Mappō
PulseAugur coverage of Mappō — every cluster mentioning Mappō across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
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New research explores multi-agent RL, safe exploration, and efficient training methods · 10 sources tracked
Multiple research papers explore advancements in reinforcement learning (RL) and multi-agent systems. One paper introduces Octopus, a single-model framework for multi-agent RL that shares a backbone and coordinates over…
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New MM-RIS architecture enhances wireless communication with hybrid reinforcement learning
Researchers have developed a novel movable-element multi-functional reconfigurable intelligent surface (MM-RIS) architecture designed to enhance wireless communication flexibility and energy sustainability. This archite…
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New CALM system uses multi-agent RL for cooperative aerial manipulation
Researchers have developed CALM (Cooperative Aerial Learned Manipulation), a novel decentralized controller designed for multiple unmanned aerial vehicles (UAVs) to cooperatively manipulate a shared payload. This system…
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New RL methods refine credit assignment for LLM agents and multi-agent systems · 9 sources tracked
Multiple research papers introduce novel methods for improving credit assignment in reinforcement learning (RL) for agents and multi-agent systems. These approaches aim to more accurately attribute outcomes to specific …
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New AssemblyGrid v1 benchmark evaluates multi-robot production systems
Researchers have introduced AssemblyGrid v1, a new benchmark designed to evaluate multi-robot production systems. This benchmark addresses challenges in flexible robotic production, such as decentralized control, local …
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New FMAPPO approach enhances robot coordination and safety in factories
Researchers have developed a new multi-agent reinforcement learning approach called Feature-fusion Multi-Agent Proximal Policy Optimization (FMAPPO) for coordinating robots in industrial settings. This method integrates…
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New HMARL framework enhances wireless communication with reconfigurable surfaces
Researchers have developed a novel Hierarchical Multi-Agent Reinforcement Learning (HMARL) framework to manage reconfigurable intelligent surfaces (RIS) for enhanced wireless communication. This CSI-free approach bypass…
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New OGR-MARL framework enhances USV cooperative pursuit capabilities
Researchers have developed OGR-MARL, a novel framework for multi-agent reinforcement learning designed for cooperative pursuit scenarios involving heterogeneous unmanned surface vehicles (USVs) in constrained port water…
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Multi-agent RL enhances UAV deployment and communication in sparse networks
Two new research papers explore the application of multi-agent reinforcement learning for optimizing the deployment and communication of unmanned aerial vehicles (UAVs). The first paper introduces a framework for decent…
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New metrics analyze reward attribution effects in multi-agent RL
A new research paper introduces EffRank/$n$ and $D_ ext{act}$, two metrics designed to analyze how reward attribution affects learned representations in cooperative multi-agent reinforcement learning (MARL) systems. The…
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New analysis reveals optimal aggregation for multi-agent policy optimization
A new research paper introduces a canonical-form analysis for cooperative multi-agent policy optimization, focusing on how to aggregate information from neighboring agents. The study formalizes two key design choices, s…
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New PRIME framework recovers dormant neurons in multi-agent AI systems
Researchers have developed PRIME (Plasticity Recovery In Multi-agent Environments), a novel framework designed to address the issue of dormant neurons in multi-agent reinforcement learning systems, particularly in dynam…
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New MARL framework improves aerial target localization with delay awareness
Researchers have developed a new multi-agent reinforcement learning framework designed to improve the accuracy of 3D localization for aerial targets, particularly in Counter-UAS applications. This framework addresses th…
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New system Pharos enhances UAV safety in urban airspace
Researchers have developed Pharos, a novel multi-UAV airspace management system designed for complex urban environments. This system aims to enhance safety by preventing collisions and mitigating human fear, positioning…
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New DRL Framework Optimizes Urban EV Fleet Control
Researchers have developed a new framework for controlling urban electric vehicle (EV) fleets that uses distributionally robust reinforcement learning (DRL) to handle uncertain demand and travel times. This approach, ca…
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MARL benchmarks may not require complex reasoning, study finds
A new research paper published on arXiv questions the effectiveness of current benchmarks in cooperative multi-agent reinforcement learning (MARL). The study introduces diagnostic tools to assess whether agents truly em…
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Researchers develop semi-Markov RL for city-scale EV ride-hailing
Researchers have developed a novel semi-Markov reinforcement learning approach for optimizing city-scale electric vehicle (EV) ride-hailing fleets. This method addresses complex decisions like dispatch, repositioning, a…
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Researchers develop semi-Markov RL for EV ride-hailing, boosting profits and ensuring feasibility.
Researchers have developed a novel Semi-Markov Reinforcement Learning approach for managing large-scale electric vehicle ride-hailing fleets. This method ensures that dispatch, repositioning, and charging decisions stri…