management system (EMS) has emerged as a pivotal solution for optimizing energy usage and enhancing efficiency across various sectors.

We present a convolutional framework which significantly reduces the complexity and thus, the computational effort for distributed reinforcement learning control of dynamical systems governed by partial differential equations (PDEs). Exploiting translational invariances, the high-dimensional distributed control problem can be transformed into a multi-agent control problem with many identical, uncoupled agents. Furthermore, using the fact that information is transported with finite velocity in many cases, the dimension of the agents' environment can be drastically reduced using a convolution operation over the state space of the PDE. In this setting, the complexity can be flexibly adjusted via the kernel width or by using a stride greater than one. Moreover, scaling from smaller to larger systems -- or the transfer between different domains -- becomes a straightforward task requiring little effort. We demonstrate the performance of the proposed framework using several PDE examples with increasing complexity, where stabilization is achieved by training a low-dimensional deep deterministic policy gradient agent using minimal computing resources.

We present a distributed source-seeking and flocking control method for networked multi-agent systems with non-holonomic constraints. Based solely on identical on-board sensor systems, which measure the source local field, the group objective is attained by appointing a leader agent to seek the source while the remaining follower agents form a cohesive flocking with their neighbors using a distributed flocking control law in a connectivity-preserved undirected network. To guarantee the safe separation and group motion for all agents and to solve the conflicts with the "cohesion" flocking rule of Reynolds, the distributed control algorithm is solved individually through quadratic-programming optimization problem with constraints, which guarantees the inter-agent collision avoidance and connectivity preservation. Stability analysis of the closed-loop system is presented and the efficacy of the methods is shown in simulation results.

Under the uncoupled learning setup, the last-iterate convergence guarantee towards Nash equilibrium is shown to be impossible in many games. This work studies the last-iterate convergence guarantee in general games toward rationalizability, a key solution concept in epistemic game theory that relaxes the stringent belief assumptions in both Nash and correlated equilibrium. This learning task naturally generalizes best arm identification problems, due to the intrinsic connections between rationalizable action profiles and the elimination of iteratively dominated actions. Despite a seemingly simple task, our first main result is a surprisingly negative one; that is, a large and natural class of no regret algorithms, including the entire family of Dual Averaging algorithms, provably take exponentially many rounds to reach rationalizability. Moreover, algorithms with the stronger no swap regret also suffer similar exponential inefficiency. To overcome these barriers, we develop a new algorithm that adjusts Exp3 with Diminishing Historical rewards (termed Exp3-DH); Exp3-DH gradually "forgets" history at carefully tailored rates. We prove that when all agents run Exp3-DH (a.k.a., self-play in multi-agent learning), all iteratively dominated actions can be eliminated within polynomially many rounds. Our experimental results further demonstrate the efficiency of Exp3-DH, and that state-of-the-art bandit algorithms, even those developed specifically for learning in games, fail to reach rationalizability efficiently.

Particle Swarm Optimization (PSO) has demonstrated efficacy in addressing static path planning problems. Nevertheless, such application on dynamic scenarios has been severely precluded by PSO's low computational efficiency and premature convergence downsides. To address these limitations, we proposed a Tensor Operation Form (TOF) that converts particle-wise manipulations to tensor operations, thereby enhancing computational efficiency. Harnessing the computational advantage of TOF, a variant of PSO, designated as Self-Evolving Particle Swarm Optimization (SEPSO) was developed. The SEPSO is underpinned by a novel Hierarchical Self-Evolving Framework (HSEF) that enables autonomous optimization of its own hyper-parameters to evade premature convergence. Additionally, a Priori Initialization (PI) mechanism and an Auto Truncation (AT) mechanism that substantially elevates the real-time performance of SEPSO on dynamic path planning problems were introduced. Comprehensive experiments on four widely used benchmark optimization functions have been initially conducted to corroborate the validity of SEPSO. Following this, a dynamic simulation environment that encompasses moving start/target points and dynamic/static obstacles was employed to assess the effectiveness of SEPSO on the dynamic path planning problem. Simulation results exhibit that the proposed SEPSO is capable of generating superior paths with considerably better real-time performance (67 path planning computations per second in a regular desktop computer) in contrast to alternative methods. The code and video of this paper can be accessed here.

As autonomous systems increasingly become part of our lives, it is crucial to foster trust between humans and these systems, to ensure positive outcomes and mitigate harmful ones.

Implicit representations such as Neural Radiance Fields (NeRF) have been shown to be very effective at novel view synthesis. However, these models typically require manual and careful human data collection for training. In this paper, we present AutoNeRF, a method to collect data required to train NeRFs using autonomous embodied agents. Our method allows an agent to explore an unseen environment efficiently and use the experience to build an implicit map representation autonomously. We compare the impact of different exploration strategies including handcrafted frontier-based exploration, end-to-end and modular approaches composed of trained high-level planners and classical low-level path followers. We train these models with different reward functions tailored to this problem and evaluate the quality of the learned representations on four different downstream tasks: classical viewpoint rendering, map reconstruction, planning, and pose refinement. Empirical results show that NeRFs can be trained on actively collected data using just a single episode of experience in an unseen environment, and can be used for several downstream robotic tasks, and that modular trained exploration models outperform other classical and end-to-end baselines. Finally, we show that AutoNeRF can reconstruct large-scale scenes, and is thus a useful tool to perform scene-specific adaptation as the produced 3D environment models can be loaded into a simulator to fine-tune a policy of interest.

Keywords: carbon fiber reinforced polymer, concrete, confinement effect, strength, particle swarm optimization, grey wolf optimizer, bat algorithm Abstract This article deals with the study of predicting the confinement effect of carbon fiber reinforced polymers (CFRPs) on concrete cylinder strength using metaheuristics-based artificial neural networks. Three metaheuristic models are implemented including particle swarm optimization (PSO), grey wolf optimizer (GWO), and bat algorithm (BA). These algorithms are trained on the data using an objective function of mean square error and their predicted results are validated against the experimental studies and finite element analysis. The study shows that the hybrid model of PSO predicted the strength of CFRP-confined concrete cylinders with maximum accuracy of 99.13% and GWO predicted the results with an accuracy of 98.17%. The high accuracy of axial compressive strength predictions demonstrated that these prediction models are a reliable solution to the empirical methods. The prediction models are especially suitable for avoiding full-scale time-consuming experimental tests that make the process quick and economical. 1 Introduction Fiber-reinforced polymer is a composite material comprising fibers of either glass, aramid, or carbon and a polymer matrix. These fibers improve the properties of the polymer matrix mechanically including its stiffness and strength. The popularity of these composites has increased significantly in civil engineering due to their ability to strengthen concrete structural members. FRPs can be used either as a bar or plates embedded in concrete as an internal reinforcement and can be used as an external reinforcement by wrapping FRP sheets to existing structural members. The FRP bars have significantly higher strength than the steel reinforcement bars. They are highly durable and resistant to chemicals, corrosion (Cousin et al. 2019, Ananthkumar et al. 2020, Zhang et al. 2020), and radiation, their higher strength-to-weight ratio (Zhou et al. 2019) makes them ideal for structures that require high strength but need not be heavy. They can be molded into any required shape that provides higher design flexibility. Moreover, it has a lower environmental impact (Lee and Jain 2009), unlike concrete and timber.

Recent advances in reinforcement learning (RL) and Human-in-the-Loop (HitL) learning have made human-AI collaboration easier for humans to team with AI agents. Leveraging human expertise and experience with AI in intelligent systems can be efficient and beneficial. Still, it is unclear to what extent human-AI collaboration will be successful, and how such teaming performs compared to humans or AI agents only. In this work, we show that learning from humans is effective and that human-AI collaboration outperforms human-controlled and fully autonomous AI agents in a complex simulation environment. In addition, we have developed a new simulator for critical infrastructure protection, focusing on a scenario where AI-powered drones and human teams collaborate to defend an airport against enemy drone attacks. We develop a user interface to allow humans to assist AI agents effectively. We demonstrated that agents learn faster while learning from policy correction compared to learning from humans or agents. Furthermore, human-AI collaboration requires lower mental and temporal demands, reduces human effort, and yields higher performance than if humans directly controlled all agents. In conclusion, we show that humans can provide helpful advice to the RL agents, allowing them to improve learning in a multi-agent setting.

We study a majority based preference diffusion model in which the members of a social network update their preferences based on those of their connections. Consider an undirected graph where each node has a strict linear order over a set of $\alpha$ alternatives. At each round, a node randomly selects two adjacent alternatives and updates their relative order with the majority view of its neighbors. We bound the convergence time of the process in terms of the number of nodes/edges and $\alpha$. Furthermore, we study the minimum cost to ensure that a desired alternative will ``win'' the process, where occupying each position in a preference order of a node has a cost. We prove tight bounds on the minimum cost for general graphs and graphs with strong expansion properties. Furthermore, we investigate a more light-weight process where each node chooses one of its neighbors uniformly at random and copies its order fully with some fixed probability and remains unchanged otherwise. We characterize the convergence properties of this process, namely convergence time and stable states, using Martingale and reversible Markov chain analysis. Finally, we present the outcomes of our experiments conducted on different synthetic random graph models and graph data from online social platforms. These experiments not only support our theoretical findings, but also shed some light on some other fundamental problems, such as designing powerful countermeasures.