Corpus ID: 202712580

Meta-Inverse Reinforcement Learning with Probabilistic Context Variables

@article{Yu2019MetaInverseRL,
  title={Meta-Inverse Reinforcement Learning with Probabilistic Context Variables},
  author={Lantao Yu and Tianhe Yu and Chelsea Finn and S. Ermon},
  journal={ArXiv},
  year={2019},
  volume={abs/1909.09314}
}
  • Lantao Yu, Tianhe Yu, +1 author S. Ermon
  • Published 2019
  • Computer Science, Mathematics
  • ArXiv
  • Reinforcement learning demands a reward function, which is often difficult to provide or design in real world applications. While inverse reinforcement learning (IRL) holds promise for automatically learning reward functions from demonstrations, several major challenges remain. First, existing IRL methods learn reward functions from scratch, requiring large numbers of demonstrations to correctly infer the reward for each task the agent may need to perform. Second, and more subtly, existing… CONTINUE READING
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