Editing revision 23 of RougeDC/MovementLab

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Well, as a parallel to [[RougeDC/TargetingLab]], here's the movement lab.

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[[WaveSurfingChallenge2K6]]
| '''RR Version''' | '''MC Version''' | '''Bot A''' | '''Bot B''' | '''Bot C''' | '''Total''' | '''Comment'''
| Gamma4 | MC01 | 96.22 | 98.18 | 97.53 | '''97.31'''  | 1 season (500 Rounds)
|                | MC03 | 99.94 | 98.33 | 95.98 | '''98.08'''  | 1 season (500 Rounds)
|                | MC04 | 100.00 | 98.72 | 96.58 | '''98.43'''  | 1 season (500 Rounds)

[[MovementChallenge2K7/ResultsFastLearning]]
| '''RR Version''' | '''MC Version''' | '''HOF''' | '''SPL''' | '''GRG''' | '''Sub 1''' | '''WAY (Sub 2)''' | '''GR3''' | '''RKM''' | '''Sub 3''' | '''ASC''' | '''CC''' | '''CHK''' | '''Sub 4''' | '''Total''' | '''Comments'''
| Alpha12 |            | 99.85 | 84.95 | 87.93 | '''90.91''' | '''65.80''' | 72.81 | 76.26 | '''74.54''' | 29.26 | 37.74 | 37.49 | '''34.83''' | '''66.52''' | 31.0 seasons
| Alpha13 |            | 99.70 | 85.87 | 90.58 | '''92.05''' | '''66.35''' | 73.17 | 76.61 | '''74.89''' | 31.28 | 41.07 | 40.58 | '''37.64''' | '''67.73''' | 31.0 seasons
| Alpha14 |            | 99.81 | 87.45 | 91.45 | '''92.90''' | '''67.68''' | 72.74 | 78.37 | '''75.55''' | 28.80 | 39.62 | 38.00 | '''35.47''' | '''67.90''' | 45.0 seasons
| Alpha15 |            | 99.70 | 85.91 | 91.53 | '''92.38''' | '''66.31''' | 72.88 | 77.88 | '''75.38''' | 32.18 | 40.19 | 39.11 | '''37.16''' | '''67.81''' | 45.0 seasons
| Gamma4 | MC01 | 99.83 | 85.55 | 92.07 | '''92.48''' | '''68.29''' | 72.74 | 79.32 | '''76.03''' | 30.83 | 38.73 | 37.33 | '''35.63''' | '''68.11''' | 34.0 seasons
|                | MC03 | 99.93 | 85.25 | 90.46 | '''91.88''' | '''65.71''' | 71.99 | 78.99 | '''75.49''' | 25.78 | 37.35 | 33.67 | '''32.27''' | '''66.34''' | 47.0 seasons
|                | MC04 | 99.37 | 86.45 | 90.01 | '''91.94''' | '''65.70''' | 72.90 | 77.96 | '''75.43''' | 26.03 | 35.21 | 32.83 | '''31.36''' | '''66.11''' | 50.0 seasons
|                | MC05 | 99.88 | 85.56 | 91.67 | '''92.37''' | '''66.60''' | 72.29 | 79.19 | '''75.74''' | 27.40 | 37.25 | 32.56 | '''32.40''' | '''66.78''' | 29.0 seasons
|                | MC06 | 99.79 | 86.61 | 92.03 | '''92.81''' | '''67.68''' | 72.24 | 78.63 | '''75.43''' | 26.14 | 37.47 | 34.68 | '''32.76''' | '''67.17''' | 30.0 seasons
|                | MC07 | 99.69 | 86.35 | 91.65 | '''92.56''' | '''68.12''' | 72.36 | 79.06 | '''75.71''' | 26.49 | 37.62 | 35.74 | '''33.29''' | '''67.42''' | 50.0 seasons
|                | MC09 | 99.80 | 85.01 | 88.72 | '''91.18''' | '''67.44''' | 72.43 | 77.19 | '''74.81''' | 26.89 | 38.59 | 36.58 | '''34.02''' | '''66.86''' | 50.0 seasons
|                | MC10 | 99.92 | 86.33 | 91.28 | '''92.51''' | '''66.64''' | 72.99 | 78.86 | '''75.93''' | 27.26 | 37.43 | 34.57 | '''33.09''' | '''67.04''' | 27.0 seasons
|                | MC11 | 99.80 | 85.59 | 92.64 | '''92.68''' | '''68.22''' | 71.95 | 78.10 | '''75.03''' | 25.86 | 37.62 | 33.08 | '''32.19''' | '''67.03''' | 28.0 seasons
|                | MC12 | 99.88 | 87.02 | 92.28 | '''93.06''' | '''68.75''' | 72.40 | 78.75 | '''75.57''' | 26.14 | 37.02 | 34.76 | '''32.64''' | '''67.51''' | 28.0 seasons
|                | MC13 | | | | | | | | | 27.60 | 40.59 | 34.34 | '''34.18''' | | 4.0 seasons
|                | MC14 | | | | | | | | | 28.82 | 39.16 | 38.62 | '''35.54'''  | | 6.0 seasons
|                | MC15 | | | | | | | | | 29.14 | 40.34 | 38.08 | '''35.85'''  | | 20.0 seasons
|                | MC16 | | | | | | | | | 29.54 | 39.01 | 36.89 | '''35.15'''  | | 20.0 seasons
|                | MC17 | | | | | | | | | 30.17 | 43.20 | 39.52 | '''37.63'''  | | 22.0 seasons

'''Version History:'''
* '''MC01:''' The same as the movement in RougeDC Gamma4. While I didn't change the movement since Alpha15, there was a score increase, probably from a bugfix I made in my generic wave code somewhere along the way.
* '''MC02:''' Trying an all new surfing algorithm that I think should handle all waves better. Still needs work, the results were not worth recording even.
* '''MC03:''' The same all new surfing algorithm as in MC02, however using the old danger formula from MC01, and works far better than MC02. It still performs worse than MC01 for the most part though, interestingly though, it gets a HOF score among the very highest there is. To me this indicates that it's better quality surfing but that the danger formula needs work to tune it to it probably...
* '''MC04:''' Some tweaks to the danger formula. Seems to help some though the Bot C performance still isn't great.
* '''MC05:''' Remove all but one tweak to the danger formula (making it pickier about how far away the DC match is). Bah... still a lot of work needed...
* '''MC06:''' Change cluster size to 10 (from 20). Seems to have a nice effect overall.
* '''MC07:''' First try with adding a neural net to the mix. I'm now suspecting that the improvements were mostly from just making it trickier for adaptive guns to understand my movement.
* '''MC08:''' Attempts to make the neural net work better. Failed attempts not worth recording...
* '''MC09:''' Replace the neural net with linear regression... doesn't seem to do much good...
* '''MC10:''' Surf both adjusted and non-adjusted values. 
* '''MC11:''' MC06 plus one interpolated value.
* '''MC12:''' MC06 plus a distance segment.
* '''MC13:''' First attempt at building a flattener. Disable all stats except the flattener.
* '''MC14:''' 50/50 balance of flattener and normal stats. Whoops... that was still pure flattener I think...
* '''MC15:''' Fixed the 50/50 balance to work right, and increase cluster size to 20 for the flattener...
* '''MC16:''' Switch the flattener to a movement profile system matching my gun.
* '''MC17:''' Based on MC15. Add a "prefer newer data segment".

=== Discussion ===

Gahhh.... even though MC04 didn't hurt head-on performance according to WSC2K6, it's HOF performance took a major nosedive in MC2K7. The changes there were helpful to the [[Splinter]] and [[GrubbmThree]] performance but not much else. The surfer performance could surely be helped a great deal by a flattener, but I should first get my scores in "Sub 1" and "Sub3" up to decent levels I think... -- [[Rednaxela]]

Haha! The upcoming MC07 adds a NeuralNetwork to the mix, and results so far seem to show it as an improvement. What could it possibly offer a DC surfing? Well, The way I see it, normal DynamicClustering, is far from optimal against simple linear-targeting. The problem, is that for it to be accurate, it needs an awful lot of data samples (at least one at just about any velocity you may expect to be moving at) to produce good results, and it's inherently incapable of making guesses at gf values it has not seen before. Well, to solve that I added a simple back-propagation net. It's designed to find general trends in how the segment data tends to affect the guessfactor. Instead of just weighting results from DC lower when the situation is further from the current situation, I also use the neural net to reposition the guessfactors that the DC spits out, to be more relevant to the current situation. Wow... my bot is really tending to hybridize things all over the place. Actually, I find hybridizing far more pleasant than tuning, it's both more interesting and seems to get me results quicker. My targeting is DC/PM and my surfing is now DC/NN. I think this could likely be the first high ranking bot that uses both DC, PM, and NN learning/prediction techniques. Anyways, I'll post the MC07 results when I have 30 seasons I think. I think there's certainly room for improvement in the neural net though, such as trying out my [[Rednaxela/MultiplePlaneRegressionClustering|"multiple plane regression clustering"]] idea to cluster multiple neural nets in order to more accurately model multi-mode targeters. -- [[Rednaxela]]

Well, it seems like my ideas to "focus" the data with a neural net or linear regression have kind of failed. They don't seem to actually do much except confuse adaptive guns somewhat. I'm thinking now that I might attempt to overlay a couple DC-trees with different segmentations. Well... or just before I let the focusing idea die, I might try overlaying it with the non-focused data at the same time and see how that does... -- [[Rednaxela]]

Apparently, my flattener really sucks! Hooray! Time to figure out what sucks so much about it... -- [[Rednaxela]]

Try weighting your flattener 50/50 with your main stats - that's what worked best for me. -- [[Skilgannon]]

Just tried it now. That seems to improve results a bit, however I think I can still safely conclude that my flattener really sucks. I do have a few ideas to test for improving it though... -- [[Rednaxela]]
 '''Update:''' Whoops, that was pure flattener again still I think. Running a new test now, with 50/50 weighting, and the cluster size for the flattener increased to 20... -- [[Rednaxela]]
 '''Update:''' Okay! It appears my flattener does indeed still suck just like I thought. To fix it, I'm thinking of making it so, instead of dodging specific points the DC returns, it will dodge a "hit range profile", basically what my gun uses. In other words, I'll make my flattener into something that's more of a model of my own DC gun. At very least, It'll be better at dodging myself, and I also suspect that processing DC stats this way will give results closer to VCS too. -- [[Rednaxela]]
 '''Update:''' And... that doesn't seem to help... -- [[Rednaxela]]

What attributes are you clustering your flattener on? The important ones are distance (because their guns use it), acceleration, and lateral velocity. Throwing in a time-based attribute also helps. What you're trying to do is create a copy of their gun. -- [[Skilgannon]]

Currently I use distance, lateral velocity, and advancing velocity, and a very very light segment on how many waves have been fired before this one (just strong enough to prefer new data over old). I guess I might see if acceleration helps, but I'm not sure it will because FloodGrapher appears to show me as reasonably flat in that segment too I think. -- [[Rednaxela]]

I see you've increased the number of points surfed - are you weighting them by the inverse euclidean distance squared? Have you tried other methods, like inverse 'absolute value' distance, etc? Be careful of trusting FloodGrapher on surfing matters - it takes every single wave, and not just the waves with bullets, as a strong anti-surfer gun would (Ascendant, Dookious, Phoenix, etc). Try weighting advancing velocity lower, I don't think many anti-surfer guns use that one. Time-based attributes (also weighted low) prevent pattern matchers from picking up on your movement, as well as confusing the higher-legion "every single tick" guns like Shadow and Dookious's main gun. I found that multiplying the distance by how many hits ago the data was recorded was an effective way of preferring new data over old. [[DrussGT/ChallengeResults]] shows some interesting info - take a look at the lower MC2K7 section, testing out DC movement. -- [[Skilgannon]]

Well, I actually didn't have my "prefer new data" segment in MC15/16, and adding that seems to have helped significantly. I didn't need such a segment in my normal surfing because there isn't enough data for old data to flood the nearest-neighbors,  but there are far more data points for a flattener of course. So still not quite there yet, but getting closer. Multiplying the distance by how many hits ago the data was recorded sounds interesting however I prefer my approach of making it a very weakly weighted segment, because when doing DC it's possible that all of the selected data points might be very old without that preference. As far as the weighting of points, I'm currently sticking with the same thing that I tuned for my normal surfing. It's a bit overcomplicated and messy my danger formula, but I'd rather change it for both the surfing and flattening at once some other time to keep it in sync and avoid code duplication. I think that next, I'll try acceleration, wall, and time-based segments. -- [[Rednaxela]]

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