Stochastic Neural Networks for Hierarchical Reinforcement Learning

@article{Florensa2017StochasticNN,
  title={Stochastic Neural Networks for Hierarchical Reinforcement Learning},
  author={Carlos Florensa and Yan Duan and Pieter Abbeel},
  journal={CoRR},
  year={2017},
  volume={abs/1704.03012}
}
Deep reinforcement learning has achieved many impressive results in recent years. However, tasks with sparse rewards or long horizons continue to pose significant challenges. To tackle these important problems, we propose a general framework that first learns useful skills in a pre-training environment, and then leverages the acquired skills for learning faster in downstream tasks. Our approach brings together some of the strengths of intrinsic motivation and hierarchical methods: the learning… CONTINUE READING
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