Corpus ID: 16878327

LOCAL GOALS DRIVEN HIERARCHICAL REINFORCEMENT LEARNING*

@inproceedings{Pchelkin2007LOCALGD,
  title={LOCAL GOALS DRIVEN HIERARCHICAL REINFORCEMENT LEARNING*},
  author={A. Pchelkin},
  year={2007}
}
Efficient exploration is of fundamental importance for autonomous agents that learn to act. Previous approaches to exploration in reinforcement learning usually address exploration in the case when the environment is fully observable. In contrast, the current paper, like the previous paper [Pch2003], studies the case when the environment is only partially observable. One additional difficulty is considered – complex temporal dependencies. In order to overcome this additional difficulty a new… Expand

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