RougeDC/MovementLab - Robo Wiki -= Collecting Robocode Knowledge =-

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

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

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.

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 "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

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)

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