Body: <p>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.</p>
  <p>By HRL I mean all methods that (among others):</p>
  <ul>
  <li>split hard and complex problem into a series of simpler ones</li>
  <li>create desired intermediate goals (or spaces of such goals)</li>
  <li>somehow think in terms of 'what to achieve' rather than 'how to achieve'</li>
  </ul>
  <p>Do you know any examples of solving logically hard problems with HRL or maybe just any promising approaches to such problems?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-11-23T20:09:57.307Z
Comments.Text: I am not sure if "hard logical problems with combinatorial complexity" is the right term/expression to describe games like chess or Rubik's cube, though I think I understand what you mean. Maybe the term "combinatorial optimization problems" is what you mean?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-11-30T14:15:40.563Z
Comments.Text: Check this paper [Combinatorial Optimization by Graph Pointer Networks
  and Hierarchical Reinforcement Learning](https://arxiv.org/pdf/1911.04936.pdf).
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-12-01T21:22:52.683Z
Comments.Text: Thanks a lot. It may be something I was looking for.
Comments.UserDisplayName: Tomasz Odrzygozdz
Tags: <reinforcement-learning><combinatorial-games><hierarchical-rl>
Title: Hierarchical reinforcement learning for combinatorial complexity
UserDisplayName: Tomasz Odrzygozdz
ThreadText: # Hierarchical reinforcement learning for combinatorial complexity
  
  (Asked by: username_0 on 2020-11-23)
  
  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?
  
  **Tags:** reinforcement-learning, combinatorial-games, hierarchical-rl
  > **Comments:**
  > **username_1**: I am not sure if "hard logical problems with combinatorial complexity" is the right term/expression to describe games like chess or Rubik's cube, though I think I understand what you mean. Maybe the term "combinatorial optimization problems" is what you mean?  
  > **username_1**: Check this paper [Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement Learning](https://arxiv.org/pdf/1911.04936.pdf).  
  > **username_0**: Thanks a lot. It may be something I was looking for.  
  
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  ## 0 answers
  
  