6,789 research outputs found
TaxAI: A Dynamic Economic Simulator and Benchmark for Multi-Agent Reinforcement Learning
Taxation and government spending are crucial tools for governments to promote
economic growth and maintain social equity. However, the difficulty in
accurately predicting the dynamic strategies of diverse self-interested
households presents a challenge for governments to implement effective tax
policies. Given its proficiency in modeling other agents in partially
observable environments and adaptively learning to find optimal policies,
Multi-Agent Reinforcement Learning (MARL) is highly suitable for solving
dynamic games between the government and numerous households. Although MARL
shows more potential than traditional methods such as the genetic algorithm and
dynamic programming, there is a lack of large-scale multi-agent reinforcement
learning economic simulators. Therefore, we propose a MARL environment, named
\textbf{TaxAI}, for dynamic games involving households, government, firms,
and financial intermediaries based on the Bewley-Aiyagari economic model. Our
study benchmarks 2 traditional economic methods with 7 MARL methods on TaxAI,
demonstrating the effectiveness and superiority of MARL algorithms. Moreover,
TaxAI's scalability in simulating dynamic interactions between the government
and 10,000 households, coupled with real-data calibration, grants it a
substantial improvement in scale and reality over existing simulators.
Therefore, TaxAI is the most realistic economic simulator, which aims to
generate feasible recommendations for governments and individuals.Comment: 26 pages, 8 figures, 12 table
Self-Organized Coverage and Capacity Optimization for Cellular Mobile Networks
ï»żDie zur ErfĂŒllung der zu erwartenden Steigerungen ĂŒbertragener
Datenmengen notwendige gröĂere HeterogenitĂ€t und steigende Anzahl von
Zellen werden in der Zukunft zu einer deutlich höheren KomplexitÀt bei
Planung und Optimierung von Funknetzen fĂŒhren. ZusĂ€tzlich erfordern
rĂ€umliche und zeitliche Ănderungen der Lastverteilung eine dynamische
Anpassung von Funkabdeckung und -kapazitÀt
(Coverage-Capacity-Optimization, CCO). Aktuelle Planungs- und
Optimierungsverfahren sind hochgradig von menschlichem Einfluss abhÀngig,
was sie zeitaufwÀndig und teuer macht. Aus diesen Grnden treffen AnsÀtze
zur besseren Automatisierung des Netzwerkmanagements sowohl in der
Industrie, als auch der Forschung auf groes
Interesse.Selbstorganisationstechniken (SO) haben das Potential, viele der
aktuell durch Menschen gesteuerten AblÀufe zu automatisieren. Ihnen wird
daher eine zentrale Rolle bei der Realisierung eines einfachen und
effizienten Netzwerkmanagements zugeschrieben. Die vorliegende Arbeit
befasst sich mit selbstorganisierter Optimierung von Abdeckung und
ĂbertragungskapazitĂ€t in Funkzellennetzwerken. Der Parameter der Wahl
hierfĂŒr ist die Antennenneigung. Die zahlreichen vorhandenen AnsĂ€tze
hierfĂŒr befassen sich mit dem Einsatz heuristischer Algorithmen in der
Netzwerkplanung. Im Gegensatz dazu betrachtet diese Arbeit den verteilten
Einsatz entsprechender Optimierungsverfahren in den betreffenden
Netzwerkknoten. Durch diesen Ansatz können zentrale Fehlerquellen (Single
Point of Failure) und Skalierbarkeitsprobleme in den kommenden heterogenen
Netzwerken mit hoher Knotendichte vermieden werden.Diese Arbeit stellt
einen "Fuzzy Q-Learning (FQL)"-basierten Ansatz vor, ein einfaches
Maschinenlernverfahren mit einer effektiven Abstraktion kontinuierlicher
Eingabeparameter. Das CCO-Problem wird als Multi-Agenten-Lernproblem
modelliert, in dem jede Zelle versucht, ihre optimale Handlungsstrategie
(d.h. die optimale Anpassung der Antennenneigung) zu lernen. Die
entstehende Dynamik der Interaktion mehrerer Agenten macht die
Fragestellung interessant. Die Arbeit betrachtet verschiedene Aspekte des
Problems, wie beispielsweise den Unterschied zwischen egoistischen und
kooperativen Lernverfahren, verteiltem und zentralisiertem Lernen, sowie
die Auswirkungen einer gleichzeitigen Modifikation der Antennenneigung auf
verschiedenen Knoten und deren Effekt auf die Lerneffizienz.Die
LeistungsfÀhigkeit der betrachteten Verfahren wird mittels eine
LTE-Systemsimulators evaluiert. Dabei werden sowohl gleichmĂ€Ăig verteilte
Zellen, als auch Zellen ungleicher GröĂe betrachtet. Die entwickelten
AnsÀtze werden mit bekannten Lösungen aus der Literatur verglichen. Die
Ergebnisse zeigen, dass die vorgeschlagenen Lösungen effektiv auf
Ănderungen im Netzwerk und der Umgebung reagieren können. Zellen stellen
sich selbsttÀtig schnell auf AusfÀlle und Inbetriebnahmen benachbarter
Systeme ein und passen ihre Antennenneigung geeignet an um die
Gesamtleistung des Netzes zu verbessern. Die vorgestellten Lernverfahren
erreichen eine bis zu 30 Prozent verbesserte Leistung als bereits bekannte
AnsĂ€tze. Die Verbesserungen steigen mit der NetzwerkgröĂe.The challenging task of cellular network planning and optimization will
become more and more complex because of the expected heterogeneity and
enormous number of cells required to meet the traffic demands of coming
years. Moreover, the spatio-temporal variations in the traffic patterns of
cellular networks require their coverage and capacity to be adapted
dynamically. The current network planning and optimization procedures are
highly manual, which makes them very time consuming and resource
inefficient. For these reasons, there is a strong interest in industry and
academics alike to enhance the degree of automation in network management.
Especially, the idea of Self-Organization (SO) is seen as the key to
simplified and efficient cellular network management by automating most of
the current manual procedures. In this thesis, we study the self-organized
coverage and capacity optimization of cellular mobile networks using
antenna tilt adaptations. Although, this problem is widely studied in
literature but most of the present work focuses on heuristic algorithms for
network planning tool automation. In our study we want to minimize this
reliance on these centralized tools and empower the network elements for
their own optimization. This way we can avoid the single point of failure
and scalability issues in the emerging heterogeneous and densely deployed
networks.In this thesis, we focus on Fuzzy Q-Learning (FQL), a machine
learning technique that provides a simple learning mechanism and an
effective abstraction level for continuous domain variables. We model the
coverage-capacity optimization as a multi-agent learning problem where each
cell is trying to learn its optimal action policy i.e. the antenna tilt
adjustments. The network dynamics and the behavior of multiple learning
agents makes it a highly interesting problem. We look into different
aspects of this problem like the effect of selfish learning vs. cooperative
learning, distributed vs. centralized learning as well as the effect of
simultaneous parallel antenna tilt adaptations by multiple agents and its
effect on the learning efficiency.We evaluate the performance of the
proposed learning schemes using a system level LTE simulator. We test our
schemes in regular hexagonal cell deployment as well as in irregular cell
deployment. We also compare our results to a relevant learning scheme from
literature. The results show that the proposed learning schemes can
effectively respond to the network and environmental dynamics in an
autonomous way. The cells can quickly respond to the cell outages and
deployments and can re-adjust their antenna tilts to improve the overall
network performance. Additionally the proposed learning schemes can achieve
up to 30 percent better performance than the available scheme from
literature and these gains increases with the increasing network size
Biomechanical Locomotion Heterogeneity in Synthetic Crowds
Synthetic crowd simulation combines rule sets at different conceptual layers to represent the dynamic nature of crowds while adhering to basic principles of human steering, such as collision avoidance and goal completion. In this dissertation, I explore synthetic crowd simulation at the steering layer using a critical approach to define the central theme of the work, the impact of model representation and agent diversity in crowds. At the steering layer, simulated agents make regular decisions, or actions, related to steering which are often responsible for the emergent behaviours found in the macro-scale crowd. Because of this bottom-up impact of a steering model's defining rule-set, I postulate that biomechanics and diverse biomechanics may alter the outcomes of dynamic synthetic-crowds-based outcomes. This would mean that an assumption of normativity and/or homogeneity among simulated agents and their mobility would provide an inaccurate representation of a scenario. If these results are then used to make real world decisions, say via policy or design, then those populations not represented in the simulated scenario may experience a lack of representation in the actualization of those decisions.
A focused literature review shows that applications of both biomechanics and diverse locomotion representation at this layer of modelling are very narrow and often not present. I respond to the narrowness of this representation by addressing both biomechanics and heterogeneity separately. To address the question of performance and importance of locomotion biomechanics in crowd simulation, I use a large scale comparative approach. The industry standard synthetic crowd models are tested under a battery of benchmarks derived from prior work in comparative analysis of synthetic crowds as well as new scenarios derived from built environments. To address the question of the importance of heterogeneity in locomotion biomechanics, I define tiers of impact in the multi-agent crowds model at the steering layer--from the action space, to the agent space, to the crowds space. To this end, additional models and layers are developed to address the modelling and application of heterogeneous locomotion biomechanics in synthetic crowds. The results of both studies form a research arc which shows that the biomechanics in steering models provides important fidelity in several applications and that heterogeneity in the model of locomotion biomechanics directly impacts both qualitative and quantitative synthetic crowds outcomes. As well, systems, approaches, and pitfalls regarding the analysis of steering model and human mobility diversity are described
How to Identify Investor's types in real financial markets by means of agent based simulation
The paper proposes a computational adaptation of the principles underlying
principal component analysis with agent based simulation in order to produce a
novel modeling methodology for financial time series and financial markets.
Goal of the proposed methodology is to find a reduced set of investor s models
(agents) which is able to approximate or explain a target financial time
series. As computational testbed for the study, we choose the learning system L
FABS which combines simulated annealing with agent based simulation for
approximating financial time series. We will also comment on how L FABS s
architecture could exploit parallel computation to scale when dealing with
massive agent simulations. Two experimental case studies showing the efficacy
of the proposed methodology are reported.Comment: 18 pages, in pres
Adaptive-Aggressive Traders Don't Dominate
For more than a decade Vytelingum's Adaptive-Aggressive (AA) algorithm has
been recognized as the best-performing automated auction-market trading-agent
strategy currently known in the AI/Agents literature; in this paper, we
demonstrate that it is in fact routinely outperformed by another algorithm when
exhaustively tested across a sufficiently wide range of market scenarios. The
novel step taken here is to use large-scale compute facilities to brute-force
exhaustively evaluate AA in a variety of market environments based on those
used for testing it in the original publications. Our results show that even in
these simple environments AA is consistently out-performed by IBM's GDX
algorithm, first published in 2002. We summarize here results from more than
one million market simulation experiments, orders of magnitude more testing
than was reported in the original publications that first introduced AA. A 2019
ICAART paper by Cliff claimed that AA's failings were revealed by testing it in
more realistic experiments, with conditions closer to those found in real
financial markets, but here we demonstrate that even in the simple experiment
conditions that were used in the original AA papers, exhaustive testing shows
AA to be outperformed by GDX. We close this paper with a discussion of the
methodological implications of our work: any results from previous papers where
any one trading algorithm is claimed to be superior to others on the basis of
only a few thousand trials are probably best treated with some suspicion now.
The rise of cloud computing means that the compute-power necessary to subject
trading algorithms to millions of trials over a wide range of conditions is
readily available at reasonable cost: we should make use of this; exhaustive
testing such as is shown here should be the norm in future evaluations and
comparisons of new trading algorithms.Comment: To be published as a chapter in "Agents and Artificial Intelligence"
edited by Jaap van den Herik, Ana Paula Rocha, and Luc Steels; forthcoming
2019/2020. 24 Pages, 1 Figure, 7 Table
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