WIMLE: Uncertainty-Aware World Models with IMLE for Sample-Efficient Continuous Control | ScienceToStartup | ScienceToStartup

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References (59)

[1]

Learning with Imperfect Models: When Multi-step Prediction Mitigates Compounding Error

2025Anne Somalwar, Bruce D. Lee et al.

[2]

On Rollouts in Model-Based Reinforcement Learning

2025Bernd Frauenknecht, Devdutt Subhasish et al.

[3]

Towards General-Purpose Model-Free Reinforcement Learning

Founder's Pitch

"WIMLE enhances reinforcement learning with IMLE for superior sample efficiency in continuous control tasks."

Reinforcement Learning•Score: 3•View PDF ↗

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WIMLE: Uncertainty-Aware World Models with IMLE for Sample-Efficient Continuous Control

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References (59)

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"WIMLE enhances reinforcement learning with IMLE for superior sample efficiency in continuous control tasks."

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WIMLE: Uncertainty-Aware World Models with IMLE for Sample-Efficient Continuous Control

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References (59)

Founder's Pitch

"WIMLE enhances reinforcement learning with IMLE for superior sample efficiency in continuous control tasks."

Commercial Viability Breakdown

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