Deterministic Techniques for Stochastic Planning

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Deterministic Techniques for Stochastic Planning

• No longer the Rodney Dangerfield of Stochastic
  Planning?

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Solving stochastic planning problems via
determinizations

• Quite an old idea (e.g. envelope extension
  methods)
• What is new is that there is increasing
  realization that determinizing approaches provide
  state-of-the-art performance
• Even for probabilistically interesting domains ?
• Should be a happy occasion..

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Ways of using deterministic planning

• To compute the conditional branches
• Robinson et al.
• To seed/approximate the value function
• ReTraSE,Peng Dai, McLUG/POND, FF-Hop

• Use single determinization
• FF-replan
• ReTrASE (use diverse plans for a single
  determinization)
• Use sampled determinizations
• FF-hop AAAI 2008 with Yoon et al
• Use Relaxed solutions (for sampled
  determinizations)
• Peng Dais paper
• McLug AIJ 2008 with Bryce et al

Determinization Sampling evolution of the
world
Would be good to understand the tradeoffs
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Comparing approaches..

• ReTrASE and FF-Hop seem closely related
• ReTrASE uses diverse deterministic plans for a
  single determinization FF-HOP computes
  deterministic plans for sampled determinizations
• Is there any guarantee that syntactic (action)
  diversity is actually related to likely sample
  worlds?
• Cost of generating deterministic plans isnt
  exactly too cheap..
• Relaxed reachability style approaches can compute
  multiple plans (for samples of the worlds)
• Would relaxation of samples plans be better or
  worse in convergence terms..?

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Science may never fully explain who killed JFK,
but any explanation must pass the scientific
judgement.
MDPs may never fully generate policies
efficiently but any approach that does must pass
MDP judgement.