Corpus ID: 102350686

Reinforced Imitation in Heterogeneous Action Space

@article{Zolna2019ReinforcedII,
  title={Reinforced Imitation in Heterogeneous Action Space},
  author={Konrad Zolna and N. Rostamzadeh and Yoshua Bengio and Sungjin Ahn and Pedro H. O. Pinheiro},
  journal={ArXiv},
  year={2019},
  volume={abs/1904.03438}
}
  • Konrad Zolna, N. Rostamzadeh, +2 authors Pedro H. O. Pinheiro
  • Published 2019
  • Computer Science, Mathematics
  • ArXiv
  • Imitation learning is an effective alternative approach to learn a policy when the reward function is sparse. In this paper, we consider a challenging setting where an agent and an expert use different actions from each other. We assume that the agent has access to a sparse reward function and state-only expert observations. We propose a method which gradually balances between the imitation learning cost and the reinforcement learning objective. In addition, this method adapts the agent's… CONTINUE READING
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