1. The natural hydropattern in the seasonally-flooded marl prairie wetlands of Everglades National Park has been severely disrupted by human water control activities, seriously impacting higher trophic organisms, e.g. wading birds, that depend on these wetlands. Less is known about the impacis on key aquatic fauna, such as crayfish Procambarus alleni, or how these populations might respond to p...

In many multi-agent systems, the interactions between agents are sparse and exploiting interaction sparseness in multiagent reinforcement learning (MARL) can improve the learning performance. Also, agents may have already learnt some single-agent knowledge (e.g., local value function) before the multi-agent learning process. In this work, we investigate how such knowledge can be utilized to lea...

Pedestrian simulation is complex because there are different levels of behavior modeling. At the lowest level, local interactions between agents occur; at the middle level, strategic and tactical behaviors appear like overtakings or route choices; and at the highest level path-planning is necessary. The agent-based pedestrian simulators either focus on a specific level (mainly in the lower one)...

Here we present multi-proxy data from two cores taken from Hundred Mile Lake in the Matanuska Valley of south-central Alaska to investigate the climate, vegetation and deglaciation history of the last 14,000 years. The chronology of the cores was controlled by five AMS dates. Sediment lithology changes from clay at 14–13 ka (1 ka 1⁄4 1000 cal BP), through marl at 13–8 ka, to gyttja at 8–0 ka. T...

MDP Reward Shaping for Multi-Agent Reinforcement Learning Kyriakos Efthymiadis, Sam Devlin and Daniel Kudenko Department of Computer Science, The University of York, UK Abstract. Reward shaping has been shown to significantly improve an agent’s performance in reinforcement learning. As attention is shifting from tabula-rasa approaches to methods where some heuristic domain knowledge can be give...

The purpose of this paper aims for a geotechnical analysis based on experimental physical and mechanical characteristics of Miocene marl situated at Medea region in Algeria. More than 150 soil samples were taken in the investigation part of the North-South Highway which extends over than 53 km from Chiffa in the North to Berrouaghia in the South of Algeria. The analysis of data in terms of Atte...

This paper proposed a distributed Multi-Agent Reinforcement Learning (MARL) algorithm for a team of Unmanned Aerial Vehicles (UAVs) that can learn to cooperate to provide a full coverage of an unknown field of interest while minimizing the overlapping sections among their field of views. Two challenges in MARL for such a system are discussed in the paper: firstly, the complex dynamic of the joi...