Meurthe-et-Moselle
Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning
We also study the more general case of an additional entropy regularizer.
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Low-Rank Optimal Transport through Factor Relaxation with Latent Coupling Peter Halmos
We leverage these advantages to derive a new algorithm Factor Relaxation with Latent Coupling (FRLC), which uses coordinate mirror descent to compute the LC factorization.
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Reinforcement Learning with Lookahead Information
In reinforcement learning (RL), agents sequentially interact with a changing environment, aiming to collect as much reward as possible.
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rho-POMDPs have Lipschitz-Continuous epsilon-Optimal Value Functions
Mathieu Fehr, Olivier Buffet, Vincent Thomas, Jilles Dibangoye
Neural Information Processing Systems http://nips.cc/
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