new curve

5 年前 · e2217a9a
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--- a/Project/Assets/ML-Agents/Examples/Walker/Scripts/WalkerAgent.cs
+++ b/Project/Assets/ML-Agents/Examples/Walker/Scripts/WalkerAgent.cs

 public class WalkerAgent : Agent
 {
+    [Header("Walk Speed")]
-//    float m_maxWalkingSpeed = 15; //The max walking speed
+    float m_maxWalkingSpeed = 15; //The max walking speed
-    [Header("Target To Walk Towards")] [Space(10)]
+    [Header("Target To Walk Towards")]
-    [Header("Body Parts")] [Space(10)] public Transform hips;
+    [Header("Body Parts")] public Transform hips;
    public Transform chest;
    public Transform spine;
    public Transform head;
    JointDriveController m_JdController;

    EnvironmentParameters m_ResetParams;
-    private WalkGroup walkGroup;
+//    private WalkGroup walkGroup;
-        walkGroup = FindObjectOfType<WalkGroup>();
+//        walkGroup = FindObjectOfType<WalkGroup>();
        //Setup each body part
        m_JdController = GetComponent<JointDriveController>();
        m_JdController.SetupBodyPart(hips);

        rewardManager.ResetEpisodeRewards();
        
-//        walkingSpeed = randomizeWalkSpeedEachEpisode? Random.Range(0.0f, m_maxWalkingSpeed): walkingSpeed; //Random Walk Speed
+        walkingSpeed = randomizeWalkSpeedEachEpisode? Random.Range(0.0f, m_maxWalkingSpeed): walkingSpeed; //Random Walk Speed

        SetResetParameters();
    }

        var cubeForward = orientationCube.transform.forward;
 //        avgVelValue = GetVelocity();
-        //normalized value of the difference in avg speed vs target walking speed.
-        //value of 0 means we are matching velocity perfectly
-        //value of 1 means we are not matching velocity
+
-        sensor.AddObservation(VelocityInverseLerp( cubeForward * walkGroup.walkingSpeed));
-//        sensor.AddObservation(VelocityInverseLerp( cubeForward * walkingSpeed));
-        
+
+
+        //current ragdoll velocity. normalized 
+//        sensor.AddObservation(VelocityInverseLerp( cubeForward * walkGroup.walkingSpeed)); //
+        sensor.AddObservation(VelocityInverseLerp( cubeForward * walkingSpeed));
-//        sensor.AddObservation(walkingSpeed/m_maxWalkingSpeed);
-        sensor.AddObservation(walkGroup.walkingSpeed/walkGroup.m_maxWalkingSpeed);
+        //current speed goal. normalized.
+        sensor.AddObservation(walkingSpeed/m_maxWalkingSpeed);
+//        sensor.AddObservation(walkGroup.walkingSpeed/walkGroup.m_maxWalkingSpeed);
        sensor.AddObservation(Quaternion.FromToRotation(hips.forward, cubeForward));
        sensor.AddObservation(Quaternion.FromToRotation(head.forward, cubeForward));

        {
            counter++;
            velSum += item.rb.velocity;
-//            velSum += Mathf.Clamp(item.rb.velocity.magnitude, 0, m_maxWalkingSpeed);
-//            velSum += Vector3.ClampMagnitude(item.rb.velocity, m_maxWalkingSpeed);
        }
        avg = velSum/counter;
        return avg;
 //        velSum += m_JdController.bodyPartsDict[head].rb.velocity;
 //        avgVelValue = velSum/4;
 //        velInverseLerpVal = VelocityInverseLerp(cubeForward * walkingSpeed, avgVelValue);
-//        velInverseLerpVal = VelocityInverseLerp(cubeForward * walkingSpeed);
-        velInverseLerpVal = VelocityInverseLerp(cubeForward * walkGroup.walkingSpeed);
+        velInverseLerpVal = VelocityInverseLerp(cubeForward * walkingSpeed);
+//        velInverseLerpVal = VelocityInverseLerp(cubeForward * walkGroup.walkingSpeed);
        rewardManager.UpdateReward("productOfAllRewards", velInverseLerpVal * lookAtTargetReward);
 //        rewardManager.UpdateReward("productOfAllRewards", velInverseLerpVal * lookAtTargetReward * headHeightOverFeetReward);
 //            velInverseLerpVal = VelocityInverseLerp(Vector3.zero, cubeForward * walkingSpeed, avgVelValue);
 //        {
 //            counter++;
 //            velSum += item.rb.velocity;
-////            velSum += Mathf.Clamp(item.rb.velocity.magnitude, 0, m_maxWalkingSpeed);
-////            velSum += Vector3.ClampMagnitude(item.rb.velocity, m_maxWalkingSpeed);
 //        }
 //            avgVelValue = velSum/counter;
 //        //This reward will approach 1 if it matches and approach zero as it deviates
 //        rewardManager.UpdateReward("productOfAllRewards", matchSpeedReward * lookAtTargetReward * headHeightOverFeetReward);
        
    }
-    public Vector3 avgVelValue;
+    public Vector3 bodyVelocity;
+    //value of 0 means we are matching velocity perfectly
+    //value of 1 means we are not matching velocity
+    
+    //normalized value of the difference in avg speed vs goal walking speed.
-        avgVelValue = GetVelocity();
+        bodyVelocity = GetVelocity();
-        velDeltaDistance = Vector3.Distance(avgVelValue, velocityGoal);
-//        float percent = Mathf.InverseLerp(m_maxWalkingSpeed, 0, velDeltaDistance);
+//        velDeltaDistance = Vector3.Distance(avgVelValue, velocityGoal);
+//        velDeltaDistance = Vector3.Distance(avgVelValue, velocityGoal);
+//        velDeltaDistance = Mathf.Clamp(Vector3.Distance(bodyVelocity, velocityGoal), 0, walkGroup.walkingSpeed);
+        velDeltaDistance = Mathf.Clamp(Vector3.Distance(bodyVelocity, velocityGoal), 0, walkingSpeed);
-        float percent = Mathf.InverseLerp(velocityGoal.magnitude, 0, velDeltaDistance);
+//        float percent = Mathf.InverseLerp(velocityGoal.magnitude, 0, velDeltaDistance);
+//        float percent = Mathf.Pow(1 - Mathf.Pow(velDeltaDistance/walkGroup.walkingSpeed, 2), 2);
+        float percent = Mathf.Pow(1 - Mathf.Pow(velDeltaDistance/walkingSpeed, 2), 2);
+//    public float VelocityInverseLerp(Vector3 velocityGoal)
+//    {
+//        avgVelValue = GetVelocity();
+//
+//        velDeltaDistance = Vector3.Distance(avgVelValue, velocityGoal);
+////        float percent = Mathf.InverseLerp(walkingSpeed, 0, velDeltaDistance);
+//        float percent = Mathf.InverseLerp(velocityGoal.magnitude, 0, velDeltaDistance);
+//        return percent;
+//    }
    
 //    public float VelocityInverseLerp(Vector3 velocityGoal, Vector3 currentVel)
 //    {
-////        float percent = Mathf.InverseLerp(m_maxWalkingSpeed, 0, velDeltaDistance);
 //        float percent = Mathf.InverseLerp(walkingSpeed, 0, velDeltaDistance);
 //        return percent;
 //    }
--- a/config/ppo/WalkerDynamic.yaml
+++ b/config/ppo/WalkerDynamic.yaml
        gamma: 0.995
        strength: 1.0
    keep_checkpoints: 5
-    max_steps: 20000000
+    max_steps: 25000000
    time_horizon: 1000
    summary_freq: 30000
    threaded: true