
What can I do here? A Theory of Affordances in Reinforcement Learning
Reinforcement learning algorithms usually assume that all actions are al...
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Learning to Prove from Synthetic Theorems
A major challenge in applying machine learning to automated theorem prov...
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Marginalized State Distribution Entropy Regularization in Policy Optimization
Entropy regularization is used to get improved optimization performance ...
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InfoBot: Transfer and Exploration via the Information Bottleneck
A central challenge in reinforcement learning is discovering effective p...
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Understanding the impact of entropy on policy optimization
Entropy regularization is commonly used to improve policy optimization i...
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Understanding the impact of entropy in policy learning
Entropy regularization is commonly used to improve policy optimization i...
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VFunc: a Deep Generative Model for Functions
We introduce a deep generative model for functions. Our model provides a...
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Zafarali Ahmed
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