Papers › Learning Approximate Stochastic Transition Models

Learning Approximate Stochastic Transition Models

26 Oct 2017arXiv:1710.09718archive 2025-07-28

Yuhang Song, Christopher Grimm, Xianming Wang, Michael L. Littman

We examine the problem of learning mappings from state to state, suitable for use in a model-based reinforcement-learning setting, that simultaneously generalize to novel states and can capture stochastic transitions. We show that currently popular generative adversarial networks struggle to learn these stochastic transition models but a modification to their loss functions results in a powerful learning algorithm for this class of problems.

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Model-based Reinforcement Learning Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

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