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We explore methods for option discovery based on variational inference and make two algorithmic contributions.
Universal Value Function Approximators
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The Eigenoption-Critic Framework
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Count-Based Exploration with Neural Density Models
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Curiosity-driven Exploration by Self-supervised Prediction
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Independently Controllable Factors
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