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Showing 1 to 20 of 58 for “"Marl"”.
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Paleontology and Stratigraphy of the Caloosahatchee Marl of Florida
<p>Considerable controversy has arisen over the naming of Pleistocene deposits in Kidder County, North Dakota. This controversy deals with the naming of drift sheets in the western two-thirds of the County. The eastern one-third and limited portions in the northern part of the County have …
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Achieving Robustness and Generalization in MARL for Sequential Social Dilemmas through Bilinear Value Networks
… for training multi-agent reinforcement learning (MARL) agents that are robust to different unforeseen gameplay strategies in sequential social dilemma (SSD) games. Recent literature has demonstrated that reward shaping can not only be used to enable MARL agents to discover diverse, …
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Jurassic limestone-marl sequences in northern spain: detecting diagenetic signals using rare earth and trace elements
Mesozoic rhythmically bedded limestone and marl sequences have been traditionally interpreted to be the result of eustatic sea-level changes related to Milankovitch cycles. An alternative interpretation supports the idea that these calcareous alternations are a product of diagenetic bedding. …
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Numerical analysis of test pile data from instrumented large diameter bored piles formed in keuper marl (mercia mudstone)
… bored, cast in-situ piles founded in Keuper marl (Mercia mudstone) is presented. The work is based on instrumented full-scale pile load tests carried out as part of the design of a major Highway communication project in Cardiff, U.K. This research also forms part of an on-going research …
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Diagenesis of the B chalk, B marl, and Fort Hays member of the Niobrara Formation, Denver Basin, Colorado
A diagenetic assessment of the B chalk, B marl and Fort Hays Member of the Niobrara Formation, in eight cores and one outcrop sample, revealed that progressive diagenetic alteration occurred with increasing burial depth and increasing temperatures. Cathodoluminescence petrography, scanning electron …
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Towards a unified multi-agent reinforcement learning framework
The field of Multi-Agent Reinforcement Learning (MARL) has rapidly evolved, yet integrating diverse tasks and algorithms into a cohesive system remains a complex challenge. This thesis proposes a unified framework aimed at improving adaptability, scalability, and cooperative dynamics among agents …
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Multi-Agent Reinforcement Learning for Autonomous Robotics
… of multi-agent reinforcement learning (MARL) to autonomous robotics by teaching a central controller to navigate multiple agents across various environments without collisions. The simulated scenarios range from simple, obstacle-free environments to complex environments with obstacles …
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The petrology of clay-rich beds in the Turonian (Upper Cretaceous) of the Anglo-Paris Basin
Clay-rich beds ('marl bands') in the Turonian of the Anglo-Paris Basin are primary features reflecting a sudden, sharp increases in non-carbonate deposition on the sea-floor. They differ from omission surfaces because they are not rhythmic, and usually only occur during eustatic falls in sea level. …
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Warm-Starting Networks for Sample-Efficient Continuous Adaptation to Parameter Perturbations in Multi-Agent Reinforcement Learning
… of RL is multi-agent reinforcement learning (MARL). Learning robust multi-agent policies in real-time strategy games, such as StarCraft II, is an important objective. In particular, being able to quickly adapt game playing agents to perturbations in rules and successfully displaying the …
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Learning to teach and meta-learning for sample-efficient multiagent reinforcement learning
… challenge in multiagent reinforcement learning (MARL). The difficulty is further complicated by other challenges, including the multiagent credit assignment, the high dimensionality of the problems, and the lack of convergence guarantees. As a result, many experiences are often required to learn …
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ML-Based Optimization of Large-Scale Systems: Case Study in Smart Microgrids and 5G RAN
… management. Multi-agent reinforcement learning (MARL) is presumed to be an ideal solution for MG energy management by considering each entity as an independent agent. We further investigate how communication failures will affect MG energy trading by using Bayesian deep reinforcement learning …
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Geologic and limnologic history of Glovers Pond, northwestern New Jersey
… of sediment. Basim1ard these are: (1) peat, (2) marl, (3) "transitional", calcareous, organic-rich silt, and (4) gyttja. Glovers Pond is thermally and chemically stratified. The hypolimnion is undersaturated with CaCO3; tl1is prohibits deposition of calcium carbonate in the profundal zone. Marl …
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Study of the organic content and mineralogy effects on the acoustic properties of the Niobrara Formation, Denver Basin
… gas and oil production from Niobrara chalk and marl intervals across the Greater Wattenberg Area (GWA). The Niobrara carbonate system in the Denver Basin contains a TOC range of 1-8 wt.%. Additionally, GWA is associated with a geothermal anomaly. This study investigates the effects of TOC, clay …
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Analysis of calcite-filled faults in carbonate strata, balcones trend near Waco, Texas.
… quarry ranges from relatively strong chalk to marly chalk to chalky marl to relatively weak marl. Higher clay content yields weaker rock. The dip angle of the normal faults varies with rock strength, with steeper dips across the stronger strata. Carbon stable isotopes in 174 samples indicate …
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Mitigating Generative Agent Social Dilemmas
… context of multi-agent reinforcement learning (MARL). However, with the rise of large language models (LLMs), a new design paradigm for AI systems has started to emerge—generative agents, in which actions performed by agents are chosen by prompting LLMs. This paradigm has seen recent success, …
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Addressing deep reinforcement learning: empirical algorithm performance evaluations∗
… across RL environments. As multi-agent RL (MARL) presents a recently popular research paradigm whose evaluation procedures have not yet been carefully scrutinised in the literature, we analyse a dataset by Gorsane et al. [1] which documents the evaluation methodologies of many recent deep …
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Reinforcement Learning for Cybersecurity Risk Assessment of Advanced Air Mobility Systems
… modeled in a Multi-Agent Reinforcement Learning (MARL) framework. Our threat model is particularly suited for quantifying the safety risks of nominally trained MARL algorithms under attacks by an adversary capable of compromising observational data of a single target agent. In contrast to prior …
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Logic-Targeted Data Poisoning and Cascading Failures in AI-Driven Distributed Traffic Control Systems
MARL (multi-agent reinforcement learning) is increasingly used to create intelligent traffc signal control systems for city-wide traffc management. This improves the movement of people and vehicles, but it also introduces new physical-layer safety risks at the sensor level that have not been …
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Fabric and Strength of Clays Stabilized with Lime
… for cylindrical specimens of a Devonian Red-Marl containing various amounts of lime (2-14 wt%), cured for up to 24 weeks at different temperatures (25, 50 and 75°C) and in different environments (sealed, unsealed, air, nitrogen and carbon dioxide).<br/><br/>Mineralogical analysis of Red-Marl …
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Effective Learning in Non-Stationary Multiagent Environments
Multiagent reinforcement learning (MARL) provides a principled framework for a group of artificial intelligence agents to learn collaborative and/or competitive behaviors at the level of human experts. Multiagent learning settings inherently solve much more complex problems than single-agent …
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