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Sample Efficiency: Learning from Limited Experience - RL Applications and Challenges | OneNoughtOne

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Machine Learning0/328 completed

1.Game Playing: RL's Proving Ground 2.Robotics: RL Meets the Physical World 3.Sample Efficiency: Learning from Limited Experience 4.Exploration: Discovering Rewarding Behaviors 5.Reward Engineering: Specifying What We Want

Intra-Group Advantage Normalization for Policy Training
Unbiased Policy Divergence Estimation for Regularized Optimization

Course overview & roadmap

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Sample Efficiency: Learning from Limited Experience

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