A multi-agent RL framework for unlabeled multi-robot planning that uses velocity obstacle projections to guarantee collision-free trajectories applicable to arbitrary robot models.
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2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2019 2verdicts
UNVERDICTED 2representative citing papers
Supervised and reinforcement learning predict LTE control information to enable more device sleep states, with reported energy savings up to 17%.
citing papers explorer
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Learning Safe Unlabeled Multi-Robot Planning with Motion Constraints
A multi-agent RL framework for unlabeled multi-robot planning that uses velocity obstacle projections to guarantee collision-free trajectories applicable to arbitrary robot models.
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Adaptive Predictive Power Management for Mobile LTE Devices
Supervised and reinforcement learning predict LTE control information to enable more device sleep states, with reported energy savings up to 17%.