1,310 research outputs found
Multi-Agent Reach-Avoid Games: Two Attackers Versus One Defender and Mixed Integer Programming
We propose a hybrid approach that combines Hamilton-Jacobi (HJ) reachability
and mixed-integer optimization for solving a reach-avoid game with multiple
attackers and defenders. The reach-avoid game is an important problem with
potential applications in air traffic control and multi-agent motion planning;
however, solving this game for many attackers and defenders is intractable due
to the adversarial nature of the agents and the high problem dimensionality. In
this paper, we first propose an HJ reachability-based method for solving the
reach-avoid game in which 2 attackers are playing against 1 defender; we derive
the numerically convergent optimal winning sets for the two sides in
environments with obstacles. Utilizing this result and previous results for the
1 vs. 1 game, we further propose solving the general multi-agent reach-avoid
game by determining the defender assignments that can maximize the number of
attackers captured via a Mixed Integer Program (MIP). Our method generalizes
previous state-of-the-art results and is especially useful when there are fewer
defenders than attackers. We validate our theoretical results in numerical
simulations
Coordinated Defense Allocation in Reach-Avoid Scenarios with Efficient Online Optimization
In this paper, we present a dual-layer online optimization strategy for
defender robots operating in multiplayer reach-avoid games within general
convex environments. Our goal is to intercept as many attacker robots as
possible without prior knowledge of their strategies. To balance optimality and
efficiency, our approach alternates between coordinating defender coalitions
against individual attackers and allocating coalitions to attackers based on
predicted single-attack coordination outcomes. We develop an online convex
programming technique for single-attack defense coordination, which not only
allows adaptability to joint states but also identifies the maximal region of
initial joint states that guarantees successful attack interception. Our
defense allocation algorithm utilizes a hierarchical iterative method to
approximate integer linear programs with a monotonicity constraint, reducing
computational burden while ensuring enhanced defense performance over time.
Extensive simulations conducted in 2D and 3D environments validate the efficacy
of our approach in comparison to state-of-the-art approaches, and show its
applicability in wheeled mobile robots and quadcopters
A Framework for Turn-Based Local Multiplayer Games
Mobile devices are present in people’s everyday lives and have gone from being a tool
used purely to communicate. Currently they are also used as a means to entertain, by
listening to music, watching videos or playing games. When it comes to games, these
can be played alone (single player games) or with other people (multiplayer games), from
strangers to family and friends. Local multiplayer games are a popular choice because
they connect groups of physically close people to play and allow them to interact.
However, there are some concerns to address. Local multiplayer games connect de vices but that alone isn’t enough to ensure correct game play. These games need to
distribute the game state between the devices and solve the issues that ensue from that.
These involve matching players, managing game state (making sure players get the cur rent state in a reasonable time frame, in order for the next moves to be performed), dealing
with player inflow and outflow, among other problems.
To reliably handle the aforementioned issues, in this thesis we propose Peppermint, a
framework and runtime system to program local multiplayer games on the mobile edge.
It was developed on top of Basil GardenBed, a data storage and dissemination system
for the mobile edge developed at NOVA LINCS, that provides communication between
devices. On the other hand, the challenges stemming from the games’ execution will be
addressed by our framework, which are validated by the development and evaluation of
one game according to a set of functional metrics.
The results obtained during testing of our framework, mostly in a simulated setting,
show that the framework is able to create and store matches, letting players join, leave
and play in them. It will also discard the generated data when the match ends, so that the
network doesn’t end up being cluttered with data that isn’t being accessed anymore. These
characteristics constitute a framework has a set of core features that can be expanded in
future work.Os dispositivos mĂłveis estĂŁo presentes no dia-a-dia das pessoas e deixaram de ser apenas
utilizados para comunicar. Presentemente sĂŁo tambĂ©m usados como meio de entreteni mento, ao permitirem ouvir mĂşsica, ver vĂdeos ou jogar jogos. Em relação a jogos, estes
podem ser apenas para um jogador, ou podem ser jogados por várias pessoas (jogos mul tijogador), desde desconhecidos a famĂlia e amigos. Os jogos multijogador locais sĂŁo uma
escolha popular porque permitem que grupos de pessoas prĂłximas fisicamente se juntem
e interajam.
No entanto, existem problemas a resolver. Os jogos multijogador locais conectam dis positivos mas apenas isso não é suficiente para garantir a sua correcção. Os jogos necessi tam de distribuir o seu estado entre os dispositivos e resolver as questões que decorrem
disso. Estas envolvem agrupar jogadores, gerir o estado do jogo (ao garantir que os joga dores recebem o estado mais recente atempadamente, para que os prĂłximos movimentos
possam ser efectuados), lidar com o fluxo de jogadores, entre outros problemas.
Para resolver os problemas mencionados, nesta tese apresentamos Peppermint, uma
infraestrutura e sistema de execução para implementar jogos multijogador locais em
dispositivos ligados a uma rede na mobile edge. Foi desenvolvido sobre o sistemaBasil
GardenBed, um sistema de armazenamento e disseminação de dados na mobile edge de senvolvido no NOVA LINCS, que fornece comunicação entre dispositivos. Por outro lado,
os desafios resultantes da execução dos jogos são endereçados pela nossa infraestrutura,
validados pelo desenvolvimento e avaliação de um jogo de acordo com um conjunto de
métricas relativas ao seu funcionamento.
Os resultados, predominantemente obtidos em ambiente simulado, mostram que a
infraestrutura permite criar e armazenar partidas, deixando outros jogadores entrar, sair e
jogar. Também elimina os dados criados quando estas terminam, para que a rede não fique
preenchida com dados que já não serão acedidos. Tudo isto forma uma infraestrutura com
um conjunto de caracterĂsticas básicas que podem ser expandidas em trabalho futuro
Deep Reinforcement Learning for Swarm Systems
Recently, deep reinforcement learning (RL) methods have been applied
successfully to multi-agent scenarios. Typically, these methods rely on a
concatenation of agent states to represent the information content required for
decentralized decision making. However, concatenation scales poorly to swarm
systems with a large number of homogeneous agents as it does not exploit the
fundamental properties inherent to these systems: (i) the agents in the swarm
are interchangeable and (ii) the exact number of agents in the swarm is
irrelevant. Therefore, we propose a new state representation for deep
multi-agent RL based on mean embeddings of distributions. We treat the agents
as samples of a distribution and use the empirical mean embedding as input for
a decentralized policy. We define different feature spaces of the mean
embedding using histograms, radial basis functions and a neural network learned
end-to-end. We evaluate the representation on two well known problems from the
swarm literature (rendezvous and pursuit evasion), in a globally and locally
observable setup. For the local setup we furthermore introduce simple
communication protocols. Of all approaches, the mean embedding representation
using neural network features enables the richest information exchange between
neighboring agents facilitating the development of more complex collective
strategies.Comment: 31 pages, 12 figures, version 3 (published in JMLR Volume 20
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