Equivalence Between Wasserstein and Value-Aware Loss for Model-based Reinforcement Learning

Kavosh Asadi
Kavosh Asadi
Evan Cater
Evan Cater
Cited by: 0|Views16

Abstract:

Learning a generative model is a key component of model-based reinforcement learning. Though learning a good model in the tabular setting is a simple task, learning a useful model in the approximate setting is challenging. In this context, an important question is the loss function used for model learning as varying the loss function ca...More

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