Difference between revisions of "AgentSmith/Wolfmans Todo List"

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* <strike>Weight normalised DC values for unbounded segments in a better manner (such as time-since values etc)</strike> - DONE - ''but performance decreased. Needs investigating''
 
* <strike>Weight normalised DC values for unbounded segments in a better manner (such as time-since values etc)</strike> - DONE - ''but performance decreased. Needs investigating''
 
* <strike>Factor in distance from the best match when deciding what angle to shoot at based on our K nearest matches.</strike> - DONE
 
* <strike>Factor in distance from the best match when deciding what angle to shoot at based on our K nearest matches.</strike> - DONE
* <strike>Store dynamic DC weights per bot between matches to give the targeting a head start even if we don't store any of the tree.</strike> Dynamic weighting removed. Needs more thinking.
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* <strike>Store dynamic DC weights per bot between matches to give the targeting a head start even if we don't store any of the tree.</strike> - "Dynamic weighting removed. Needs more thinking."
 
* Add in really quick and dirty random movement
 
* Add in really quick and dirty random movement
 
* Release into the rumble
 
* Release into the rumble

Revision as of 21:31, 26 March 2013

Agent Smith Sub-pages:
AgentSmithVersion History - Challenge Results - Wolfmans Todo List

Short Term

  • Get the gun to a reasonable standard - add more segmentation
  • Use targeting challenge results to tune gun
  • Weight normalised DC values for unbounded segments in a better manner (such as time-since values etc) - DONE - but performance decreased. Needs investigating
  • Factor in distance from the best match when deciding what angle to shoot at based on our K nearest matches. - DONE
  • Store dynamic DC weights per bot between matches to give the targeting a head start even if we don't store any of the tree. - "Dynamic weighting removed. Needs more thinking."
  • Add in really quick and dirty random movement
  • Release into the rumble
  • Calculate MEA using Precise Prediction and Wall Smoothing

Long Term

  • Multiple gun modes chosen dynamically based on match state
  • Anti-surfing DC gun mode
  • Wave surfing movement
  • Genetic algorithms to tune various constants in the bot
  • Investigate storing small pre-populated data set for faster DC learning in a fresh match scenario