[0] Peters J, Schaal S, Reinforcement learning of motor skills with policy gradients.Neural Netw 21:4, 682-97 (2008 May)


{651} revision 4 modified: 02-17-2009 18:49 gmt

PMID-18482830[0] Reinforcement learning of motor skills with policy gradients

  • they say that the only way to deal with reinforcement or general-type learning in a high-dimensional policy space defined by parameterized motor primitives are policy gradient methods.
  • article is rather difficult to follow; they do not always provide enough details (for me) to understand exactly what their equations mean. Perhaps this is related to their criticism that others's papers are 'ad-hoc' and not 'statistically motivated'
  • none the less, it seems interesting..
  • their previous paper - Reinforcement learning for Humanoid robotics - maybe slightly easier to understand.

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