We uncover a surprising phenomenon in deep reinforcement learning: training a diverse ensemble of data-sharing agents - a well-established exploration strategy - can significantly impair the performance of the individual ensemble members when compared to standard single-agent training. Through careful analysis, we attribute the degradation in performance to the low proportion of self-generated data in the shared training data for each ensemble member, as well as the inefficiency of the individual ensemble members to learn from such highly off-policy data. We thus name this phenomenon the curse of diversity. We find that several intuitive solutions - such as a larger replay buffer or a smaller ensemble size - either fail to consistently mitigate the performance loss or undermine the advantages of ensembling. Finally, we demonstrate the potential of representation learning to counteract the curse of diversity with a novel method named Cross-Ensemble Representation Learning (CERL) in both discrete and continuous control domains. Our work offers valuable insights into an unexpected pitfall in ensemble-based exploration and raises important caveats for future applications of similar approaches. The potential benefits of a diverse ensemble are twofold. At training time, it enables concurrent exploration with multiple distinct policies without the need for additional samples. At test time, the learned policies can be aggregated into a robust ensemble policy, via aggregation methods such as majority voting (Osband et al., 2016) or averaging (Januszewski et al., 2021). Despite the generally positive perception of ensemble-based exploration, we argue that this approach has a potentially negative aspect that has been long overlooked. As shown in Figure 1, for each member in a data-sharing ensemble, only a small proportion of its training data comes from its own interaction with the environment. The majority of its training data is generated by other members of the ensemble, whose policies might be distinct from its own policy. This type of off-policy learning has been shown to be highly challenging in previous work (Ostrovski et al., 2021). We thus hypothesize that similar learning difficulties can also occur in ensemble-based exploration. We verify our hypothesis in the Arcade Learning Environment (Bellemare et al., 2012) with the Bootstrapped DQN (Osband et al., 2016) algorithm and the Gym MuJoCo benchmark (Towers et al., 2023) with an ensemble SAC (Haarnoja et al., 2018a) algorithm. We show that, in many environments, the individual members of a data-sharing ensemble significantly underperform their single-agent counterparts. Moreover, while aggregating the policies of all ensemble members via voting or averaging sometimes compensates for the degradation in individual members' performance, it is not always the case.

Reasoning about large numbers of diverse plans to achieve high speed navigation in cluttered environments remains a challenge for robotic systems even in the case of perfect perceptual information. Often, this is tackled by methods that iteratively optimize around a prior seeded trajectory and consequently restrict to local optima. We present a novel planning method using normalizing flows (NFs) to encode expert-styled motion primitives. We also present an accelerated collision checking framework that enables rejecting samples from the prior distribution before running them through the NF model for rapid sampling of collision-free trajectories. The choice of an NF as the generator permits a flexible way to encode diverse multi-modal behavior distributions while maintaining a smooth relation to the input space which allows approximating collision checks on NF inputs rather than outputs. We show comparable performance to model predictive path integral control in random cluttered environments and improved exit rates in a cul-de-sac environment. We conclude by discussing our plans for future work to improve both safety and performance of our controller.

We report on the development of an implementable physics-data hybrid dynamic model for an articulated manipulator to plan and operate in various scenarios. Meanwhile, the physics-based and data-driven dynamic models are studied in this research to select the best model for planning. The physics-based model is constructed using the Lagrangian method, and the loss terms include inertia loss, viscous loss, and friction loss. As for the data-driven model, three methods are explored, including DNN, LSTM, and XGBoost. Our modeling results demonstrate that, after comprehensive hyperparameter optimization, the XGBoost architecture outperforms DNN and LSTM in accurately representing manipulator dynamics. The hybrid model with physics-based and data-driven terms has the best performance among all models based on the RMSE criteria, and it only needs about 24k of training data. In addition, we developed a virtual force sensor of a manipulator using the observed external torque derived from the dynamic model and designed a motion planner through the physics-data hybrid dynamic model. The external torque contributes to forces and torque on the end effector, facilitating interaction with the surroundings, while the internal torque governs manipulator motion dynamics and compensates for internal losses. By estimating external torque via the difference between measured joint torque and internal losses, we implement a sensorless control strategy which is demonstrated through a peg-in-hole task. Lastly, a learning-based motion planner based on the hybrid dynamic model assists in planning time-efficient trajectories for the manipulator. This comprehensive approach underscores the efficacy of integrating physics-based and data-driven models for advanced manipulator control and planning in industrial environments.

The United States national forest inventory (NFI) serves as the foundation for forest aboveground biomass (AGB) and carbon accounting across the nation. These data enable design-based estimates of forest carbon stocks and stock-changes at state and regional levels, but also serve as inputs to model-based approaches for characterizing forest carbon stocks and stock-changes at finer resolutions. Although NFI tree and plot-level data are often treated as truth in these models, they are in fact estimates based on regional species-group models known collectively as the Component Ratio Method (CRM). In late 2023 the Forest Inventory and Analysis (FIA) program introduced a new National Scale Volume and Biomass Estimators (NSVB) system to replace CRM nationwide and offer more precise and accurate representations of forest AGB and carbon. Given the prevalence of model-based AGB studies relying on FIA, there is concern about the transferability of methods from CRM to NSVB models, as well as the comparability of existing CRM AGB products (e.g. maps) to new and forthcoming NSVB AGB products. To begin addressing these concerns we compared previously published CRM AGB maps to new maps produced using identical methods with NSVB AGB reference data. Our results suggest that models relying on passive satellite imagery (e.g. Landsat) provide acceptable estimates of point-in-time NSVB AGB and carbon stocks, but fail to accurately quantify growth in mature closed-canopy forests. We highlight that existing estimates, models, and maps based on FIA reference data are no longer compatible with NSVB, and recommend new methods as well as updated models and maps for accommodating this step-change. Our collective ability to adopt NSVB in our modeling and mapping workflows will help us provide the most accurate spatial forest carbon data possible in order to better inform local management and decision making.

New products must be formulated rapidly to succeed in the global formulated product market; however, key product indicators (KPIs) can be complex, poorly understood functions of the chemical composition and processing history. Consequently, scale-up must currently undergo expensive trial-and-error campaigns. To accelerate process flow diagram (PFD) optimisation and knowledge discovery, this work proposed a novel digital framework to automatically quantify process mechanisms by integrating symbolic regression (SR) within model-based design of experiments (MBDoE). Each iteration, SR proposed a Pareto front of interpretable mechanistic expressions, and then MBDoE designed a new experiment to discriminate between them while balancing PFD optimisation. To investigate the framework's performance, a new process model capable of simulating general formulated product synthesis was constructed to generate in-silico data for different case studies. The framework could effectively discover ground-truth process mechanisms within a few iterations, indicating its great potential for use within the general chemical industry for digital manufacturing and product innovation.

Policymakers frequently analyze air quality and climate change in isolation, disregarding their interactions. This study explores the influence of specific climate factors on air quality by contrasting a regression model with K-Means Clustering, Hierarchical Clustering, and Random Forest techniques. We employ Physics-based Deep Learning (PBDL) and Long Short-Term Memory (LSTM) to examine the air pollution predictions. Our analysis utilizes ten years (2009-2018) of daily traffic, weather, and air pollution data from three major cities in Norway. Findings from feature selection reveal a correlation between rising heating degree days and heightened air pollution levels, suggesting increased heating activities in Norway are a contributing factor to worsening air quality. PBDL demonstrates superior accuracy in air pollution predictions compared to LSTM. This paper contributes to the growing literature on PBDL methods for more accurate air pollution predictions using environmental variables, aiding policymakers in formulating effective data-driven climate policies.

Although Federated Learning (FL) enables collaborative learning in Artificial Intelligence of Things (AIoT) design, it fails to work on low-memory AIoT devices due to its heavy memory usage. To address this problem, various federated pruning methods are proposed to reduce memory usage during inference. However, few of them can substantially mitigate the memory burdens during pruning and training. As an alternative, zeroth-order or backpropagation-free (BP-Free) methods can partially alleviate the memory consumption, but they suffer from scaling up and large computation overheads, since the gradient estimation error and floating point operations (FLOPs) increase as the dimensionality of the model parameters grows. In this paper, we propose a federated foresight pruning method based on Neural Tangent Kernel (NTK), which can seamlessly integrate with federated BP-Free training frameworks. We present an approximation to the computation of federated NTK by using the local NTK matrices. Moreover, we demonstrate that the data-free property of our method can substantially reduce the approximation error in extreme data heterogeneity scenarios. Since our approach improves the performance of the vanilla BP-Free method with fewer FLOPs and truly alleviates memory pressure during training and inference, it makes FL more friendly to low-memory devices. Comprehensive experimental results obtained from simulation- and real test-bed-based platforms show that our federated foresight-pruning method not only preserves the ability of the dense model with a memory reduction up to 9x but also boosts the performance of the vanilla BP-Free method with dramatically fewer FLOPs.

Large language models (LLMs) have significantly evolved, moving from simple output generation to complex reasoning and from stand-alone usage to being embedded into broader frameworks. In this paper, we introduce \emph{Fleet of Agents (FoA)}, a novel framework utilizing LLMs as agents to navigate through dynamic tree searches, employing a genetic-type particle filtering approach. FoA spawns a multitude of agents, each exploring autonomously, followed by a selection phase where resampling based on a heuristic value function optimizes the balance between exploration and exploitation. This mechanism enables dynamic branching, adapting the exploration strategy based on discovered solutions. We experimentally validate FoA using two benchmark tasks, "Game of 24" and "Mini-Crosswords". FoA outperforms the previously proposed Tree-of-Thoughts method in terms of efficacy and efficiency: it significantly decreases computational costs (by calling the value function less frequently) while preserving comparable or even superior accuracy.

We present a generative model that amortises computation for the field around e.g. gravitational or magnetic sources. Exact numerical calculation has either computational complexity $\mathcal{O}(M\times{}N)$ in the number of sources and field evaluation points, or requires a fixed evaluation grid to exploit fast Fourier transforms. Using an architecture where a hypernetwork produces an implicit representation of the field around a source collection, our model instead performs as $\mathcal{O}(M + N)$, achieves accuracy of $\sim\!4\%-6\%$, and allows evaluation at arbitrary locations for arbitrary numbers of sources, greatly increasing the speed of e.g. physics simulations. We also examine a model relating to the physical properties of the output field and develop two-dimensional examples to demonstrate its application. The code for these models and experiments is available at https://github.com/cmt-dtu-energy/hypermagnetics.

These authors contributed equally to this work. Abstract Foundation models have enormous potential in advancing Earth and climate sciences, however, current approaches may not be optimal as they focus on a few basic features of a desirable Earth and climate foundation model. Crafting the ideal Earth foundation model, we define eleven features which would allow such a foundation model to be beneficial for any geoscientific downstream application in an environmental-and human-centric manner. We further shed light on the way forward to achieve the ideal model and to evaluate Earth foundation models. What comes after foundation models? Energy efficient adaptation, adversarial defenses, and interpretability are among the emerging directions. In the past decade in particular, we have witnessed a paradigm shift from single-purpose models to general-purpose models, and from supervised pre-training to self-supervised pre-training. The majority of FMs like CLIP and GPT focus on the image and text domains. In this work, we specifically focus on "data" and "downstream tasks" relating to the Earth and its climate system, as shown in Figure 1. We choose to limit the scope of our work to the Earth's surface and atmosphere for three reasons. First, the Earth's surface and troposphere are our home, and include the majority of processes that directly impact and are impacted by human activity.