Reproduce “Echo: Scaling Visual Reinforcement Learning with Off-policy Experience” on OpenResearch
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About this paper
Gagan Khandate
Echo trains a visual policy using additional experience collected by a state-based imitator acting without rendering observations. A shared critic learns from both transition streams and updates the visual policy using rendered state-observation pairs. Compared with SDPG under evaluated configurations, Echo reduces time-to-reference by 60% in locomotion and 56% in hand tasks; these comparisons do not isolate the imitator’s contribution.
Summary by alphaXiv.
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