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06/23/2023
GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models
Knowledge distillation is commonly used for compressing neural networks ...
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05/31/2023
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback
Despite the seeming success of contemporary grounded text generation sys...
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05/22/2023
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice
Mirror descent value iteration (MDVI), an abstraction of Kullback-Leible...
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05/27/2022
KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal
In this work, we consider and analyze the sample complexity of model-fre...
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08/16/2021
Implicitly Regularized RL with Implicit Q-Values
The Q-function is a central quantity in many Reinforcement Learning (RL)...
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06/11/2021
Offline Reinforcement Learning as Anti-Exploration
Offline Reinforcement Learning (RL) aims at learning an optimal control ...
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03/02/2021
Offline Reinforcement Learning with Pseudometric Learning
Offline Reinforcement Learning methods seek to learn a policy from logge...
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07/28/2020
Munchausen Reinforcement Learning
Bootstrapping is a core mechanism in Reinforcement Learning (RL). Most a...
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03/31/2020
Leverage the Average: an Analysis of Regularization in RL
Building upon the formalism of regularized Markov decision processes, we...
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10/21/2019
Momentum in Reinforcement Learning
We adapt the optimization's concept of momentum to reinforcement learnin...
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10/18/2019
On Connections between Constrained Optimization and Reinforcement Learning
Dynamic Programming (DP) provides standard algorithms to solve Markov De...
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06/24/2019