ROVER pretrains transferable exploration policies by maximizing occupancy coverage with a learned resolvent world model and virtual sink state, outperforming baselines on sparse navigation tasks.
Wilson, and Emmanuel Rachelson
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Reward-free Pretraining for Reinforcement Learning via Occupancy Coverage Maximization
ROVER pretrains transferable exploration policies by maximizing occupancy coverage with a learned resolvent world model and virtual sink state, outperforming baselines on sparse navigation tasks.