Tuesday, March 6, 2012

Minutes 06-03-2012

D. Silver's paper
  • Discussed belief state approximation with particles
  • Discussed pseudo code in detail
  • Worked out tree construction
Possible Extensions
  • Continuous observations
    • Store observation and corresponding reward for all roll-outs
  • Continuous actions
    • How to transform a large number of discrete actions to one (or a few) continuous action(s)?
  • Combinations of both
Possible Modifications
  • Particle filter could be replaced by (extended) Kalman filter
  • Expected reward computation (RDiff)
Evaluation
  • Compare approximate results to exact results (e.g. from J. Hoey [1])
[1] Jesse Hoey and Pascal Poupart. Solving POMDPs with continuous or large discrete observation spaces. In Proceedings on the International Joint Conference on Artificial Intelligence. pages 1332 - 1338. 2005.

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