Scaling Tangled Program Graphs to Visual Reinforcement Learning in ViZDoom

Created by W.Langdon from gp-bibliography.bib Revision:1.4031

  author =       "Robert J. Smith and Malcolm I. Heywood",
  title =        "Scaling Tangled Program Graphs to Visual Reinforcement
                 Learning in {ViZDoom}",
  booktitle =    "EuroGP 2018: Proceedings of the 21st European
                 Conference on Genetic Programming",
  year =         "2018",
  month =        "4-6 " # apr,
  editor =       "Mauro Castelli and Lukas Sekanina and 
                 Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez",
  series =       "LNCS",
  volume =       "10781",
  publisher =    "Springer Verlag",
  address =      "Parma, Italy",
  organisation = "EvoStar, Species",
  note =         "forthcoming",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-77552-4",
  abstract =     "A tangled program graph framework (TPG) was recently
                 proposed as an emergent process for decomposing tasks
                 and simultaneously composing solutions by organizing
                 code into graphs of teams of programs. The initial
                 evaluation assessed the ability of TPG to discover
                 agents capable of playing Atari game titles under the
                 Arcade Learning Environment. This is an example of
                 visual reinforcement learning, i.e. agents are evolved
                 directly from the frame buffer without recourse to hand
                 designed features. TPG was able to evolve solutions
                 competitive with state-of-the-art deep reinforcement
                 learning solutions, but at a fraction of the
                 complexity. One simplification assumed was that the
                 visual input could be down sampled from a 210 x 160
                 resolution to 42 x 32. In this work, we consider the
                 challenging 3D first person shooter environment of
                 ViZDoom and require that agents be evolved at the
                 original visual resolution of 320 x 240 pixels. To do
                 so, we address issues with task scenarios performing
                 fitness evaluation over multiple tasks. The resulting
                 TPG solutions retain all the emergent properties of the
                 original work as well as the computational efficiency.
                 Moreover, solutions appear to generalize across
                 multiple task scenarios, whereas equivalent solutions
                 from deep reinforcement learning have focused on single
                 task scenarios alone.",
  notes =        "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in
                 conjunction with EvoCOP2018, EvoMusArt2018 and

Genetic Programming entries for Robert J Smith Malcolm Heywood