IWR improves CRL sample efficiency and performance in interaction-rich manipulation by interaction-aware resampling that preserves mode boundaries, yielding 19.8% average gains and a real-world air-hockey agent.
Proto successor measure: Representing the behavior space of an RL agent
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Switching successor measures extend classical successor measures to enable hierarchical zero-shot RL via the FB π-Switch algorithm that extracts subgoal-selection and control policies from forward-backward representations.
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.
citing papers explorer
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Learning Object Manipulation from Scratch via Contrastive Interaction
IWR improves CRL sample efficiency and performance in interaction-rich manipulation by interaction-aware resampling that preserves mode boundaries, yielding 19.8% average gains and a real-world air-hockey agent.
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Switching Successor Measures for Hierarchical Zero-shot Reinforcement Learning
Switching successor measures extend classical successor measures to enable hierarchical zero-shot RL via the FB π-Switch algorithm that extracts subgoal-selection and control policies from forward-backward representations.
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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.