CODA augments offline multi-agent RL with on-policy diffusion trajectories that evolve with the joint policy to enable coordination.
org/CorpusID:248965046
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
CNeVA combines variational behavior latents with rectified-flow generators and soft eligibility to deliver controllable yet realistic traffic simulation on Waymo data.
citing papers explorer
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CODA: Coordination via On-Policy Diffusion for Multi-Agent Offline Reinforcement Learning
CODA augments offline multi-agent RL with on-policy diffusion trajectories that evolve with the joint policy to enable coordination.
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Controllable Sim Agents with Behavior Latents
CNeVA combines variational behavior latents with rectified-flow generators and soft eligibility to deliver controllable yet realistic traffic simulation on Waymo data.