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The Bottleneck Simulator: A Model-Based Deep Reinforcement Learning Approach
2020
The Journal of Artificial Intelligence Research
Deep reinforcement learning has recently shown many impressive successes. However, one major obstacle towards applying such methods to real-world problems is their lack of data-efficiency. To this end, we propose the Bottleneck Simulator: a model-based reinforcement learning method which combines a learned, factorized transition model of the environment with rollout simulations to learn an effective policy from few examples. The learned transition model employs an abstract, discrete
doi:10.1613/jair.1.12463
fatcat:2atgr7pihjfrzj4eypbokbhzzq