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This is a Experimental Study study.

September 20, 2025

State Revisit and Re-explore: Bridging Sim-to-Real Gaps in Offline-and-Online Reinforcement Learning with An Imperfect Simulator

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The SR2 framework effectively improves performance in sim-to-real robotic tasks, enhancing policy transferability.
The algorithm combines a meta-policy to identify quality states, along with a sub-policy to learn robot skills with diverse data.
By revisiting and re-exploring states, the approach mitigates issues of sample inefficiency and state-action coverage.
Evaluation shows significant advancements over existing state-of-the-art methods, validating the effectiveness of our technique.

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Implication

This method proposes a hybrid offline-and-online reinforcement learning framework, improving performance in robot tasks while addressing state-action limitations in imperfect simulators.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68d469d631b076d99fa66dfd https://doi.org/10.24963/ijcai.2025/970

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Implication

This method proposes a hybrid offline-and-online reinforcement learning framework, improving performance in robot tasks while addressing state-action limitations in imperfect simulators.

State Revisit and Re-explore: Bridging Sim-to-Real Gaps in Offline-and-Online Reinforcement Learning with An Imperfect Simulator

Key Points

Discussion

Implication

Cite This Study

State Revisit and Re-explore: Bridging Sim-to-Real Gaps in Offline-and-Online Reinforcement Learning with An Imperfect Simulator

Key Points

Discussion

Implication

Cite This Study

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