Lipschitz Continuity in Model-based Reinforcement Learning.

ICML, (2018): 264-273

Cited by: 39|Views26
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Abstract:

Model-based reinforcement-learning methods learn transition and reward models and use them to guide behavior. We analyze the impact of learning models that are Lipschitz continuous---the distance between function values for two inputs is bounded by a linear function of the distance between the inputs. Our first result shows a tight bound ...More

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