CRONA: Multi-Agent Reinforcement Learning for Cross-Modal Navigation
Researchers have introduced CRONA, a novel Multi-Agent Reinforcement Learning (MARL) framework designed to enhance robust embodied navigation through cross-modal collaboration. Addressing the challenges of obtaining high-quality, aligned multi-modal data and the complexity of training monolithic models, CRONA employs lightweight, modality-specialized agents that collaborate effectively. This approach allows for flexible deployment and parallel execution while preserving the unique strengths of each sensory modality. The framework improves collaboration by leveraging control-relevant auxiliary beliefs and a centralized multi-modal critic with global state information. Experimental results on visual-acoustic navigation tasks demonstrate that CRONA significantly outperforms single-agent baselines in both performance and efficiency. The study reveals that homogeneous collaboration suffices for short-range navigation with salient cues, whereas heterogeneous collaboration among agents with complementary modalities is more effective for complex scenarios. Furthermore, navigation in large, intricate environments necessitates richer multi-modal perception and increased model capacity. This research offers a scalable paradigm for robotics and artificial intelligence, highlighting the potential of multi-agent systems in overcoming the limitations of traditional single-model approaches in processing diverse sensory inputs for autonomous navigation tasks.
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CRONA: Multi-Agent Reinforcement Learning for Cross-Modal Navigation
Researchers have introduced CRONA, a novel Multi-Agent Reinforcement Learning (MARL) framework designed to enhance robust embodied navigation through cross-modal collaboration. Addressing the challenges of obtaining high-quality, aligned multi-modal data and the complexity of training monolithic models, CRONA employs lightweight, modality-specialized agents that collaborate effectively. This approach allows for flexible deployment and parallel execution while preserving the unique strengths of each sensory modality. The framework improves collaboration by leveraging control-relevant auxiliary beliefs and a centralized multi-modal critic with global state information. Experimental results on visual-acoustic navigation tasks demonstrate that CRONA significantly outperforms single-agent baselines in both performance and efficiency. The study reveals that homogeneous collaboration suffices for short-range navigation with salient cues, whereas heterogeneous collaboration among agents with complementary modalities is more effective for complex scenarios. Furthermore, navigation in large, intricate environments necessitates richer multi-modal perception and increased model capacity. This research offers a scalable paradigm for robotics and artificial intelligence, highlighting the potential of multi-agent systems in overcoming the limitations of traditional single-model approaches in processing diverse sensory inputs for autonomous navigation tasks.
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