1,310 research outputs found

    Multi-Agent Reach-Avoid Games: Two Attackers Versus One Defender and Mixed Integer Programming

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    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

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    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

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    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 móveis estão presentes no dia-a-dia das pessoas e deixaram de ser apenas utilizados para comunicar. Presentemente são também usados como meio de entreteni mento, ao permitirem ouvir música, ver vídeos 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 família e amigos. Os jogos multijogador locais são uma escolha popular porque permitem que grupos de pessoas próximas 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 próximos 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 características básicas que podem ser expandidas em trabalho futuro

    Deep Reinforcement Learning for Swarm Systems

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    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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