Shifting inductive bias with success-story algorithm, adaptive Levin search, and incremental self-improvement

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Schmidhuber:1997:ML,
  author =       "Juergen Schmidhuber and Jieyu Zhao and Marco Wiering",
  title =        "Shifting inductive bias with success-story algorithm,
                 adaptive Levin search, and incremental
                 self-improvement",
  journal =      "Machine Learning",
  year =         "1997",
  volume =       "28",
  pages =        "105--130",
  keywords =     "genetic algorithms, genetic programming, inductive
                 bias, reinforcement learning, reward acceleration,
                 Levin search, success-story algorithm, incremental
                 self-improvement",
  URL =          "ftp://ftp.idsia.ch/pub/juergen/mljssalevin.pdf",
  URL =          "http://www.idsia.ch/~juergen/mljssalevin/",
  size =         "30 pages",
  abstract =     "We study task sequences that allow for speeding up the
                 learner's average reward intake through appropriate
                 shifts of inductive bias (changes of the learner's
                 policy). To evaluate long-term effects of bias shifts
                 setting the stage for later bias shifts we use the
                 ``success-story algorithm'' (SSA). SSA is occasionally
                 called at times that may depend on the policy itself.
                 It uses backtracking to undo those bias shifts that
                 have not been empirically observed to trigger long-term
                 reward accelerations (measured up until the current SSA
                 call). Bias shifts that survive SSA represent a
                 lifelong success history. Until the next SSA call, they
                 are considered useful and build the basis for
                 additional bias shifts. SSA allows for plugging in a
                 wide variety of learning algorithms. We plug in (1) a
                 novel, adaptive extension of Levin search and (2) a
                 method for embedding the learner's policy modification
                 strategy within the policy itself (incremental
                 self-improvement). Our inductive transfer case studies
                 involve complex, partially observable environments
                 where traditional reinforcement learning fails.",
}

Genetic Programming entries for Jurgen Schmidhuber Jieyu Zhao Marco Wiering

Citations