Difference between revisions of "Thread:Talk:Scalar/Version History/Taking your own movement into account when targeting?"
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Sometimes an opponent that always moves perpendicular to you moves in straight lines, not circles, when you keep moving perpendicular to him as well. And for anything that controls attack angle, they won't move in perfect circle either. We've been already adding lat, adv, distance etc. to help our gun distinguish these situations, but what is currently lacking mostly, is about our own movement (although I see DrussGT is adding mirror offset). The lack of attributes about our own movement cannot be compensated by more data, and it can neither be made up by using more prediction power, e.g. PIF. | Sometimes an opponent that always moves perpendicular to you moves in straight lines, not circles, when you keep moving perpendicular to him as well. And for anything that controls attack angle, they won't move in perfect circle either. We've been already adding lat, adv, distance etc. to help our gun distinguish these situations, but what is currently lacking mostly, is about our own movement (although I see DrussGT is adding mirror offset). The lack of attributes about our own movement cannot be compensated by more data, and it can neither be made up by using more prediction power, e.g. PIF. | ||
| − | If we can eliminate the effect of wall (e.g. the use of precise MEA) & our own movement to enemy movement, the accuracy of our guns could ascend to an even higher level, where only the weakness (the flaw of being flat) of enemy is learned. I see this as the holy grail of guns, where each choice affects greatly how good your learning algorithm could perform. | + | If we can eliminate the effect of wall (e.g. the use of precise MEA) & our own movement to enemy movement, the accuracy of our guns could ascend to an even higher level, where only the weakness (the flaw of being flat) of enemy is learned. I see this as the holy grail of guns, where each choice affects greatly how good your learning algorithm could perform and with this holy grail, any learning algorithm should reach its theoretical limit. |
Latest revision as of 08:20, 2 November 2017
While in melee PIF is more popular, in 1v1 most people (I guess) is using something similar to GuessFactor, where lateral movement is assumed. And whether you are scaling based on theoretical MEA or orbital MEA or precise MEA, we always assume that enemy movement is only relevant to our initial position and scales as if it moves in a circle — which is not true for real battles.
Sometimes an opponent that always moves perpendicular to you moves in straight lines, not circles, when you keep moving perpendicular to him as well. And for anything that controls attack angle, they won't move in perfect circle either. We've been already adding lat, adv, distance etc. to help our gun distinguish these situations, but what is currently lacking mostly, is about our own movement (although I see DrussGT is adding mirror offset). The lack of attributes about our own movement cannot be compensated by more data, and it can neither be made up by using more prediction power, e.g. PIF.
If we can eliminate the effect of wall (e.g. the use of precise MEA) & our own movement to enemy movement, the accuracy of our guns could ascend to an even higher level, where only the weakness (the flaw of being flat) of enemy is learned. I see this as the holy grail of guns, where each choice affects greatly how good your learning algorithm could perform and with this holy grail, any learning algorithm should reach its theoretical limit.
