Difference between revisions of "AgentSmith/Wolfmans Todo List"
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* Use targeting challenge results to tune gun | * Use targeting challenge results to tune gun | ||
* <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'' | ||
| − | * Factor in distance from the best match when deciding what angle to shoot at based on our K nearest matches. | + | * <strike>Factor in distance from the best match when deciding what angle to shoot at based on our K nearest matches.</strike> - 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. | + | * <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:
- AgentSmith - Version 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 investigatingFactor in distance from the best match when deciding what angle to shoot at based on our K nearest matches.- DONEStore 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
