Can We Really Learn One Representation to Optimize All Rewards? | ScienceToStartup | ScienceToStartup

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References (97)

[1]

BFM-Zero: A Promptable Behavioral Foundation Model for Humanoid Control Using Unsupervised Reinforcement Learning

2025Yitang Li, Zhengyi Luo et al.

[2]

Riesz Regression As Direct Density Ratio Estimation

2025Masahiro Kato

[3]

Transitive RL: Value Learning via Divide and Conquer

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"Develop a pre-trained RL policy representation method for improved zero-shot performance."

Reinforcement Learning•Score: 5•View PDF ↗

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Can We Really Learn One Representation to Optimize All Rewards?

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"Develop a pre-trained RL policy representation method for improved zero-shot performance."

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Can We Really Learn One Representation to Optimize All Rewards?

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References (97)

Founder's Pitch

"Develop a pre-trained RL policy representation method for improved zero-shot performance."

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