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  • title: How Far I’ll Go: Offline Goal-Conditioned RL via f-Advantage Regression
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            How Far I’ll Go: Offline Goal-Conditioned RL via f-Advantage Regression
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            How Far I’ll Go: Offline Goal-Conditioned RL via f-Advantage Regression

            Nov 28, 2022

            Speakers

            JYM

            Jason Yecheng Ma

            Speaker · 0 followers

            JY

            Jason Yan

            Speaker · 0 followers

            DJ

            Dinesh Jayaraman

            Speaker · 0 followers

            About

            Offline goal-conditioned reinforcement learning (GCRL) promises general-purpose skill learning in the form of reaching diverse goals from purely offline datasets. We propose Goal-conditioned f-Advantage Regression (GoFAR), a novel regression-based offline GCRL algorithm derived from a state-occupancy matching perspective; the key intuition is that the goal-reaching task can be formulated as a state-occupancy matching problem between a dynamics-abiding imitator agent and an expert agent that dire…

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            NeurIPS 2022

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