ORION introduces an option-critic RL method with shared graph encoding and dual-stage cooperation for decentralized multi-agent navigation and exploration in partially known environments, scaling to 10 robots with real-world validation.
The option-critic architecture
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
A hierarchical DRL method (TBH-DDPG) optimizes UAV trajectories at coarse granularity and bandwidth allocation at fine granularity, reporting 44.44% faster convergence and 58.05% lower computational cost than a non-hierarchical baseline in simulations.
citing papers explorer
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ORION: Option-Regularized Deep Reinforcement Learning for Cooperative Multi-Agent Online Navigation
ORION introduces an option-critic RL method with shared graph encoding and dual-stage cooperation for decentralized multi-agent navigation and exploration in partially known environments, scaling to 10 robots with real-world validation.
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UAV Trajectory and Bandwidth Allocation for Efficient Data Collection in Low-Altitude Intelligent IoT: A Hierarchical DRL Approach
A hierarchical DRL method (TBH-DDPG) optimizes UAV trajectories at coarse granularity and bandwidth allocation at fine granularity, reporting 44.44% faster convergence and 58.05% lower computational cost than a non-hierarchical baseline in simulations.