New Variable Speed Walker Environments (#4301)

* init

* Add reward manager and hurryUpReward

* fix hurry reward/ add awful first training

* Turn off head height and hurry rew

* changed max speed to 15. added small hh rew

* add NaN check for reward manager. start vel penalty

* add bpVel pen

* add new BPVelPen nn file

* remove outdated nn file

* add randomize speed bool

* try rewad product

* change coeff to 1

* try avg vel of all bp for reward

* move outside loop

* try linear inverselerp for vel

* add avg rew matchspeed15 nn file. looks much better

* save scene

* no hand penalty, random walk speed

* fix inverse lerp

* try new reward falloff

* cleanup

* added new nn file. don't allow hand contact

* update obsv

* remove hh rew. add trained no-hh model

* add new nn file

* new curve

* add new models. try no reset

* add hh rew

* clamp hh

* zero rewards if ground contact

* switch to approved with movi...

Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/DirectionIndicator.cs

-using System.Collections;
-using System.Collections.Generic;
-using UnityEngine;
+using UnityEngine;
+       
+        public bool updatedByAgent; //should this be updated by the agent? If not, it will use local settings
-        
+
         void OnEnable()
         {
             m_StartingYPos = transform.position.y;
         {
-            transform.position = new Vector3(transformToFollow.position.x, m_StartingYPos + heightOffset, transformToFollow.position.z);
+            if (updatedByAgent)
+                return;
+            transform.position = new Vector3(transformToFollow.position.x, m_StartingYPos + heightOffset,
+                transformToFollow.position.z);
+        }
+
+        //Public method to allow an agent to directly update this component
+        public void MatchOrientation(Transform t)
+        {
+            transform.position = new Vector3(t.position.x, m_StartingYPos + heightOffset, t.position.z);
+            transform.rotation = t.rotation;
         }
     }
 }

Project/Assets/ML-Agents/Examples/Walker/Scenes/WalkerDynamic.unity.meta

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Project/Assets/ML-Agents/Examples/Walker/Scripts/WalkerAgent.cs

 using System;
-using MLAgentsExamples;
 using UnityEngine;
 using Unity.MLAgents;
 using Unity.MLAgentsExamples;
 
 public class WalkerAgent : Agent
 {
-    public float maximumWalkingSpeed = 999; //The max walk velocity magnitude an agent will be rewarded for
-    Vector3 m_WalkDir; //Direction to the target
-//    Quaternion m_WalkDirLookRot; //Will hold the rotation to our target
+    [Header("Walk Speed")]
+    [Range(0.1f, 10)]
+    [SerializeField]
+    //The walking speed to try and achieve
+    private float m_TargetWalkingSpeed = 10;
+
+    public float MTargetWalkingSpeed // property
+    {
+        get { return m_TargetWalkingSpeed; }
+        set { m_TargetWalkingSpeed = Mathf.Clamp(value, .1f, m_maxWalkingSpeed); }
+    }
+
+    const float m_maxWalkingSpeed = 10; //The max walking speed
+
+    //Should the agent sample a new goal velocity each episode?
+    //If true, walkSpeed will be randomly set between zero and m_maxWalkingSpeed in OnEpisodeBegin() 
+    //If false, the goal velocity will be walkingSpeed
+    public bool randomizeWalkSpeedEachEpisode;
+
+    //The direction an agent will walk during training.
+    private Vector3 m_WorldDirToWalk = Vector3.right;
-    [Header("Target To Walk Towards")] [Space(10)]
-    public TargetController target; //Target the agent will walk towards.
+    [Header("Target To Walk Towards")] public Transform target; //Target the agent will walk towards during training.
-    [Header("Body Parts")] [Space(10)] public Transform hips;
+    [Header("Body Parts")] public Transform hips;
     public Transform chest;
     public Transform spine;
     public Transform head;
     public Transform forearmR;
     public Transform handR;
 
-    [Header("Orientation")] [Space(10)]
-    public OrientationCubeController orientationCube;
+    OrientationCubeController m_OrientationCube;
+    //The indicator graphic gameobject that points towards the target
+    DirectionIndicator m_DirectionIndicator;
-
-        orientationCube.UpdateOrientation(hips, target.transform);
+        m_OrientationCube = GetComponentInChildren<OrientationCubeController>();
+        m_DirectionIndicator = GetComponentInChildren<DirectionIndicator>();
 
         //Setup each body part
         m_JdController = GetComponent<JointDriveController>();
         }
 
         //Random start rotation to help generalize
-        transform.rotation = Quaternion.Euler(0, Random.Range(0.0f, 360.0f), 0);
+        hips.rotation = Quaternion.Euler(0, Random.Range(0.0f, 360.0f), 0);
+
+        UpdateOrientationObjects();
-        orientationCube.UpdateOrientation(hips, target.transform);
+        //Set our goal walking speed
+        MTargetWalkingSpeed =
+            randomizeWalkSpeedEachEpisode ? Random.Range(0.1f, m_maxWalkingSpeed) : MTargetWalkingSpeed;
 
         SetResetParameters();
     }
 
         //Get velocities in the context of our orientation cube's space
         //Note: You can get these velocities in world space as well but it may not train as well.
-        sensor.AddObservation(orientationCube.transform.InverseTransformDirection(bp.rb.velocity));
-        sensor.AddObservation(orientationCube.transform.InverseTransformDirection(bp.rb.angularVelocity));
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformDirection(bp.rb.velocity));
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformDirection(bp.rb.angularVelocity));
-        sensor.AddObservation(orientationCube.transform.InverseTransformDirection(bp.rb.position - hips.position));
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformDirection(bp.rb.position - hips.position));
 
         if (bp.rb.transform != hips && bp.rb.transform != handL && bp.rb.transform != handR)
         {
     /// </summary>
     public override void CollectObservations(VectorSensor sensor)
     {
-        sensor.AddObservation(Quaternion.FromToRotation(hips.forward, orientationCube.transform.forward));
-        sensor.AddObservation(Quaternion.FromToRotation(head.forward, orientationCube.transform.forward));
+        var cubeForward = m_OrientationCube.transform.forward;
-        sensor.AddObservation(orientationCube.transform.InverseTransformPoint(target.transform.position));
+        //velocity we want to match
+        var velGoal = cubeForward * MTargetWalkingSpeed;
+        //ragdoll's avg vel
+        var avgVel = GetAvgVelocity();
+
+        //current ragdoll velocity. normalized 
+        sensor.AddObservation(Vector3.Distance(velGoal, avgVel));
+        //avg body vel relative to cube
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformDirection(avgVel));
+        //vel goal relative to cube
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformDirection(velGoal));
+
+        //rotation deltas
+        sensor.AddObservation(Quaternion.FromToRotation(hips.forward, cubeForward));
+        sensor.AddObservation(Quaternion.FromToRotation(head.forward, cubeForward));
+
+        //Position of target position relative to cube
+        sensor.AddObservation(m_OrientationCube.transform.InverseTransformPoint(target.transform.position));
 
         foreach (var bodyPart in m_JdController.bodyPartsList)
         {
         bpDict[forearmR].SetJointStrength(vectorAction[++i]);
     }
 
+    //Update OrientationCube and DirectionIndicator
+    void UpdateOrientationObjects()
+    {
+        m_WorldDirToWalk = target.position - hips.position;
+        m_OrientationCube.UpdateOrientation(hips, target);
+        if (m_DirectionIndicator)
+        {
+            m_DirectionIndicator.MatchOrientation(m_OrientationCube.transform);
+        }
+    }
+
-        var cubeForward = orientationCube.transform.forward;
-        orientationCube.UpdateOrientation(hips, target.transform);
+        UpdateOrientationObjects();
+
+        var cubeForward = m_OrientationCube.transform.forward;
+
-        // a. Velocity alignment with goal direction.
-        var moveTowardsTargetReward = Vector3.Dot(cubeForward,
-            Vector3.ClampMagnitude(m_JdController.bodyPartsDict[hips].rb.velocity, maximumWalkingSpeed));
-        if (float.IsNaN(moveTowardsTargetReward))
+        // a. Match target speed
+        //This reward will approach 1 if it matches perfectly and approach zero as it deviates
+        var matchSpeedReward = GetMatchingVelocityReward(cubeForward * MTargetWalkingSpeed, GetAvgVelocity());
+
+        //Check for NaNs
+        if (float.IsNaN(matchSpeedReward))
-                $" cubeForward: {cubeForward}\n"+
-                $" hips.velocity: {m_JdController.bodyPartsDict[hips].rb.velocity}\n"+
-                $" maximumWalkingSpeed: {maximumWalkingSpeed}"
+                $" cubeForward: {cubeForward}\n" +
+                $" hips.velocity: {m_JdController.bodyPartsDict[hips].rb.velocity}\n" +
+                $" maximumWalkingSpeed: {m_maxWalkingSpeed}"
-        // b. Rotation alignment with goal direction.
-        var lookAtTargetReward = Vector3.Dot(cubeForward, head.forward);
+        // b. Rotation alignment with target direction.
+        //This reward will approach 1 if it faces the target direction perfectly and approach zero as it deviates
+        var lookAtTargetReward = (Vector3.Dot(cubeForward, head.forward) + 1) * .5F;
+
+        //Check for NaNs
-                $" cubeForward: {cubeForward}\n"+
+                $" cubeForward: {cubeForward}\n" +
-        // c. Encourage head height. //Should normalize to ~1
-        var headHeightOverFeetReward =
-            ((head.position.y - footL.position.y) + (head.position.y - footR.position.y) / 10);
-        if (float.IsNaN(headHeightOverFeetReward))
+        AddReward(matchSpeedReward * lookAtTargetReward);
+    }
+
+    //Returns the average velocity of all of the body parts
+    //Using the velocity of the hips only has shown to result in more erratic movement from the limbs, so...
+    //...using the average helps prevent this erratic movement
+    Vector3 GetAvgVelocity()
+    {
+        Vector3 velSum = Vector3.zero;
+        Vector3 avgVel = Vector3.zero;
+
+        //ALL RBS
+        int numOfRB = 0;
+        foreach (var item in m_JdController.bodyPartsList)
-            throw new ArgumentException(
-                "NaN in headHeightOverFeetReward.\n" +
-                $" head.position: {head.position}\n"+
-                $" footL.position: {footL.position}\n"+
-                $" footR.position: {footR.position}"
-            );
+            numOfRB++;
+            velSum += item.rb.velocity;
-        AddReward(
-            + 0.02f * moveTowardsTargetReward
-            + 0.02f * lookAtTargetReward
-            + 0.005f * headHeightOverFeetReward
-        );
+        avgVel = velSum / numOfRB;
+        return avgVel;
+    }
+
+    //normalized value of the difference in avg speed vs goal walking speed.
+    public float GetMatchingVelocityReward(Vector3 velocityGoal, Vector3 actualVelocity)
+    {
+        //distance between our actual velocity and goal velocity
+        var velDeltaMagnitude = Mathf.Clamp(Vector3.Distance(actualVelocity, velocityGoal), 0, MTargetWalkingSpeed);
+
+        //return the value on a declining sigmoid shaped curve that decays from 1 to 0
+        //This reward will approach 1 if it matches perfectly and approach zero as it deviates
+        return Mathf.Pow(1 - Mathf.Pow(velDeltaMagnitude / MTargetWalkingSpeed, 2), 2);
     }
 
     /// <summary>

Project/Assets/ML-Agents/Examples/Walker/TFModels/WalkerDynamic.nn.meta

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Project/Assets/ML-Agents/Examples/Walker/TFModels/WalkerStatic.nn.meta

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config/ppo/WalkerDynamic.yaml

         gamma: 0.995
         strength: 1.0
     keep_checkpoints: 5
-    max_steps: 20000000
+    max_steps: 30000000
     time_horizon: 1000
     summary_freq: 30000
     threaded: true

config/ppo/WalkerStatic.yaml

         gamma: 0.995
         strength: 1.0
     keep_checkpoints: 5
-    max_steps: 20000000
+    max_steps: 30000000
     time_horizon: 1000
     summary_freq: 30000
     threaded: true

docs/Learning-Environment-Examples.md

 - Set-up: Physics-based Humanoid agents with 26 degrees of freedom. These DOFs
   correspond to articulation of the following body-parts: hips, chest, spine,
   head, thighs, shins, feet, arms, forearms and hands.
-- Goal: The agents must move its body toward the goal direction as quickly as
-  possible without falling.
-  - `WalkerStatic` - Goal direction is always forward.
+- Goal: The agents must move its body toward the goal direction without falling.
+  - `WalkerDynamicVariableSpeed`- Goal direction and walking speed are randomized. 
+  - `WalkerStatic` - Goal direction is always forward.
+  - `WalkerStaticVariableSpeed` - Goal direction is always forward. Walking
+     speed is randomized
-  - +0.02 times body velocity in the goal direction. (run towards target)
-  - +0.01 times head direction alignment with goal direction. (face towards target)
-  - +0.005 times head y position - left foot y position. (encourage head height)
-  - +0.005 times head y position - right foot y position. (encourage head height)
+  The reward function is now geometric meaning the reward each step is a product
+  of all the rewards instead of a sum, this helps the agent try to maximize all
+  rewards instead of the easiest rewards.
+  - Body velocity matches goal velocity. (normalized between (0,1))
+  - Head direction alignment with goal direction. (normalized between (0,1))
-  - Vector Observation space: 236 variables corresponding to position, rotation,
+  - Vector Observation space: 243 variables corresponding to position, rotation,
     velocity, and angular velocities of each limb, along with goal direction.
   - Vector Action space: (Continuous) Size of 39, corresponding to target
     rotations and strength applicable to the joints.
     - Recommended Minimum:
     - Recommended Maximum:
   - hip_mass: Mass of the hip component of the walker
-    - Default: 15
+    - Default: 8
     - Recommended Minimum: 7
     - Recommended Maximum: 28
   - chest_mass: Mass of the chest component of the walker
   - spine_mass: Mass of the spine component of the walker
-    - Default: 10
+    - Default: 8
-- Benchmark Mean Reward for `WalkerStatic`: 1500
-- Benchmark Mean Reward for `WalkerDynamic`: 700
+- Benchmark Mean Reward for `WalkerDynamic`: 2500
+- Benchmark Mean Reward for `WalkerDynamicVariableSpeed`: 2500
+- Benchmark Mean Reward for `WalkerStatic`: 3500
+- Benchmark Mean Reward for `WalkerStaticVariableSpeed`: 3500
+
+
 
 ## Pyramids
 

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