IPD: Boosting Sequential Policy with Imaginary Planning Distillation in Offline Reinforcement Learning | ScienceToStartup | ScienceToStartup

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References (30)

[1]

Retrieval Dexterity: Efficient Object Retrieval in Clutters with Dexterous Hand

2025Fengshuo Bai, Yu Li et al.

[2]

AdaManip: Adaptive Articulated Object Manipulation Environments and Policy Learning

2025Yuanfei Wang, Xiaojie Zhang et al.

[3]

RAT: Adversarial Attacks on Deep Reinforcement Agents for Targeted Behaviors

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"A new framework increases offline RL performance by enhancing dataset quality with Imaginary Planning Distillation."

Reinforcement Learning•Score: 6•View PDF ↗

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IPD: Boosting Sequential Policy with Imaginary Planning Distillation in Offline Reinforcement Learning

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References (30)

Founder's Pitch

"A new framework increases offline RL performance by enhancing dataset quality with Imaginary Planning Distillation."

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IPD: Boosting Sequential Policy with Imaginary Planning Distillation in Offline Reinforcement Learning

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References (30)

Founder's Pitch

"A new framework increases offline RL performance by enhancing dataset quality with Imaginary Planning Distillation."

Commercial Viability Breakdown

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