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Learning approximate representations of partially observable systems

McGill University
Publication Date
  • Applied Sciences - Computer Science
  • Computer Science


In this thesis we are primarily concerned with ways of representing the agent's state that allows it to predict the conditional probability of a restricted set of future events, given the agent's past experience. Because of memory limitations, the agent's experience must be summarized in such a way as to make these restricted predictions possible. We introduce the novel idea of history representations, which allow us to condition the predictions on ``interesting'' behaviour, and present a simple algorithmic implementation of this framework. The learned model abstracts away the unnecessary details of the agent's experience and focuses only on making certain predictions of interest. We illustrate our approach empirically in small computational examples, demonstrating the data efficiency of the algorithm.

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