We propose Learning Off-Policy with Online Planning (LOOP), combining the techniques from model-based and model-free reinforcement learning algorithms. The agent learns a model of the environment, and then uses trajectory optimization with the learned model to select actions. To sidestep the myopic effect of fixed horizon trajectory optimization, a value function is attached to the end of the planning horizon. This value function is learned through off-policy reinforcement learning, using trajectory optimization as its behavior policy. Furthermore, we introduce "actor-guided" trajectory optimization to mitigate the actor-divergence issue in the proposed method. We benchmark our methods on continuous control tasks and demonstrate that it offers a significant improvement over the underlying model-based and model-free algorithms.

The theory-guided neural network (TgNN) is a kind of method which improves the effectiveness and efficiency of neural network architectures by incorporating scientific knowledge or physical information. Despite its great success, the theory-guided (deep) neural network possesses certain limits when maintaining a tradeoff between training data and domain knowledge during the training process. In this paper, the Lagrangian dual-based TgNN (TgNN-LD) is proposed to improve the effectiveness of TgNN. We convert the original loss function into a constrained form with fewer items, in which partial differential equations (PDEs), engineering controls (ECs), and expert knowledge (EK) are regarded as constraints, with one Lagrangian variable per constraint. These Lagrangian variables are incorporated to achieve an equitable tradeoff between observation data and corresponding constraints, in order to improve prediction accuracy, and conserve time and computational resources adjusted by an ad-hoc procedure. To investigate the performance of the proposed method, the original TgNN model with a set of optimized weight values adjusted by ad-hoc procedures is compared on a subsurface flow problem, with their L2 error, R square (R2), and computational time being analyzed. Experimental results demonstrate the superiority of the Lagrangian dual-based TgNN.

It was the Australian bush fire that finally did it. For 12 years Adam Hearne had worked at companies that represented some of the world's largest sources of greenhouse gas emissions. First at Rio Tinto, one of the largest industrial miners, and then at Amazon, where he handled inbound delivery operations across the EU, Hearne was involved in ensuring that things flowed smoothly for companies whose operations spew millions of tons of carbon dioxide into the environment. Amazon's business alone was responsible for emitting 51.17 million metric tons of carbon dioxide last year -- the equivalent of 13 coal-burning power plants, according to a report from the company. Then, Hearne's home country burned.

Over the past few years, sustainability has gradually become a greater and more important part of each of our lives. As human beings, we strive to sustain many things; our relationships with others, local and global economies, our livelihoods and the world we live in. We see the act of sustainable living encouraged and documented everywhere – think'Reduce, Re-use, Recycle.' It's becoming increasingly apparent that every individual in every organization can have a unique and significant impact in creating a more sustainable future. If humans can have such a measurable impact, what about AI? Our minds usually jump to the environment when we talk about sustainability, but its meaning has changed over the years as global priorities have shifted. It wasn't until the 20th century that it began to represent what we now recognize as the accepted definition: creating lasting and efficient methods through which to live our lives in order to maintain our world for future generations.

How do you summarise India's journey so far in nearly three quarters of a century of Independence and where do we stand globally? After being freed from the shackles of colonialism, India has established itself as a leader at the global stage. However first, India had to play catch-up. We have gone from a food deficit nation to a food surplus one. Our service sector has emerged as a global powerhouse.

Energy investors in search of resilience are rediscovering the power of the Internet of Things (IoT) and machine learning to guide data-driven decisions in the face of an increasingly volatile economic environment. Much has happened since I first wrote about the cloud-based IoT platform from Kaiserwetter Energy Asset Management called ARISTOTELES. "We're seeing strong interest from investors who want to stay ahead of ongoing shocks related to the COVID-19 pandemic, as well as all the other variables that impact the energy industry," said Hanno Schoklitsch, chief executive officer (CEO) and founder at Kaiserwetter Energy Asset Management. "Our customers have been super astonished when they've seen how they could use applied data intelligence on a daily basis to benchmark the performance of the assets they have invested in and predict production outcomes of renewable energy facilities throughout the world at any given time." It is built on SAP Cloud Platform and uses a number of SAP solutions, including SAP Internet of Things and SAP Analytics Cloud.

Dynamic resource management has become one of the major areas of research in modern computer and communication system design due to lower power consumption and higher performance demands. The number of integrated cores, level of heterogeneity and amount of control knobs increase steadily. As a result, the system complexity is increasing faster than our ability to optimize and dynamically manage the resources. Moreover, offline approaches are sub-optimal due to workload variations and large volume of new applications unknown at design time. This paper first reviews recent online learning techniques for predicting system performance, power, and temperature. Then, we describe the use of predictive models for online control using two modern approaches: imitation learning (IL) and an explicit nonlinear model predictive control (NMPC). Evaluations on a commercial mobile platform with 16 benchmarks show that the IL approach successfully adapts the control policy to unknown applications. The explicit NMPC provides 25% energy savings compared to a state-of-the-art algorithm for multi-variable power management of modern GPU sub-systems.

The closed feedback loop in recommender systems is a common setting that can lead to different types of biases. Several studies have dealt with these biases by designing methods to mitigate their effect on the recommendations. However, most existing studies do not consider the iterative behavior of the system where the closed feedback loop plays a crucial role in incorporating different biases into several parts of the recommendation steps. We present a theoretical framework to model the asymptotic evolution of the different components of a recommender system operating within a feedback loop setting, and derive theoretical bounds and convergence properties on quantifiable measures of the user discovery and blind spots. We also validate our theoretical findings empirically using a real-life dataset and empirically test the efficiency of a basic exploration strategy within our theoretical framework. Our findings lay the theoretical basis for quantifying the effect of feedback loops and for designing Artificial Intelligence and machine learning algorithms that explicitly incorporate the iterative nature of feedback loops in the machine learning and recommendation process.

From a numerical perspective, resolving the wide range of spatiotemporal scales within such physical systems is challenging since extremely small spatial and temporal numerical We propose MeshfreeFlowNet, a novel deep learningbased stencils would be required. In order to alleviate the super-resolution framework to generate continuous computational burden of fully resolving such a wide range (grid-free) spatiotemporal solutions from the low-resolution of spatial and temporal scales, multiscale computational approaches inputs. While being computationally efficient, MeshfreeFlowNet have been developed. For instance, in the subsurface accurately recovers the fine-scale quantities flow problem, the main idea of the multiscale approach of interest. MeshfreeFlowNet allows for: (i) the output is to build a set of operators that map between the unknowns to be sampled at all spatiotemporal resolutions, (ii) a set associated with the computational cells in a fine-grid and the of Partial Differential Equation (PDE) constraints to be imposed, unknowns on a coarser grid. The operators are computed and (iii) training on fixed-size inputs on arbitrarily numerically by solving localized flow problems. The multiscale sized spatiotemporal domains owing to its fully convolutional basis functions have subgrid-scale resolutions, ensuring encoder.

Swarm Intelligence-based optimization techniques combine systematic exploration of the search space with information available from neighbors and rely strongly on communication among agents. These algorithms are typically employed to solve problems where the function landscape is not adequately known and there are multiple local optima that could result in premature convergence for other algorithms. Applications of such algorithms can be found in communication systems involving design of networks for efficient information dissemination to a target group, targeted drug-delivery where drug molecules search for the affected site before diffusing, and high-value target localization with a network of drones. In several of such applications, the agents face a hostile environment that can result in loss of agents during the search. Such a loss changes the communication topology of the agents and hence the information available to agents, ultimately influencing the performance of the algorithm. In this paper, we present a study of the impact of loss of agents on the performance of such algorithms as a function of the initial network configuration. We use particle swarm optimization to optimize an objective function with multiple sub-optimal regions in a hostile environment and study its performance for a range of network topologies with loss of agents. The results reveal interesting trade-offs between efficiency, robustness, and performance for different topologies that are subsequently leveraged to discover general properties of networks that maximize performance. Moreover, networks with small-world properties are seen to maximize performance under hostile conditions.