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狼 追逐 V8

夏洛爾 | 2023-02-12 00:03:40 | 巴幣 0 | 人氣 123

Wolf Run V8

實驗目標:
1.進入靜立狀態後,進入追逐狀態,在追逐狀態下,要能持續跑至接近目標的距離內
2.尺寸介於1-5倍

實驗設計:
1.任何弱點觸地皆失敗 (尾巴和四個小腿並非是弱點)
2.非弱點肢體
if(wolfBodies[i].damageCoef > 0f){clampReward += -0.01f * wolfBodies[i].damageCoef;}
3.
//Set: judge.endEpisode = true//Set: judge.episodeLength = 30f//Set: useClampReward = true//Set: SharpingBuffer Len=250 Th=-0.4if(weaknessOnGround){if(inferenceMode){brainMode = BrainMode.GetUp;SetModel("WolfGetUp", getUpBrain);behaviorParameters.BehaviorType = BehaviorType.InferenceOnly;}else{AddReward(-1f);judge.outLife++;judge.Reset();return;}}else if(wolfRoot.localPosition.y < -10f){if(inferenceMode){brainMode = BrainMode.GetUp;SetModel("WolfGetUp", getUpBrain);behaviorParameters.BehaviorType = BehaviorType.InferenceOnly;}else{AddReward(-1f);judge.outY++;judge.Reset();return;}}else{targetSmoothPosition = targetPositionBuffer.GetSmoothVal();headDir = targetSmoothPosition - stageBase.InverseTransformPoint(wolfHeadRb.position);rootDir = targetSmoothPosition - stageBase.InverseTransformPoint(wolfRootRb.position);flatTargetVelocity = rootDir;flatTargetVelocity.y = 0f;targetDistance = flatTargetVelocity.magnitude;Vector3 forwardDir = flatTargetVelocity.normalized;Vector3 flatLeftDir = Vector3.Cross(flatTargetVelocity, Vector3.up);lookAngle = Mathf.InverseLerp(180f, 0f, Vector3.Angle(wolfHead.right * -1f, headDir));//SideUpupAngle = Mathf.InverseLerp(180f, 0f, Vector3.Angle(wolfHead.forward, flatLeftDir));aimVelocity = flatTargetVelocity.normalized;aimVelocity.y = 0.3f;spineUpAngle = Mathf.InverseLerp(180f, 0f, Vector3.Angle(wolfSpine.up*-1f, Vector3.up));//SideLookspineLookAngle = Mathf.InverseLerp(180f, 0f, Vector3.Angle(wolfSpine.forward, flatLeftDir));rootUpAngle = Mathf.InverseLerp(180f, 0f, Vector3.Angle(wolfRoot.up, Vector3.up));//SideLookrootLookAngle = Mathf.InverseLerp(180f, 0f, Vector3.Angle(wolfRoot.right*-1f, flatLeftDir));leftThighAngle = Mathf.InverseLerp(180f, 0f, Vector3.Angle(wolfLeftThigh.forward * -1f, flatLeftDir));rightThighAngle = Mathf.InverseLerp(180f, 0f, Vector3.Angle(wolfRightThigh.forward * -1f, flatLeftDir));//For Sync runVector3 leftThighUpDir = Vector3.ProjectOnPlane(wolfLeftThigh.right, flatLeftDir);Vector3 rightThighUpDir = Vector3.ProjectOnPlane(wolfRightThigh.right, flatLeftDir);float thighUpAngle = Mathf.InverseLerp(180f, 0f, Vector3.Angle(leftThighUpDir, rightThighUpDir));leftUpperArmAngle = Mathf.InverseLerp(180f, 0f, Vector3.Angle(wolfLeftUpperArm.forward * -1f, flatLeftDir));rightUpperArmAngle = Mathf.InverseLerp(180f, 0f, Vector3.Angle(wolfRightUpperArm.forward * -1f, flatLeftDir));//For Sync runVector3 leftUpperArmUpDir = Vector3.ProjectOnPlane(wolfLeftUpperArm.right, flatLeftDir);Vector3 rightUpperArmUpDir = Vector3.ProjectOnPlane(wolfRightUpperArm.right, flatLeftDir);float upperArmUpAngle = Mathf.InverseLerp(180f, 0f, Vector3.Angle(leftUpperArmUpDir, rightUpperArmUpDir));tailAngle = Mathf.InverseLerp(180f, 0f, Vector3.Angle(wolfTail.right, flatTargetVelocity));avgVelocity = velocityBuffer.GetSmoothVal();velocityAngle = Vector3.Angle(avgVelocity, aimVelocity);velocityAngleCoef = Mathf.InverseLerp(180f, 0f, velocityAngle);flatVelocity = avgVelocity;flatVelocity.y = 0f;flatVelocityManitude = flatVelocity.magnitude;velocityCoef = Mathf.InverseLerp(0f, 10f*currentSize, Vector3.Project(avgVelocity, aimVelocity).magnitude );flatVelocityAngle = Vector3.Angle(flatVelocity, flatTargetVelocity);if(!inferenceMode){if(targetDistance > nearModeRange){if(Time.fixedTime - landingMoment > landingBufferTime){bool outSpeed = flatVelocityManitude < Mathf.Lerp(0f, 7f*currentSize, (Time.fixedTime - landingMoment - landingBufferTime)/10f);bool outDirection = flatVelocityAngle > Mathf.Lerp(180f, 10f, (Time.fixedTime - landingMoment - landingBufferTime)/5f);// float motionLimit = Mathf.Lerp(0.0f, 0.7f, (Time.fixedTime - landingMoment - landingBufferTime)/5f);// float motionLimit3 = Mathf.Lerp(0.0f, 0.8f, (Time.fixedTime - landingMoment - landingBufferTime)/5f);float motionLimit2 = Mathf.Lerp(0f, 0.7f, (Time.fixedTime - landingMoment - landingBufferTime)/5f);float sharpingResetVal = Mathf.Lerp(0f, sharpingResetThreshould, (Time.fixedTime - landingMoment - landingBufferTime - 2f)/5f);// bool outMotion = lookAngle < motionLimit // || upAngle < motionLimit // || leftThighAngle < motionLimit2 // || rightThighAngle < motionLimit2 // || spineLookAngle < motionLimit // || rootLookAngle < motionLimit// || spineUpAngle < motionLimit3 // || rootUpAngle < motionLimit3 // || thighUpAngle < motionLimit2 // || upperArmUpAngle < motionLimit2;// || leftUpperArmAngle < motionLimit2 // || rightUpperArmAngle < motionLimit2;bool outMotion = thighUpAngle < motionLimit2 || upperArmUpAngle < motionLimit2;if( outSpeed || outDirection || outMotion){// AddReward(-1f);if(outSpeed){#if UNITY_EDITORDebug.Log("outSpeed");#endifclampReward += -0.1f;judge.outSpeed++;}if(outDirection){#if UNITY_EDITORDebug.Log("outDirection");#endifclampReward += -0.1f;judge.outDirection++;}if(outMotion){#if UNITY_EDITORDebug.Log("outMotion");#endifclampReward += -0.1f;judge.outMotion++;}sharpingBuffer.PushVal(-1f);// judge.Reset();// return;}else{sharpingBuffer.PushVal(0f);}#if UNITY_EDITORsharpingVal = sharpingBuffer.GetSmoothVal();#endifif( sharpingBuffer.GetSmoothVal() < sharpingResetVal){AddReward(-0.7f);judge.Reset();return;}}if(useStep){if(IsOverSteps()){// AddReward(-0.5f);judge.outY++;judge.Reset();return;}else{AverageSteps( 0.3f * Time.fixedDeltaTime);}}else{useStep = true;ResetSteps();}bool isFalling = avgVelocity.y < 0f;// bool isFalling = false;if(isFalling){lastReward = 0f;}else{lastReward = velocityCoef * (0.1f + velocityAngleCoef * 0.02f+ (lookAngle+upAngle) * 0.01f + (leftThighAngle+rightThighAngle+leftUpperArmAngle+rightUpperArmAngle) * 0.0025f+ (spineLookAngle+rootLookAngle+spineUpAngle+rootUpAngle) * 0.005f+ (tailAngle) * 0.005f+ (thighUpAngle + upperArmUpAngle) * 0.005f+ (1f - exertionRatio) * 0.005f);}if(useClampReward){lastReward = lastReward+clampReward;if(lastReward < 0f) lastReward = 0f;}totalReward += lastReward;AddReward( lastReward );}// else if(targetDistance > 1.5f)else{// SetReward(1f);judge.survived++;judge.Reset();return;}}}

//大致來說,
--1.獎勵視線
--2.獎勵投影至"跑動推薦向量"的速度和角度,並使用Force Sharping
--3.獎勵四個大腿的Side Look
--4.獎勵尾巴符合指定角度
--5.獎勵減少動作變化
--6.獎勵雙手和雙足要同步奔跑
--7.引導身體要盡量平行地面
--8.速度要求正比尺寸

4.Force Sharping改為有容錯空間,但是容許值逆向Sharping
允許角色在5秒內發生總計2秒以內的失誤,希望藉此讓角色就算輕微失衡也能嘗試自行修正
但是容許值是逆向Sharping,會在開始Force Sharping後兩秒才逐步放寬標準

5.四腳需輪流著地

6.只有上昇過程可以得分,以避免狼試圖滑翔然後墜機

實驗時間:
Step: 5e8
Time Elapsed: 101116s (28.09hr)

實驗結果:
實驗結果為較好的失敗

狼有明確的提升速度了,但仍然是交錯跑

然後同時有觀察到,不同比例的狼跑動能力有顯著差異

因此可以假設
1.速度為主的係數雖然可能催生速度,但會隱藏對雙腳同步的引導
2.在雙腳同步引導被隱藏的情況下,就算對雙腳未同步進行Force Sharping也沒有引導往理想發向前進的誘因
3.不同尺寸造就的情況可能對於ML來說複雜其實相當高

因此下個實驗
1.得分係數恢復成獨立分配,但是拉高速度和雙腳同步的係數
2.雖然違反實驗目標,但是暫時將尺寸鎖定在1

然後根據情況,下下個實驗將考慮使用num_layer = 4 或增加hidden_units

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