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arXiv · 1811.00128

Towards a Simple Approach to Multi-step Model-based Reinforcement Learning

Kavosh Asadi·Evan Cater·Dipendra Misra·Michael L. Littman
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Abstract

When environmental interaction is expensive, model-based reinforcement learning offers a solution by planning ahead and avoiding costly mistakes. Model-based agents typically learn a single-step transition model. In this paper, we propose a multi-step model that predicts the outcome of an action sequence with variable length. We show that this model is easy to learn, and that the model can make policy-conditional predictions. We report preliminary results that show a clear advantage for the multi-step model compared to its one-step counterpart.

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Kavosh Asadi, Evan Cater, Dipendra Misra, Michael L. Littman. 2018-10-31. Towards a Simple Approach to Multi-step Model-based Reinforcement Learning. https://arxiv.org/abs/1811.00128

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