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

Kavosh Asadi
Kavosh Asadi
Evan Cater
Evan Cater

arXiv: Learning, Volume abs/1806.01265, 2018.

Cited by: 2|Views32
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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. Recently Farahmand et al. (2017) proposed a value-aware (VAML) objective that captures the structure of value functio...More

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