Context of this question:
- I’m a pretty big fan of AMR - and stick as closely to what I learn about optimization (and rotations) from your simulations.
- I support (and understand) the idea of Zero human influence in machine learning simulations (think of AlphaZero, or LC0).
- I am completely perplexed by the recommendation I’m getting for my Havoc Demon Hunter Legendary.
- Feel free to look at Veast on Farstriders (US) - I will post my Addon Info at the bottom.
Using AMR, here’s the rotation thought to be optimal for my Havoc DH:
Sinful Brand
Metamorphosis
Vengeful Retreat
Fel Rush
Eye Beam
Death Sweep (Blade Dance)
Blade Dance
Annihilation (Chaos Strike)
Chaos Strike
Immolation Aura
Felblade
Throw Glaive
Here’s my Gnome Sequencer Script trying to be as close to this recommendation as possible. Note that the recommendation includes some logic that can’t be built into scripts:
/cast [nodead,harm] Sinful Brand
/targetenemy [noharm][dead]
/startattack [nocombat]
/cast [nochanneling,combat,@player] Metamorphosis
/castsequence [nochanneling,combat,exists,mod:alt] Vengeful Retreat, Fel Rush
/cast [nochanneling,combat] Eye Beam
/cast [nochanneling, combat] Blade Dance
/cast [nochanneling, combat] Chaos Strike
/cast [nochanneling,combat] Immolation Aura
/castsequence [nochanneling,combat,exists,mod:alt] Vengeful Retreat, Fel Rush
/cast [nochanneling,combat,talent:1/3,exists] Felblade
/cast [nochanneling,combat] Throw Glaive
Two runs against a covenant dummy for 1m shows fairly good DPS output:
From the Simulation, AMR measured the following damage output ( re-run 37683d6eb2c847b084320f058d8b55d1 ) 4258 DPS:
(Had an image but not allowed to post more than 1, re-run above)
For comparison, the run against the covenant dummy produce this output:
(Had an image but not allowed to post more than 1)
I’m not concerned about the obvious differences because I wasn’t’ using Raid appropriate simulation adjustments (Food, and Potions). I know I can tune these out of the simulation, but didn’t bother). Even so the overall pattern is reflected. Chaos Strike, Melee, Blade Dance are all high. The covenant power is near the top of the list. Annihilation is powerful (there’s slightly more output in the real test, but this is one power that doesn’t have the logic built in).
Yet, when I look at the legendary recommendation:
(Use my character to recreate recommendation. Had an Image but not allowed more than 1)
It suggests legendries that improve low impact skills.
For example, Fel Bombardment is number 1, thought to improve DPS by 10.6% and yet it influences Throw Glaive which is only responsible for 0.9% in simulation and 2.9% in real tests. How can such a low impact skill result in +10.6% improvement in DPS output?
Similarly, Everburn is 3rd at 5.66% improvement. It improves Demon’s Bite, yet my talent build, which AMR knows is 3311222 and talent 2/3 Demon Blades removes Demon’s Bite. Does Everburn work on Demon’s Blades too?
Is anyone able to help me understand AMR’s legendary recommendation given AMR’s recommended rotation (priority rotation)?
Character:
97;US;Farstriders;Veast;Local Tourist;6;1;60;13:66,15:1,12:200,8:100,2:150;1;.s1;4;3311222;8;.s2;5;;0;.q1;172316s10b1507b5142b1b67b333e6205!971F1BF17;6420s12b-5549b4367b778b6b283b269e-41!97580786D;19s15b-5703b4367b778b6b553!978891499;14s14b-5704b4360b785b6b157!96E357696;739s17b-6767b1430b4419b755!9784409C9;1407s1b-5132b5132b555!95D8EC0F8;5s3b-5687b5132b555!9675152BA;1503s16b-5703b5148b6b537!977708CA0;555s9b-5717b5174b6b537b4!969451B67;15s7b-5721b5174b6b537!96652F98D;2s6b-5717b5174b6b537b4!95B38A830;14s5b-5721b5174b6b537e50!968983103;1007s13b-7147b1443b5161b542!9774EE20C;124s8b-5696b5154b6e-3!9660532C9;2s11b-5160b5154b6b542e-45!96A619CA1;613s2v60b-1b47!96EF1C259;.sol;u.8.7;8.1.0.153.5;8.2.0.0.0;8.3.1.134.5;8.4.1.0.0;8.5.1.131.5;8.6.0.150.5;8.7.0.0.0;8.0.1.0.0;u.9.0;u.3.0;.con;134.5;135.5;138.5;145.5;150.5;151.5;152.5;153.5;172.4;198.5;130.4;131.5;132.4;179.4;180.4;.inv;814!954BBE923;1511!95E7A79A7;353!960F7480D;693!962A14949;971!95E7A79C3;1634!95D28449D;913!95ECD164A;59!94081A119;57!94406E1D6;63!972857EBB;7!97285179C;1!972859C3F;1!972851794;1!972856627;834!9782A782F;238!972852E05;2140!94AC5D490;895!973E4F43C;130!973E69404;5695!97286F68A;1!972859C3A;183!95E94F922;7!95ECC7F9B;1!95E9E264C;101!95E94F974;1!95E94F90B;3!95E94F98C;415!95E8F4723;3491!95E8F472F;672!958AB7E88;693!95DBEA739;8239!960F7489E;1410!947688E13;1!947687D3C;1!94768B639;1!947682958;1!94769920E;18!947699208;179!947684FAB;319!94769E014;150!947688E12;1365!95ECC8041;151!95E94FAB6;3018!9736D4F0F;14647!97088AA44;898!970892625;2662v35b1708b4946!97085B9C2;7140!9708925BD;13!97091DA2E;4114v0b56!9708900F8;4018!95E94E83C;694!94760EC0A;1257v0b2!9708CEC43;10133!96A8CC7AE;2561!96A8C77AD;575!97371788E;9423!973D23324;13618!9715CAC7F;1376!9429A2B28;51!9715CAC83;11118v10b-3081!953B17961;611!97829304D;1771!95CBBBE6A;1!95A14EA96;1!95C5FC0B9;0!95E0737E0;2!95E74208E;5!9454CB3F2;1!95E073770;3!95E716953;314!95E07376A;1!95E09D252;3202v15b3021b539!9639773A6;188b-6472b807x133764y-773z19701!9564C544D;5!973DACB91;816!95ECBA0A6;118!95ECB90D3;64b-805!956561CEC;360!9408727DE;872!953A73962;2284v-15b4092!953C6D8CB;469v0b-23b1!9546B3B1D;215v0b-1!9539B6CA8;45v0b-1159!953B4DD44;62v0b1182!9541DFB29;64v15b1839b539!9708F26B8;3v0b-539b539!97092D55B;15v0b-539b539!97095F937;33v0b-539b539!97085B9BE;124!97088AA42;14v0b-539b539!9708C14FC;215!95CBBA637;63!95E858072;15b-5719b354b1702!954261A03;78b-2056b254b1802!95E75332B;47!953A7DC87;2!953C2E1E4;2!953A06B88;2!95476E411;2!9541BFC5F;9!95461D95F;1!95399A5C1;2943b-2056b354b1702!9542466BE;606b-2056b254b1802!95C616665;173b-2056b254b1802!95A2B568D;1700b-2056b335b1721!9540EA1B1;1b-2056b335b1721!9540EA1A1;1b-2056b335b1721!9540EA1A9;48!95C61B874;415b-145b2!95CB7227E;7b-4b5!95A2D9E2C;86!9605EFFA8;384!943A7E204;1095v-15b3268!95A2578D0;1v0b0!953A4A3A5;5v0b0!9547B5CA4;312!947D48E6E;47!9547EB7AF;266!953979A5C;140!95CB72281;452b-5182b2034b22!95460C49A;4b-2056b1995b61!95423A6B0;4b-2056b2034b22!95460C488;1172b-2056b353b1703!95E74F890;95!943A7E205;64!95E197E6B;3077!958DCA238;154b-2056b2056b33!955825136;4b-2089b2056b34!95583078A;1b-2090b2056b34!955850296;3b-2090b2056b34!95583AD70;13b-2090b2056b34!9558561F2;3b-2090b1864b226b2293!95583079A;3b-4383b1864b226b2293!955A569BA;1b-4383b2056b33!95580A349;1b-2089b2056b34!955848BF0;3b-2090b2056b33!95580A34C;0b-2089b2056b34!95583078D;3887b-2090b2056b101!956F9DEFB;14b-2151b1918b132!954C4AE64;338v15b3663!95EA67372;215!95A3098DF;2!955D4E926;41!96D1CB7F3;419b-5719b2056b82!954DA258B;7b-2138b2056b82!954DBF6DE;4b-2138b1864b274b2245!954DA2594;252b-4383b2056b82!954DAFEC1;204!96E26036C;1!9540CA16C;1!95644AB13;6!95D945E25;47!9533B8973;471!954CDD67D;29!966B1C690;547!9540CA169;104!947095542;2!947095541;1!947095540;460!966B1C692;733!966C71228;933!94875B295;740!94758CBF3;1505b-2087b3409b1b1383!945BB59B6;305!97242D3AC;1405!94A40F157;926!96A2A7E04;24!948C41D85;609!9481A87EE;93!948332AE5;1134!946A4F2E7;9!946A4E39D;1!94702BA03;3!946B50B48;452!974B32FBF;0!978CD12CA;188!96BBB5635;27b-4842b3312b339b261!963B3BD77;492!9646286C1;1892b-3859b3259b1058!9488F2ACD;313!966C3B469;365!95F7FAD68;533!966C62893;124!966D4268C;1!9655DBC9A;584!966CAD6C5;1!966CD58B3;6!966C9C748;653!965C08DCF;1!966C56C8B;1!965C0BC90;45!966C8DE3B;65!9655DBB50;256!952104715;6!954F71A34;163!966CD58AD;0b-4361b3303!966BBE75F;4b-3307b3307!9656029B9;76!966C62890;386!966C84222;252!966CB3435;21!966C3B46C;720!9655CD6EA;1!9655CD610;1051!971461E81;1!9553AA17E;2!96EDDBCB6;1!97956083D;1!973B2D39C;1!97524546F;16!96C129922;16!96EDD0B93;14!9716FD298;126!954F8D32A;387!97878D9BA;0!96471FC40;1!97878D9BC;1!96F5585D9;1!977B2DE68;1!953398206;8!96471FC41;0!97878D9BB;205!97724C9D8;0!9743A4D0B;4!97724CA16;2!977BE6B55;1!97485F524;1!976EBC30D;0!9783C5E1E;1!95E3BA0A1;0!97899A7C8;1!9789B572D;0!971311AF4;1!958BA187B;1!958BA18F7;1!960F746CE;0!958BA2940;1!958BA28FA;10!9743A4E26;20!97843F4B0;0!9782E7F34;0!973312095;0!974B1FA56;0!976AC1DD1;0!975908C19;0!96696A958;0!97735BDC0;0!97516090F;3!9736EBEF6;0!94B08134E;0!97735CDED;0!95DA3EC93;0!958B429DB;0!96C8FBE36;0!976EA09E1;0!9710D5A8B;0!96D731029;0!96196C600;2!9789A1096;2!972408555;0!952F89AB7;0!9723F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