Abstract Connected autonomous vehicles (CAVs) can exploit information received from other in addition to their sensor make decisions. For this reason, deployment is expected improve traffic safety and efficiency. Safe lane-changing a significant challenge for CAVs, particularly mixed traffic, i.e. with human-driven (HDVs) on the road, as set of around them varies very quickly, they only communi...

The lake and bog deposits of Cedar Creek Forest, Isanti and Anoka counties, Minnesota, consist of a multiple-facies accumulation of marl, peat, copropel, and sapropel. Chromatographic analyses of the lipoid extracts of samples of the sediments show measurable amounts of saturated and aromatic hydrocarbons as well as large proportions of asphalts and polar lipoid compounds. In general compositio...

The Diaprepes root weevil Diaprepes abbreviatus (L.) is a major pest of citrus, ornamentals, and vegetables in Florida and the Caribbean. Entomopathogenic nematodes can provide substantial control of the root feeding larvae, but their efÞcacy can be affected by soil type. Our objective was to determine the effects of three soil types on the control of D. abbreviatus with Steinernema riobrave (C...

This paper reports the method and mechanism for improving the strength of marl and desert sand utilizing electric arc furnace dust (EAFD), an industrial by-product, in lieu of cement or lime. EAFD was used in conjunction with a small quantity (2%) of cement. The mechanical properties and durability characteristics of marl and sand mixed with 2% cement plus 5-, 10-, 20- or 30%-EAFD, by weight of...

With the advent of sequential matching (of supply and demand) systems (uber, Lyft, Grab for taxis; ubereats, deliveroo, etc for food; amazon prime, lazada etc. for groceries) across many online and offline services, individuals (taxi drivers, delivery boys, delivery van drivers, etc.) earn more by being at the ”right” place at the ”right” time. We focus on learning techniques for providing guid...

Coordinated multi-agent reinforcement learning (MARL) provides a promising approach to scaling learning in large cooperative multiagent systems. Distributed constraint optimization (DCOP) techniques have been used to coordinate action selection among agents during both the learning phase and the policy execution phase (if learning is off-line) to ensure good overall system performance. However,...

We consider the problem of fully decentralized multi-agent reinforcement learning (MARL), where the agents are located at the nodes of a time-varying communication network. Specifically, we assume that the reward functions of the agents might correspond to different tasks, and are only known to the corresponding agent. Moreover, each agent makes individual decisions based on both the informatio...

The majority of Multi-Agent Reinforcement Learning (MARL) implementations aim to optimise systems with respect to a single objective, despite the fact that many real world problems are inherently multi-objective in nature. Research into multi-objective MARL is still in its infancy, and few studies to date have dealt with the issue of credit assignment. Reward shaping has been proposed as a mean...

Recent research has demonstrated that considering local interactions among agents in specific parts of the state space, is a successful way of simplifying the multi-agent learning process. By taking into account other agents only when a conflict is possible, an agent can significantly reduce the state-action space in which it learns. Current approaches, however, consider only the immediate rewa...