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I want to try a hierarchical reinforcement learning (HRL) approach to hard logical problems with combinatorial complexity, i.e. games like chess or Rubik's cube. The majority of HRL papers I have found so far focus either on training a control policy or they tackle quite simple games.

By HRL I mean all methods that (among others):

  • split hard and complex problem into a series of simpler ones
  • create desired intermediate goals (or spaces of such goals)
  • somehow think in terms of 'what to achieve' rather than 'how to achieve'

Do you know any examples of solving logically hard problems with HRL or maybe just any promising approaches to such problems?

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