Deterministic Techniques for Stochastic Planning No longer the Rodney Dangerfield of Stochastic...
-
date post
20-Dec-2015 -
Category
Documents
-
view
218 -
download
2
Transcript of Deterministic Techniques for Stochastic Planning No longer the Rodney Dangerfield of Stochastic...
Deterministic Techniques for Stochastic Planning
No longer the Rodney Dangerfield of Stochastic Planning?
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..
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 Dai’s paper• McLug [AIJ 2008; with
Bryce et al]
Would be good to understand the tradeoffs…
Determinization = Sampling evolution of the world
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 isn’t 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..?