105 research outputs found
Contested Nationalism: The âIrish Questionâ in St. Johnâs, Newfoundland, and Halifax, Nova Scotia, 1919-1923
From 1919 until 1923, Irish nationalist networks flourished in St. Johnâs and Halifax. This article is a comparative study of responses to the Irish Question in the two cities, and it suggests that Irish identities did not evolve in isolation. Rather, their intensity and expression were profoundly influenced by the interaction of local, regional, national, and transnational ethnic networks. Although those of Irish descent who participated in the Self-Determination for Ireland League meetings, rallies, and lectures tended to be at least a generation removed from their ancestral homeland, they remained part of a transnational Irish diaspora until well into the 20th century.De 1919 jusquâen 1923, les rĂ©seaux nationalistes irlandais Ă©taient florissants Ă St. Johnâs et Ă Halifax. Cet article est une Ă©tude comparative de rĂ©ponses Ă la question irlandaise dans les deux villes et montre que les identitĂ©s irlandaises nâĂ©voluaient pas en vase clos. Leur intensitĂ© et leur expression Ă©taient plutĂŽt profondĂ©ment marquĂ©es par lâinteraction entre des rĂ©seaux ethniques locaux, rĂ©gionaux, nationaux et transnationaux. MĂȘme si les personnes dâorigine irlandaise qui participaient aux assemblĂ©es, aux rassemblements et aux confĂ©rences de la Self- Determination for Ireland League avaient gĂ©nĂ©ralement quittĂ© leur pays ancestral depuis au moins une gĂ©nĂ©ration, ils faisaient encore partie dâune diaspora irlandaise transnationale jusque tard dans le 20e siĂšcle
Parallel Reinforcement Learning for Traffic Signal Control
AbstractDeveloping Adaptive Traffic Signal Control strategies for efficient urban traffic management is a challenging problem, which is not easily solved. Reinforcement Learning (RL) has been shown to be a promising approach when applied to traffic signal control (TSC) problems. When using RL agents for TSC, difficulties may arise with respect to convergence times and performance. This is especially pronounced on complex intersections with many different phases, due to the increased size of the state action space. Parallel Learning is an emerging technique in RL literature, which allows several learning agents to pool their experiences while learning concurrently on the same problem. Here we present an extension to a leading published work on RL for TSC, which leverages the benefits of Parallel Learning to increase exploration and reduce delay times and queue lengths
Indoor positioning of shoppers using a network of bluetooth low energy beacons
In this paper we present our work on the indoor positioning of users (shoppers), using a network of Bluetooth Low Energy (BLE) beacons deployed in a large wholesale shopping store. Our objective is to accurately determine which product sections a user is adjacent to while traversing the store, using RSSI readings from multiple beacons, measured asynchronously on a standard commercial mobile device. We further wish to leverage the store layout (which imposes natural constraints on the movement of users) and the physical configuration of the beacon network, to produce a robust and efficient solution. We start by describing our application context and hardware configuration, and proceed to introduce our node-graph model of user location. We then describe our experimental work which begins with an investigation of signal characteristics along and across aisles. We propose three methods of localization, using a ânearest-beaconâ approach as a base-line; exponentially averaged weighted range estimates; and a particle-filter method based on the RSSI attenuation model and Gaussian-noise. Our results demonstrate that the particle filter method significantly out-performs the others. Scalability also makes this method ideal for applications run on mobile devices with more limited computational capabilitie
Multi-Agent Credit Assignment in Stochastic Resource Management Games
Multi-Agent Systems (MAS) are a form of distributed intelligence, where multiple autonomous agents act in a common environment. Numerous complex, real world systems have been successfully optimised using Multi-Agent Reinforcement Learning (MARL) in conjunction with the MAS framework. In MARL agents learn by maximising a scalar reward signal from the environment, and thus the design of the reward function directly affects the policies learned. In this work, we address the issue of appropriate multi-agent credit assignment in stochastic resource management games. We propose two new Stochastic Games to serve as testbeds for MARL research into resource management problems: the Tragic Commons Domain and the Shepherd Problem Domain. Our empirical work evaluates the performance of two commonly used reward shaping techniques: Potential-Based Reward Shaping and difference rewards. Experimental results demonstrate that systems using appropriate reward shaping techniques for multi-agent credit assignment can achieve near optimal performance in stochastic resource management games, outperforming systems learning using unshaped local or global evaluations. We also present the first empirical investigations into the effect of expressing the same heuristic knowledge in state- or action-based formats, therefore developing insights into the design of multi-agent potential functions that will inform future work
Opponent Learning Awareness and Modelling in Multi-Objective Normal Form Games
Many real-world multi-agent interactions consider multiple distinct criteria,
i.e. the payoffs are multi-objective in nature. However, the same
multi-objective payoff vector may lead to different utilities for each
participant. Therefore, it is essential for an agent to learn about the
behaviour of other agents in the system. In this work, we present the first
study of the effects of such opponent modelling on multi-objective multi-agent
interactions with non-linear utilities. Specifically, we consider two-player
multi-objective normal form games with non-linear utility functions under the
scalarised expected returns optimisation criterion. We contribute novel
actor-critic and policy gradient formulations to allow reinforcement learning
of mixed strategies in this setting, along with extensions that incorporate
opponent policy reconstruction and learning with opponent learning awareness
(i.e., learning while considering the impact of one's policy when anticipating
the opponent's learning step). Empirical results in five different MONFGs
demonstrate that opponent learning awareness and modelling can drastically
alter the learning dynamics in this setting. When equilibria are present,
opponent modelling can confer significant benefits on agents that implement it.
When there are no Nash equilibria, opponent learning awareness and modelling
allows agents to still converge to meaningful solutions that approximate
equilibria.Comment: Under review since 14 November 202
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