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About this paper
Runhua Zhang, Yingjie Niu, Chao Li
DR-APO improves action targets using rollout feedback, refining actions and weighting them by advantage. It fits targets to Gaussian, diffusion, and flow policies through policy-specific objectives, without likelihood-ratio updates or inference-time multi-candidate critic selection. Across five continuous-control tasks, it reports strong diffusion and flow results; NavDP success rises in simulation and real-world trials, though simulation path-efficiency trails X-NavDP.
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