benson V5 har cooket

Assets/ML-Agents/Timers/AI training_timers.json

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Assets/Prefabs/Training Areas/TrainingV4.prefab

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Assets/Scripts/AgentControllerV5.cs

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+using System.Collections;
+using System.Collections.Generic;
+using UnityEngine;
+using Unity.MLAgents; 
+using Unity.MLAgents.Sensors; 
+using Unity.MLAgents.Actuators;
+using UnityEngine.UIElements;
+using System.Linq;
+using Unity.Mathematics;
+using Unity.VisualScripting;
+using System.Reflection;
+using System;
+
+public class AgentControllerV5 : Agent
+{
+    public float motorTorque = 300;
+    public float brakeTorque = 500;
+    public float maxSpeed = 400;
+    public float steeringRange = 9;
+    public float steeringRangeAtMaxSpeed = 7;
+    public float autoBrake = 100;
+    WheelControl[] wheels;
+    Rigidbody rigidBody;
+    public List<GameObject> checkpoints;
+    Vector3 startPosition;
+    Quaternion startRotation;
+    int currentStep = 0;
+    float totalReward = 0;
+    float totalMentalPain = 0;
+    int stepsSinceCheckpoint = 0;
+    int checkpointsReached = 0;
+    public int maxStepsPerCheckpoint = 300;
+    public int distanceBetweenCheckpoints = 5;
+    public bool ignoreMentalPain = false;
+
+    // Start is called before the first frame update
+    void Start()
+    {
+        rigidBody = GetComponent<Rigidbody>();
+        // Find all child GameObjects that have the WheelControl script attached
+        wheels = GetComponentsInChildren<WheelControl>();
+
+        startPosition = transform.localPosition;
+        startRotation = transform.localRotation;
+        
+    }
+
+    public override void OnEpisodeBegin()
+    {
+        stepsSinceCheckpoint = 0;
+        checkpointsReached = 0;
+        totalReward = 0;
+        totalMentalPain = 0;
+
+        // reset wheels
+        foreach (var wheel in wheels)
+        {
+            wheel.WheelCollider.brakeTorque = 0;
+            wheel.WheelCollider.motorTorque = 0;
+            wheel.WheelCollider.steerAngle = 0;
+        }
+
+
+        // reset car
+        transform.localPosition = startPosition;
+        transform.localRotation = startRotation;
+        rigidBody.velocity = Vector3.zero;
+        rigidBody.angularVelocity = Vector3.zero;
+
+        // reset checkpoints
+        foreach (GameObject checkpoint in checkpoints)
+        {
+            checkpoint.GetComponent<Checkpoint>().isCollected = false;
+        }
+    }
+
+    public override void CollectObservations(VectorSensor sensor)
+    {
+        Transform currentCheckpoint = checkpoints[0].transform;
+        foreach (GameObject checkpoint in checkpoints)
+        {
+            bool isCollected = checkpoint.GetComponent<Checkpoint>().isCollected;
+
+            if (!isCollected)
+            {
+                currentCheckpoint = checkpoint.transform;
+                break;
+            }
+        }
+
+        // Agent rotation
+        sensor.AddObservation(transform.localRotation.y);
+
+        Vector3 position = transform.localPosition;
+        Vector3 checkpointPosition = currentCheckpoint.localPosition;
+
+        Vector2 toCheckpoint = new Vector2(
+            checkpointPosition.x - position.x,
+            checkpointPosition.z - position.z
+        );
+
+        // Normalized vector in direction of checkpoint and distancce to checkpoint.
+        sensor.AddObservation(toCheckpoint.normalized);
+        sensor.AddObservation(distanceToCheckpoint(currentCheckpoint));
+
+
+        Vector2 velocity = new Vector2(rigidBody.velocity.x, rigidBody.velocity.z);
+
+        // Velocity
+        sensor.AddObservation(velocity);
+
+        sensor.AddObservation(angleToCheckpoint(currentCheckpoint));
+    }
+
+    public override void OnActionReceived(ActionBuffers actions)
+    {
+        // Actions size = 2 [vertical speed, horizontal speed] = [-1..1, -1..1] // discrete = [{0, 1, 2}, {0, 1, 2}] = [{-1, 0, 1}...]
+        float vInput = 0;
+        float hInput = 0;
+
+        if (actions.DiscreteActions[0] == 0)
+            vInput = -1f;
+        if (actions.DiscreteActions[0] == 1)
+            vInput = 1f;
+
+        if (actions.DiscreteActions[1] == 0)
+            hInput = -1f;
+        if (actions.DiscreteActions[1] == 1)
+            hInput = 1f;
+
+        // give benson mental pain for existing (punishment for maximizing first checkpoint by standing still)
+        // AddReward(-0.002f);
+        AddReward(-0.0018f); // less pain because of V4
+        totalMentalPain -= 0.0018f;
+
+        if (ignoreMentalPain)
+            totalReward -= 0.0018f;
+
+        float forwardSpeed = Vector3.Dot(transform.forward, rigidBody.velocity);
+        // Calculate how close the car is to top speed
+        // as a number from zero to one
+        float speedFactor = Mathf.InverseLerp(0, maxSpeed / 4, forwardSpeed);
+
+        // Use that to calculate how much torque is available 
+        // (zero torque at top speed)
+        float currentMotorTorque = Mathf.Lerp(motorTorque, 0, speedFactor);
+
+        // …and to calculate how much to steer 
+        // (the car steers more gently at top speed)
+
+        float currentSteerRange = Mathf.Lerp(steeringRange, steeringRangeAtMaxSpeed, speedFactor);
+
+        // Check whether the user input is in the same direction 
+        // as the car's velocity
+        bool isAccelerating = Mathf.Sign(vInput) == Mathf.Sign(forwardSpeed);
+
+        bool isStopping = vInput == 0; // range
+
+        bool isBraking = (vInput < 0 && forwardSpeed > 0) || (vInput > 0 && forwardSpeed < 0);
+
+        if (vInput > 0 && forwardSpeed < 0)
+        {
+            isAccelerating = false;
+        }
+
+        foreach (var wheel in wheels)
+        {
+            // Apply steering to Wheel colliders that have "Steerable" enabled
+            if (wheel.steerable)
+            {
+                wheel.WheelCollider.steerAngle = hInput * currentSteerRange;
+            }
+
+            if (isBraking)
+            {
+                wheel.WheelCollider.brakeTorque = Mathf.Abs(vInput) * brakeTorque;
+                //wheel.WheelCollider.motorTorque = 0;
+            }
+            
+            if (isAccelerating)
+            {
+                // Apply torque to Wheel colliders that have "Motorized" enabled
+                if (wheel.motorized)
+                {
+                    wheel.WheelCollider.motorTorque = vInput * currentMotorTorque;
+                }
+                wheel.WheelCollider.brakeTorque = 0;
+            }
+
+            if (isStopping)
+            {
+                // If the user is trying to go in the opposite direction
+                // apply brakes to all wheels
+                wheel.WheelCollider.brakeTorque = Mathf.Abs(vInput) * brakeTorque + autoBrake;
+
+                if (forwardSpeed < 0)
+                {
+                    wheel.WheelCollider.brakeTorque = (Mathf.Abs(vInput) * brakeTorque + autoBrake) * 5;
+                }
+
+            }
+        }
+
+        // rewards
+        Transform currentCheckpoint = checkpoints[0].transform;
+        foreach (GameObject checkpoint in checkpoints)
+        {
+            bool isCollected = checkpoint.GetComponent<Checkpoint>().isCollected;
+
+            if (!isCollected)
+            {
+                currentCheckpoint = checkpoint.transform;
+                break;
+            }
+        }
+
+        float checkpintDistance = distanceToCheckpoint(currentCheckpoint);
+
+        float reward = (1 - Mathf.InverseLerp(0, distanceBetweenCheckpoints, checkpintDistance)) / 1000;
+
+        totalReward += reward;
+        AddReward(reward);
+
+        print(GetCumulativeReward().ToString());
+        
+
+        if (checkpintDistance < 0.1f)
+        {
+            currentCheckpoint.GetComponent<Checkpoint>().isCollected = true;
+            stepsSinceCheckpoint = 0;
+            checkpointsReached += 1;
+
+            // If last checkpoint
+            if (currentCheckpoint == checkpoints[checkpoints.Count - 1].transform)
+            {
+                AddReward(10f);
+                EndEpisode();           
+            }
+
+            //TODO fix variable names
+
+            AddReward(1f);
+            AddReward(-totalReward);
+
+            totalReward = 0;
+
+            print("checkpoint");
+        }
+
+        currentStep += 1;
+        stepsSinceCheckpoint += 1;
+
+        if (stepsSinceCheckpoint >= maxStepsPerCheckpoint)
+        {
+            stepsSinceCheckpoint = 0;
+            EndEpisode();
+        }
+    }
+
+    public override void Heuristic(in ActionBuffers actionsOut)
+    {
+        var discreteActionsOut = actionsOut.DiscreteActions;
+
+        discreteActionsOut[0] = 2;
+        discreteActionsOut[1] = 2;
+
+        if (Input.GetAxis("Vertical") < -0.5)
+            discreteActionsOut[0] = 0;
+        if (Input.GetAxis("Vertical") > 0.5)
+            discreteActionsOut[0] = 1;
+
+        if (Input.GetAxis("Horizontal") < -0.5)
+            discreteActionsOut[1] = 0;
+        if (Input.GetAxis("Horizontal") > 0.5)
+            discreteActionsOut[1] = 1;
+    }
+
+    // finds distance from agent to closest point on the checkpoint line
+    float distanceToCheckpoint(Transform checkpoint)
+    {
+        var closestPoint = checkpoint.GetComponent<Collider>().ClosestPointOnBounds(transform.position);
+        var distanceToCheckpoint = Vector3.Distance(transform.position, closestPoint);
+        return distanceToCheckpoint;
+    }
+
+    float angleToCheckpoint(Transform checkpoint)
+    {
+        Vector3 checkpointDirection = checkpoint.localPosition - transform.localPosition;
+
+        float angle = Vector3.SignedAngle(transform.forward, checkpointDirection, Vector3.up);
+        return angle;
+    }
+}

Assets/Scripts/AgentControllerV5.cs.meta

@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 2a31c1c1fd9a962429d7742f2cd1ac61
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 

Assets/Scripts/AgentControllerV6.cs

@@ -0,0 +1,18 @@
+using System.Collections;
+using System.Collections.Generic;
+using UnityEngine;
+
+public class AgentControllerV6 : MonoBehaviour
+{
+    // Start is called before the first frame update
+    void Start()
+    {
+        
+    }
+
+    // Update is called once per frame
+    void Update()
+    {
+        
+    }
+}

Assets/Scripts/AgentControllerV6.cs.meta

@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: e504560e573e8634db5ced380452680f
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 

MLAgents/config/BensonImitationV3-1env.yaml

@@ -0,0 +1,65 @@
+behaviors:
+  BensonImitationV3:
+    trainer_type: ppo
+
+    hyperparameters:
+      # Hyperparameters common to PPO and SAC
+      batch_size: 50
+      buffer_size: 3000
+      learning_rate: 3.0e-4
+      learning_rate_schedule: linear
+
+      # PPO-specific hyperparameters
+      # Replaces the "PPO-specific hyperparameters" section above
+      beta: 5.0e-2
+      epsilon: 0.1
+      lambd: 0.95
+      num_epoch: 3
+
+    # Configuration of the neural network (common to PPO/SAC)
+    network_settings:
+      vis_encoder_type: simple
+      normalize: false
+      hidden_units: 128
+      num_layers: 2
+
+    # Trainer configurations common to all trainers
+    max_steps: 1.8e5
+    time_horizon: 64
+    summary_freq: 1800
+    keep_checkpoints: 5
+    checkpoint_interval: 100000
+    threaded: true
+    init_path: null
+
+    # behavior cloning
+    behavioral_cloning:
+      demo_path: 'c:\Users\noahk\Documents\Unity projects\Racesm\Assets\Demonstrations\BensonV3M.demo'
+      strength: 0.5
+      # steps: 150000
+      # batch_size: 512
+      # num_epoch: 3
+      # samples_per_update: 0
+
+    reward_signals:
+      # environment reward (default)
+      extrinsic:
+        strength: 1.0
+        gamma: 0.99
+
+      # curiosity module
+      curiosity:
+        strength: 0.02
+        gamma: 0.99
+        encoding_size: 256
+        learning_rate: 3.0e-4
+
+      # GAIL
+      gail:
+        strength: 0.5
+        # gamma: 0.99
+        # encoding_size: 128
+        demo_path: 'c:\Users\noahk\Documents\Unity projects\Racesm\Assets\Demonstrations\BensonV3M.demo'
+        # learning_rate: 3.0e-4
+        # use_actions: false
+        # use_vail: false

MLAgents/config/BensonImitationV3-5env.yaml

@@ -0,0 +1,65 @@
+behaviors:
+  BensonImitationV3:
+    trainer_type: ppo
+
+    hyperparameters:
+      # Hyperparameters common to PPO and SAC
+      batch_size: 50
+      buffer_size: 15000
+      learning_rate: 3.0e-4
+      learning_rate_schedule: linear
+
+      # PPO-specific hyperparameters
+      # Replaces the "PPO-specific hyperparameters" section above
+      beta: 5.0e-2
+      epsilon: 0.1
+      lambd: 0.95
+      num_epoch: 3
+
+    # Configuration of the neural network (common to PPO/SAC)
+    network_settings:
+      vis_encoder_type: simple
+      normalize: false
+      hidden_units: 128
+      num_layers: 2
+
+    # Trainer configurations common to all trainers
+    max_steps: 2.4e5
+    time_horizon: 64
+    summary_freq: 9000
+    keep_checkpoints: 5
+    checkpoint_interval: 100000
+    threaded: true
+    init_path: null
+
+    # behavior cloning
+    behavioral_cloning:
+      demo_path: 'c:\Users\noahk\Documents\Unity projects\Racesm\Assets\Demonstrations\BensonV3M.demo'
+      strength: 0.5
+      # steps: 150000
+      # batch_size: 512
+      # num_epoch: 3
+      # samples_per_update: 0
+
+    reward_signals:
+      # environment reward (default)
+      extrinsic:
+        strength: 1.0
+        gamma: 0.99
+
+      # curiosity module
+      curiosity:
+        strength: 0.02
+        gamma: 0.99
+        encoding_size: 256
+        learning_rate: 3.0e-4
+
+      # GAIL
+      gail:
+        strength: 0.5
+        # gamma: 0.99
+        # encoding_size: 128
+        demo_path: 'c:\Users\noahk\Documents\Unity projects\Racesm\Assets\Demonstrations\BensonV3M.demo'
+        # learning_rate: 3.0e-4
+        # use_actions: false
+        # use_vail: false

MLAgents/config/BensonImitationV3.yaml

@@ -0,0 +1,65 @@
+behaviors:
+  BensonImitationV3:
+    trainer_type: ppo
+
+    hyperparameters:
+      # Hyperparameters common to PPO and SAC
+      batch_size: 50
+      buffer_size: 3000
+      learning_rate: 3.0e-4
+      learning_rate_schedule: linear
+
+      # PPO-specific hyperparameters
+      # Replaces the "PPO-specific hyperparameters" section above
+      beta: 5.0e-2
+      epsilon: 0.1
+      lambd: 0.95
+      num_epoch: 3
+
+    # Configuration of the neural network (common to PPO/SAC)
+    network_settings:
+      vis_encoder_type: simple
+      normalize: false
+      hidden_units: 128
+      num_layers: 2
+
+    # Trainer configurations common to all trainers
+    max_steps: 1.0e6
+    time_horizon: 64
+    summary_freq: 10000
+    keep_checkpoints: 5
+    checkpoint_interval: 100000
+    threaded: true
+    init_path: null
+
+    # behavior cloning
+    behavioral_cloning:
+      demo_path: 'c:\Users\noahk\Documents\Unity projects\Racesm\Assets\Demonstrations\BensonV3M.demo'
+      strength: 0.5
+      # steps: 150000
+      # batch_size: 512
+      # num_epoch: 3
+      # samples_per_update: 0
+
+    reward_signals:
+      # environment reward (default)
+      extrinsic:
+        strength: 1.0
+        gamma: 0.99
+
+      # curiosity module
+      curiosity:
+        strength: 0.02
+        gamma: 0.99
+        encoding_size: 256
+        learning_rate: 3.0e-4
+
+      # GAIL
+      gail:
+        strength: 0.5
+        # gamma: 0.99
+        # encoding_size: 128
+        demo_path: 'c:\Users\noahk\Documents\Unity projects\Racesm\Assets\Demonstrations\BensonV3M.demo'
+        # learning_rate: 3.0e-4
+        # use_actions: false
+        # use_vail: false

MLAgents/config/BensonV4.yaml

@@ -0,0 +1,65 @@
+behaviors:
+  BensonV4:
+    trainer_type: ppo
+
+    hyperparameters:
+      # Hyperparameters common to PPO and SAC
+      batch_size: 50
+      buffer_size: 10240
+      learning_rate: 3.0e-4
+      learning_rate_schedule: linear
+
+      # PPO-specific hyperparameters
+      # Replaces the "PPO-specific hyperparameters" section above
+      beta: 5.0e-2
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+
+    # Configuration of the neural network (common to PPO/SAC)
+    network_settings:
+      vis_encoder_type: simple
+      normalize: false
+      hidden_units: 128
+      num_layers: 2
+
+    # Trainer configurations common to all trainers
+    max_steps: 5.0e6
+    time_horizon: 64
+    summary_freq: 10000
+    keep_checkpoints: 5
+    checkpoint_interval : 500000
+    threaded: false
+    init_path: null
+
+    # # behavior cloning
+    # behavioral_cloning:
+    #   demo_path: 'c:\Users\noahk\Documents\Unity projects\Racesm\Assets\Demonstrations\BensonV3M.demo'
+    #   strength: 0.5
+    #   # steps: 150000
+    #   # batch_size: 512
+    #   # num_epoch: 3
+    #   # samples_per_update: 0
+
+    reward_signals:
+      # environment reward (default)
+      extrinsic:
+        strength: 1.0
+        gamma: 0.99
+
+      # # curiosity module
+      # curiosity:
+      #   strength: 0.02
+      #   gamma: 0.99
+      #   encoding_size: 256
+      #   learning_rate: 3.0e-4
+
+      # # GAIL
+      # gail:
+      #   strength: 0.5
+      #   # gamma: 0.99
+      #   # encoding_size: 128
+      #   demo_path: 'c:\Users\noahk\Documents\Unity projects\Racesm\Assets\Demonstrations\BensonV3M.demo'
+      #   # learning_rate: 3.0e-4
+      #   # use_actions: false
+      #   # use_vail: false

MLAgents/config/BensonV5.yaml

@@ -0,0 +1,65 @@
+behaviors:
+  BensonV5:
+    trainer_type: ppo
+
+    hyperparameters:
+      # Hyperparameters common to PPO and SAC
+      batch_size: 50
+      buffer_size: 10240
+      learning_rate: 3.0e-4
+      learning_rate_schedule: linear
+
+      # PPO-specific hyperparameters
+      # Replaces the "PPO-specific hyperparameters" section above
+      beta: 5.0e-2
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+
+    # Configuration of the neural network (common to PPO/SAC)
+    network_settings:
+      vis_encoder_type: simple
+      normalize: false
+      hidden_units: 128
+      num_layers: 2
+
+    # Trainer configurations common to all trainers
+    max_steps: 3.0e7
+    time_horizon: 64
+    summary_freq: 10000
+    keep_checkpoints: 5
+    checkpoint_interval : 500000
+    threaded: false
+    init_path: null
+
+    # # behavior cloning
+    # behavioral_cloning:
+    #   demo_path: 'c:\Users\noahk\Documents\Unity projects\Racesm\Assets\Demonstrations\BensonV3M.demo'
+    #   strength: 0.5
+    #   # steps: 150000
+    #   # batch_size: 512
+    #   # num_epoch: 3
+    #   # samples_per_update: 0
+
+    reward_signals:
+      # environment reward (default)
+      extrinsic:
+        strength: 1.0
+        gamma: 0.99
+
+      # # curiosity module
+      # curiosity:
+      #   strength: 0.02
+      #   gamma: 0.99
+      #   encoding_size: 256
+      #   learning_rate: 3.0e-4
+
+      # # GAIL
+      # gail:
+      #   strength: 0.5
+      #   # gamma: 0.99
+      #   # encoding_size: 128
+      #   demo_path: 'c:\Users\noahk\Documents\Unity projects\Racesm\Assets\Demonstrations\BensonV3M.demo'
+      #   # learning_rate: 3.0e-4
+      #   # use_actions: false
+      #   # use_vail: false

MLAgents/config/BensonV6.yaml

@@ -0,0 +1,65 @@
+behaviors:
+  BensonV5:
+    trainer_type: ppo
+
+    hyperparameters:
+      # Hyperparameters common to PPO and SAC
+      batch_size: 50
+      buffer_size: 10240
+      learning_rate: 3.0e-4
+      learning_rate_schedule: linear
+
+      # PPO-specific hyperparameters
+      # Replaces the "PPO-specific hyperparameters" section above
+      beta: 5.0e-2
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+
+    # Configuration of the neural network (common to PPO/SAC)
+    network_settings:
+      vis_encoder_type: simple
+      normalize: false
+      hidden_units: 128
+      num_layers: 2
+
+    # Trainer configurations common to all trainers
+    max_steps: 3.0e6
+    time_horizon: 64
+    summary_freq: 10000
+    keep_checkpoints: 5
+    checkpoint_interval : 500000
+    threaded: false
+    init_path: null
+
+    # # behavior cloning
+    # behavioral_cloning:
+    #   demo_path: 'c:\Users\noahk\Documents\Unity projects\Racesm\Assets\Demonstrations\BensonV3M.demo'
+    #   strength: 0.5
+    #   # steps: 150000
+    #   # batch_size: 512
+    #   # num_epoch: 3
+    #   # samples_per_update: 0
+
+    reward_signals:
+      # environment reward (default)
+      extrinsic:
+        strength: 1.0
+        gamma: 0.99
+
+      # # curiosity module
+      # curiosity:
+      #   strength: 0.02
+      #   gamma: 0.99
+      #   encoding_size: 256
+      #   learning_rate: 3.0e-4
+
+      # # GAIL
+      # gail:
+      #   strength: 0.5
+      #   # gamma: 0.99
+      #   # encoding_size: 128
+      #   demo_path: 'c:\Users\noahk\Documents\Unity projects\Racesm\Assets\Demonstrations\BensonV3M.demo'
+      #   # learning_rate: 3.0e-4
+      #   # use_actions: false
+      #   # use_vail: false

MLAgents/config/imitation/Crawler.yaml

@@ -0,0 +1,39 @@
+behaviors:
+  Crawler:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 2024
+      buffer_size: 20240
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: true
+      hidden_units: 512
+      num_layers: 3
+      vis_encode_type: simple
+    reward_signals:
+      gail:
+        gamma: 0.99
+        strength: 1.0
+        network_settings:
+          normalize: true
+          hidden_units: 128
+          num_layers: 2
+          vis_encode_type: simple
+        learning_rate: 0.0003
+        use_actions: false
+        use_vail: false
+        demo_path: Project/Assets/ML-Agents/Examples/Crawler/Demos/ExpertCrawler.demo
+    keep_checkpoints: 5
+    max_steps: 10000000
+    time_horizon: 1000
+    summary_freq: 30000
+    behavioral_cloning:
+      demo_path: Project/Assets/ML-Agents/Examples/Crawler/Demos/ExpertCrawler.demo
+      steps: 50000
+      strength: 0.5
+      samples_per_update: 0

MLAgents/config/imitation/Hallway.yaml

@@ -0,0 +1,35 @@
+behaviors:
+  Hallway:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 128
+      buffer_size: 1024
+      learning_rate: 0.0003
+      beta: 0.01
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 128
+      num_layers: 2
+      vis_encode_type: simple
+      memory:
+        sequence_length: 64
+        memory_size: 256
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+      gail:
+        gamma: 0.99
+        strength: 0.01
+        learning_rate: 0.0003
+        use_actions: false
+        use_vail: false
+        demo_path: Project/Assets/ML-Agents/Examples/Hallway/Demos/ExpertHallway.demo
+    keep_checkpoints: 5
+    max_steps: 10000000
+    time_horizon: 64
+    summary_freq: 10000

MLAgents/config/imitation/PushBlock.yaml

@@ -0,0 +1,42 @@
+behaviors:
+  PushBlock:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 128
+      buffer_size: 2048
+      learning_rate: 0.0003
+      beta: 0.01
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+      gail:
+        gamma: 0.99
+        strength: 0.01
+        network_settings:
+          normalize: false
+          hidden_units: 128
+          num_layers: 2
+          vis_encode_type: simple
+        learning_rate: 0.0003
+        use_actions: false
+        use_vail: false
+        demo_path: Project/Assets/ML-Agents/Examples/PushBlock/Demos/ExpertPushBlock.demo
+    keep_checkpoints: 5
+    max_steps: 100000
+    time_horizon: 64
+    summary_freq: 60000
+    behavioral_cloning:
+      demo_path: Project/Assets/ML-Agents/Examples/PushBlock/Demos/ExpertPushBlock.demo
+      steps: 50000
+      strength: 1.0
+      samples_per_update: 0

MLAgents/config/imitation/Pyramids.yaml

@@ -0,0 +1,34 @@
+behaviors:
+  Pyramids:
+    trainer_type: ppo
+    time_horizon: 128
+    max_steps: 1.0e7
+    hyperparameters:
+      batch_size: 128
+      beta: 0.01
+      buffer_size: 2048
+      epsilon: 0.2
+      lambd: 0.95
+      learning_rate: 0.0003
+      num_epoch: 3
+    network_settings:
+      num_layers: 2
+      normalize: false
+      hidden_units: 512
+    reward_signals:
+      extrinsic:
+        strength: 1.0
+        gamma: 0.99
+      curiosity:
+        strength: 0.02
+        gamma: 0.99
+        network_settings:
+          hidden_units: 256
+      gail:
+        strength: 0.01
+        gamma: 0.99
+        demo_path: Project/Assets/ML-Agents/Examples/Pyramids/Demos/ExpertPyramid.demo
+    behavioral_cloning:
+      demo_path: Project/Assets/ML-Agents/Examples/Pyramids/Demos/ExpertPyramid.demo
+      strength: 0.5
+      steps: 150000

MLAgents/config/poca/DungeonEscape.yaml

@@ -0,0 +1,25 @@
+behaviors:
+  DungeonEscape:
+    trainer_type: poca
+    hyperparameters:
+      batch_size: 1024
+      buffer_size: 10240
+      learning_rate: 0.0003
+      beta: 0.01
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: constant
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 20000000
+    time_horizon: 64
+    summary_freq: 60000

MLAgents/config/poca/PushBlockCollab.yaml

@@ -0,0 +1,25 @@
+behaviors:
+  PushBlockCollab:
+    trainer_type: poca
+    hyperparameters:
+      batch_size: 1024
+      buffer_size: 10240
+      learning_rate: 0.0003
+      beta: 0.01
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: constant
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 15000000
+    time_horizon: 64
+    summary_freq: 60000

MLAgents/config/poca/SoccerTwos.yaml

@@ -0,0 +1,32 @@
+behaviors:
+  SoccerTwos:
+    trainer_type: poca
+    hyperparameters:
+      batch_size: 2048
+      buffer_size: 20480
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: constant
+    network_settings:
+      normalize: false
+      hidden_units: 512
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 50000000
+    time_horizon: 1000
+    summary_freq: 10000
+    self_play:
+      save_steps: 50000
+      team_change: 200000
+      swap_steps: 2000
+      window: 10
+      play_against_latest_model_ratio: 0.5
+      initial_elo: 1200.0

MLAgents/config/poca/StrikersVsGoalie.yaml

@@ -0,0 +1,63 @@
+behaviors:
+  Goalie:
+    trainer_type: poca
+    hyperparameters:
+      batch_size: 2048
+      buffer_size: 20480
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: constant
+    network_settings:
+      normalize: false
+      hidden_units: 512
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 30000000
+    time_horizon: 1000
+    summary_freq: 10000
+    self_play:
+      save_steps: 50000
+      team_change: 200000
+      swap_steps: 1000
+      window: 10
+      play_against_latest_model_ratio: 0.5
+      initial_elo: 1200.0
+  Striker:
+    trainer_type: poca
+    hyperparameters:
+      batch_size: 2048
+      buffer_size: 20480
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: constant
+    network_settings:
+      normalize: false
+      hidden_units: 512
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 30000000
+    time_horizon: 1000
+    summary_freq: 10000
+    self_play:
+      save_steps: 50000
+      team_change: 200000
+      swap_steps: 4000
+      window: 10
+      play_against_latest_model_ratio: 0.5
+      initial_elo: 1200.0

MLAgents/config/ppo/3DBall.yaml

@@ -0,0 +1,25 @@
+behaviors:
+  3DBall:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 64
+      buffer_size: 12000
+      learning_rate: 0.0003
+      beta: 0.001
+      epsilon: 0.2
+      lambd: 0.99
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: true
+      hidden_units: 128
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 500000
+    time_horizon: 1000
+    summary_freq: 12000

MLAgents/config/ppo/3DBallHard.yaml

@@ -0,0 +1,25 @@
+behaviors:
+  3DBallHard:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 120
+      buffer_size: 12000
+      learning_rate: 0.0003
+      beta: 0.001
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: true
+      hidden_units: 128
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 500000
+    time_horizon: 1000
+    summary_freq: 12000

MLAgents/config/ppo/3DBall_randomize.yaml

@@ -0,0 +1,36 @@
+behaviors:
+  3DBall:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 64
+      buffer_size: 12000
+      learning_rate: 0.0003
+      beta: 0.001
+      epsilon: 0.2
+      lambd: 0.99
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: true
+      hidden_units: 128
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 500000
+    time_horizon: 1000
+    summary_freq: 12000
+environment_parameters:
+  mass:
+    sampler_type: uniform
+    sampler_parameters:
+      min_value: 0.5
+      max_value: 10
+  scale:
+    sampler_type: uniform
+    sampler_parameters:
+      min_value: 0.75
+      max_value: 3

MLAgents/config/ppo/Basic.yaml

@@ -0,0 +1,25 @@
+behaviors:
+  Basic:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 32
+      buffer_size: 256
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 20
+      num_layers: 1
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.9
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 500000
+    time_horizon: 3
+    summary_freq: 2000

MLAgents/config/ppo/Crawler.yaml

@@ -0,0 +1,25 @@
+behaviors:
+  Crawler:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 2048
+      buffer_size: 20480
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: true
+      hidden_units: 512
+      num_layers: 3
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.995
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 10000000
+    time_horizon: 1000
+    summary_freq: 30000

MLAgents/config/ppo/FoodCollector.yaml

@@ -0,0 +1,25 @@
+behaviors:
+  GridFoodCollector:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 1024
+      buffer_size: 10240
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 1
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 2000000
+    time_horizon: 64
+    summary_freq: 10000

MLAgents/config/ppo/GridWorld.yaml

@@ -0,0 +1,25 @@
+behaviors:
+  GridWorld:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 32
+      buffer_size: 256
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 128
+      num_layers: 1
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.9
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 500000
+    time_horizon: 5
+    summary_freq: 20000

MLAgents/config/ppo/Hallway.yaml

@@ -0,0 +1,28 @@
+behaviors:
+  Hallway:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 128
+      buffer_size: 1024
+      learning_rate: 0.0003
+      beta: 0.03
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 128
+      num_layers: 2
+      vis_encode_type: simple
+      memory:
+        sequence_length: 64
+        memory_size: 128
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 10000000
+    time_horizon: 64
+    summary_freq: 10000

MLAgents/config/ppo/Match3.yaml

@@ -0,0 +1,48 @@
+default_settings:
+  trainer_type: ppo
+  hyperparameters:
+    batch_size: 16
+    buffer_size: 120
+    learning_rate: 0.0003
+    beta: 0.005
+    epsilon: 0.2
+    lambd: 0.99
+    num_epoch: 3
+    learning_rate_schedule: constant
+  network_settings:
+    normalize: true
+    hidden_units: 256
+    num_layers: 4
+    vis_encode_type: match3
+  reward_signals:
+    extrinsic:
+      gamma: 0.99
+      strength: 1.0
+  keep_checkpoints: 5
+  max_steps: 5000000
+  time_horizon: 128
+  summary_freq: 10000
+
+behaviors:
+  Match3SimpleHeuristic:
+    # Settings can be very simple since we don't care about actually training the model
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 16
+      buffer_size: 120
+    network_settings:
+      hidden_units: 4
+      num_layers: 1
+    max_steps: 5000000
+    summary_freq: 10000
+  Match3SmartHeuristic:
+    # Settings can be very simple since we don't care about actually training the model
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 16
+      buffer_size: 120
+    network_settings:
+      hidden_units: 4
+      num_layers: 1
+    max_steps: 5000000
+    summary_freq: 10000

MLAgents/config/ppo/PushBlock.yaml

@@ -0,0 +1,25 @@
+behaviors:
+  PushBlock:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 128
+      buffer_size: 2048
+      learning_rate: 0.0003
+      beta: 0.01
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 2000000
+    time_horizon: 64
+    summary_freq: 60000

MLAgents/config/ppo/Pyramids.yaml

@@ -0,0 +1,31 @@
+behaviors:
+  Pyramids:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 128
+      buffer_size: 2048
+      learning_rate: 0.0003
+      beta: 0.01
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 512
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+      curiosity:
+        gamma: 0.99
+        strength: 0.02
+        network_settings:
+          hidden_units: 256
+        learning_rate: 0.0003
+    keep_checkpoints: 5
+    max_steps: 10000000
+    time_horizon: 128
+    summary_freq: 30000

MLAgents/config/ppo/PyramidsRND.yaml

@@ -0,0 +1,32 @@
+behaviors:
+  Pyramids:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 128
+      buffer_size: 2048
+      learning_rate: 0.0003
+      beta: 0.01
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 512
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+      rnd:
+        gamma: 0.99
+        strength: 0.01
+        network_settings:
+          hidden_units: 64
+          num_layers: 3
+        learning_rate: 0.0001
+    keep_checkpoints: 5
+    max_steps: 3000000
+    time_horizon: 128
+    summary_freq: 30000

MLAgents/config/ppo/Sorter_curriculum.yaml

@@ -0,0 +1,102 @@
+behaviors:
+  Sorter:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 512
+      buffer_size: 40960
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: constant
+    network_settings:
+      normalize: False
+      hidden_units: 128
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 5000000
+    time_horizon: 256
+    summary_freq: 10000
+environment_parameters:
+  num_tiles:
+    curriculum:
+      - name: Lesson0 # The '-' is important as this is a list
+        completion_criteria:
+          measure: progress
+          behavior: Sorter
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.3
+        value: 2.0
+      - name: Lesson1
+        completion_criteria:
+          measure: progress
+          behavior: Sorter
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.4
+        value: 4.0
+      - name: Lesson2
+        completion_criteria:
+          measure: progress
+          behavior: Sorter
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.45
+        value: 6.0
+      - name: Lesson3
+        completion_criteria:
+          measure: progress
+          behavior: Sorter
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.5
+        value: 8.0
+      - name: Lesson4
+        completion_criteria:
+          measure: progress
+          behavior: Sorter
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.55
+        value: 10.0
+      - name: Lesson5
+        completion_criteria:
+          measure: progress
+          behavior: Sorter
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.6
+        value: 12.0
+      - name: Lesson6
+        completion_criteria:
+          measure: progress
+          behavior: Sorter
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.65
+        value: 14.0
+      - name: Lesson7
+        completion_criteria:
+          measure: progress
+          behavior: Sorter
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.7
+        value: 16.0
+      - name: Lesson8
+        completion_criteria:
+          measure: progress
+          behavior: Sorter
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.75
+        value: 18.0
+      - name: Lesson9
+        value: 20.0

MLAgents/config/ppo/Visual3DBall.yaml

@@ -0,0 +1,25 @@
+behaviors:
+  Visual3DBall:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 256
+      buffer_size: 2560
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 128
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 400000
+    time_horizon: 64
+    summary_freq: 20000

MLAgents/config/ppo/VisualFoodCollector.yaml

@@ -0,0 +1,25 @@
+behaviors:
+  VisualFoodCollector:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 1024
+      buffer_size: 10240
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 128
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 3000000
+    time_horizon: 100
+    summary_freq: 40000

MLAgents/config/ppo/Walker.yaml

@@ -0,0 +1,25 @@
+behaviors:
+  Walker:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 2048
+      buffer_size: 20480
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: true
+      hidden_units: 512
+      num_layers: 3
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.995
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 30000000
+    time_horizon: 1000
+    summary_freq: 30000

MLAgents/config/ppo/WallJump.yaml

@@ -0,0 +1,49 @@
+behaviors:
+  BigWallJump:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 128
+      buffer_size: 2048
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 20000000
+    time_horizon: 128
+    summary_freq: 20000
+  SmallWallJump:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 128
+      buffer_size: 2048
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 5000000
+    time_horizon: 128
+    summary_freq: 20000

MLAgents/config/ppo/WallJump_curriculum.yaml

@@ -0,0 +1,118 @@
+behaviors:
+  BigWallJump:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 128
+      buffer_size: 2048
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 20000000
+    time_horizon: 128
+    summary_freq: 20000
+  SmallWallJump:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 128
+      buffer_size: 2048
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 5000000
+    time_horizon: 128
+    summary_freq: 20000
+environment_parameters:
+  big_wall_height:
+    curriculum:
+      - name: Lesson0 # The '-' is important as this is a list
+        completion_criteria:
+          measure: progress
+          behavior: BigWallJump
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.1
+        value:
+          sampler_type: uniform
+          sampler_parameters:
+            min_value: 0.0
+            max_value: 4.0
+      - name: Lesson1 # This is the start of the second lesson
+        completion_criteria:
+          measure: progress
+          behavior: BigWallJump
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.3
+        value:
+          sampler_type: uniform
+          sampler_parameters:
+            min_value: 4.0
+            max_value: 7.0
+      - name: Lesson2
+        completion_criteria:
+          measure: progress
+          behavior: BigWallJump
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.5
+        value:
+          sampler_type: uniform
+          sampler_parameters:
+            min_value: 6.0
+            max_value: 8.0
+      - name: Lesson3
+        value: 8.0
+  small_wall_height:
+    curriculum:
+      - name: Lesson0
+        completion_criteria:
+          measure: progress
+          behavior: SmallWallJump
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.1
+        value: 1.5
+      - name: Lesson1
+        completion_criteria:
+          measure: progress
+          behavior: SmallWallJump
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.3
+        value: 2.0
+      - name: Lesson2
+        completion_criteria:
+          measure: progress
+          behavior: SmallWallJump
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.5
+        value: 2.5
+      - name: Lesson3
+        value: 4.0

MLAgents/config/ppo/Worm.yaml

@@ -0,0 +1,25 @@
+behaviors:
+  Worm:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 2024
+      buffer_size: 20240
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: true
+      hidden_units: 512
+      num_layers: 3
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.995
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 7000000
+    time_horizon: 1000
+    summary_freq: 30000

MLAgents/config/rollerball_config.yaml

@@ -0,0 +1,25 @@
+behaviors:
+  RollerBall:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 10
+      buffer_size: 100
+      learning_rate: 3.0e-4
+      beta: 5.0e-4
+      epsilon: 0.2
+      lambd: 0.99
+      num_epoch: 3
+      learning_rate_schedule: linear
+      beta_schedule: constant
+      epsilon_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 128
+      num_layers: 2
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    max_steps: 500000
+    time_horizon: 64
+    summary_freq: 10000

MLAgents/config/sac/3DBall.yaml

@@ -0,0 +1,27 @@
+behaviors:
+  3DBall:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 64
+      buffer_size: 200000
+      buffer_init_steps: 0
+      tau: 0.005
+      steps_per_update: 10.0
+      save_replay_buffer: false
+      init_entcoef: 0.5
+      reward_signal_steps_per_update: 10.0
+    network_settings:
+      normalize: true
+      hidden_units: 64
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 200000
+    time_horizon: 1000
+    summary_freq: 12000

MLAgents/config/sac/3DBallHard.yaml

@@ -0,0 +1,27 @@
+behaviors:
+  3DBallHard:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 256
+      buffer_size: 500000
+      buffer_init_steps: 0
+      tau: 0.005
+      steps_per_update: 10.0
+      save_replay_buffer: false
+      init_entcoef: 1.0
+      reward_signal_steps_per_update: 10.0
+    network_settings:
+      normalize: true
+      hidden_units: 128
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 500000
+    time_horizon: 1000
+    summary_freq: 12000

MLAgents/config/sac/Basic.yaml

@@ -0,0 +1,27 @@
+behaviors:
+  Basic:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 64
+      buffer_size: 50000
+      buffer_init_steps: 0
+      tau: 0.005
+      steps_per_update: 10.0
+      save_replay_buffer: false
+      init_entcoef: 0.01
+      reward_signal_steps_per_update: 10.0
+    network_settings:
+      normalize: false
+      hidden_units: 20
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 500000
+    time_horizon: 10
+    summary_freq: 2000

MLAgents/config/sac/Crawler.yaml

@@ -0,0 +1,27 @@
+behaviors:
+  Crawler:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 256
+      buffer_size: 500000
+      buffer_init_steps: 0
+      tau: 0.005
+      steps_per_update: 20.0
+      save_replay_buffer: false
+      init_entcoef: 1.0
+      reward_signal_steps_per_update: 20.0
+    network_settings:
+      normalize: true
+      hidden_units: 512
+      num_layers: 3
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.995
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 5000000
+    time_horizon: 1000
+    summary_freq: 30000

MLAgents/config/sac/FoodCollector.yaml

@@ -0,0 +1,28 @@
+behaviors:
+  FoodCollector:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 256
+      buffer_size: 2048
+      buffer_init_steps: 0
+      tau: 0.005
+      steps_per_update: 10.0
+      save_replay_buffer: false
+      init_entcoef: 0.05
+      reward_signal_steps_per_update: 10.0
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 1
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 2000000
+    time_horizon: 64
+    summary_freq: 60000
+    threaded: false

MLAgents/config/sac/GridWorld.yaml

@@ -0,0 +1,27 @@
+behaviors:
+  GridWorld:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 128
+      buffer_size: 50000
+      buffer_init_steps: 1000
+      tau: 0.005
+      steps_per_update: 10.0
+      save_replay_buffer: false
+      init_entcoef: 0.5
+      reward_signal_steps_per_update: 10.0
+    network_settings:
+      normalize: false
+      hidden_units: 128
+      num_layers: 1
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.9
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 500000
+    time_horizon: 5
+    summary_freq: 20000

MLAgents/config/sac/Hallway.yaml

@@ -0,0 +1,30 @@
+behaviors:
+  Hallway:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 512
+      buffer_size: 200000
+      buffer_init_steps: 0
+      tau: 0.005
+      steps_per_update: 10.0
+      save_replay_buffer: false
+      init_entcoef: 0.1
+      reward_signal_steps_per_update: 10.0
+    network_settings:
+      normalize: false
+      hidden_units: 128
+      num_layers: 2
+      vis_encode_type: simple
+      memory:
+        sequence_length: 64
+        memory_size: 128
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 4000000
+    time_horizon: 64
+    summary_freq: 10000

MLAgents/config/sac/PushBlock.yaml

@@ -0,0 +1,27 @@
+behaviors:
+  PushBlock:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 128
+      buffer_size: 50000
+      buffer_init_steps: 0
+      tau: 0.005
+      steps_per_update: 10.0
+      save_replay_buffer: false
+      init_entcoef: 0.05
+      reward_signal_steps_per_update: 10.0
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 2000000
+    time_horizon: 64
+    summary_freq: 100000

MLAgents/config/sac/Pyramids.yaml

@@ -0,0 +1,34 @@
+behaviors:
+  Pyramids:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 128
+      buffer_size: 2000000
+      buffer_init_steps: 1000
+      tau: 0.01
+      steps_per_update: 10.0
+      save_replay_buffer: false
+      init_entcoef: 0.01
+      reward_signal_steps_per_update: 10.0
+    network_settings:
+      normalize: false
+      hidden_units: 512
+      num_layers: 3
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.995
+        strength: 2.0
+      gail:
+        gamma: 0.99
+        strength: 0.01
+        learning_rate: 0.0003
+        use_actions: true
+        use_vail: false
+        demo_path: Project/Assets/ML-Agents/Examples/Pyramids/Demos/ExpertPyramid.demo
+    keep_checkpoints: 5
+    max_steps: 3000000
+    time_horizon: 128
+    summary_freq: 30000

MLAgents/config/sac/Walker.yaml

@@ -0,0 +1,27 @@
+behaviors:
+  Walker:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 1024
+      buffer_size: 2000000
+      buffer_init_steps: 0
+      tau: 0.005
+      steps_per_update: 30.0
+      save_replay_buffer: false
+      init_entcoef: 1.0
+      reward_signal_steps_per_update: 30.0
+    network_settings:
+      normalize: true
+      hidden_units: 256
+      num_layers: 3
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.995
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 15000000
+    time_horizon: 1000
+    summary_freq: 30000

MLAgents/config/sac/WallJump.yaml

@@ -0,0 +1,53 @@
+behaviors:
+  BigWallJump:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 128
+      buffer_size: 200000
+      buffer_init_steps: 0
+      tau: 0.005
+      steps_per_update: 20.0
+      save_replay_buffer: false
+      init_entcoef: 0.1
+      reward_signal_steps_per_update: 10.0
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 15000000
+    time_horizon: 128
+    summary_freq: 20000
+  SmallWallJump:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 128
+      buffer_size: 50000
+      buffer_init_steps: 0
+      tau: 0.005
+      steps_per_update: 20.0
+      save_replay_buffer: false
+      init_entcoef: 0.1
+      reward_signal_steps_per_update: 10.0
+    network_settings:
+      normalize: false
+      hidden_units: 256
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 5000000
+    time_horizon: 128
+    summary_freq: 20000

MLAgents/config/sac/Worm.yaml

@@ -0,0 +1,27 @@
+behaviors:
+  Worm:
+    trainer_type: sac
+    hyperparameters:
+      learning_rate: 0.0003
+      learning_rate_schedule: constant
+      batch_size: 256
+      buffer_size: 500000
+      buffer_init_steps: 0
+      tau: 0.005
+      steps_per_update: 20.0
+      save_replay_buffer: false
+      init_entcoef: 1.0
+      reward_signal_steps_per_update: 20.0
+    network_settings:
+      normalize: true
+      hidden_units: 512
+      num_layers: 3
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.995
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 5000000
+    time_horizon: 1000
+    summary_freq: 30000

MLAgents/results/BensonImitationV1/configuration.yaml

@@ -0,0 +1,89 @@
+default_settings: null
+behaviors:
+  BensonImitationV1:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 10
+      buffer_size: 200
+      learning_rate: 0.0003
+      beta: 0.005
+      epsilon: 0.2
+      lambd: 0.95
+      num_epoch: 3
+      shared_critic: false
+      learning_rate_schedule: linear
+      beta_schedule: linear
+      epsilon_schedule: linear
+    network_settings:
+      normalize: false
+      hidden_units: 128
+      num_layers: 2
+      vis_encode_type: simple
+      memory: null
+      goal_conditioning_type: hyper
+      deterministic: false
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+        network_settings:
+          normalize: false
+          hidden_units: 128
+          num_layers: 2
+          vis_encode_type: simple
+          memory: null
+          goal_conditioning_type: hyper
+          deterministic: false
+      curiosity:
+        gamma: 0.99
+        strength: 0.02
+        network_settings:
+          normalize: false
+          hidden_units: 256
+          num_layers: 2
+          vis_encode_type: simple
+          memory: null
+          goal_conditioning_type: hyper
+          deterministic: false
+        learning_rate: 0.0003
+        encoding_size: 256
+    init_path: null
+    keep_checkpoints: 5
+    checkpoint_interval: 50000
+    max_steps: 500000
+    time_horizon: 64
+    summary_freq: 1000
+    threaded: true
+    self_play: null
+    behavioral_cloning: null
+env_settings:
+  env_path: null
+  env_args: null
+  base_port: 5005
+  num_envs: 1
+  num_areas: 1
+  seed: -1
+  max_lifetime_restarts: 10
+  restarts_rate_limit_n: 1
+  restarts_rate_limit_period_s: 60
+engine_settings:
+  width: 84
+  height: 84
+  quality_level: 5
+  time_scale: 20
+  target_frame_rate: -1
+  capture_frame_rate: 60
+  no_graphics: false
+environment_parameters: null
+checkpoint_settings:
+  run_id: BensonImitationV1
+  initialize_from: null
+  load_model: false
+  resume: false
+  force: true
+  train_model: false
+  inference: false
+  results_dir: results
+torch_settings:
+  device: null
+debug: false

MLAgents/results/BensonImitationV1/run_logs/Player-0.log

@@ -0,0 +1,486 @@
+Mono path[0] = 'C:/Users/noahk/Desktop/BensonsTrainingArea/Racesm_L_Data/Managed'
+Mono config path = 'C:/Users/noahk/Desktop/BensonsTrainingArea/MonoBleedingEdge/etc'
+[Physics::Module] Initialized MultithreadedJobDispatcher with 15 workers.
+Initialize engine version: 2022.3.11f1 (d00248457e15)
+[Subsystems] Discovering subsystems at path C:/Users/noahk/Desktop/BensonsTrainingArea/Racesm_L_Data/UnitySubsystems
+Forcing GfxDevice: Null
+GfxDevice: creating device client; threaded=0; jobified=0
+NullGfxDevice:
+    Version:  NULL 1.0 [1.0]
+    Renderer: Null Device
+    Vendor:   Unity Technologies
+Begin MonoManager ReloadAssembly
+- Loaded All Assemblies, in  1.570 seconds
+- Finished resetting the current domain, in  0.001 seconds
+ERROR: Shader Sprites/Default shader is not supported on this GPU (none of subshaders/fallbacks are suitable)
+Microsoft Media Foundation video decoding to texture disabled: graphics device is Null, only Direct3D 11 and Direct3D 12 (only on desktop) are supported for hardware-accelerated video decoding.
+ERROR: Shader Sprites/Mask shader is not supported on this GPU (none of subshaders/fallbacks are suitable)
+ERROR: Shader Legacy Shaders/VertexLit shader is not supported on this GPU (none of subshaders/fallbacks are suitable)
+WARNING: Shader Unsupported: 'Standard' - All subshaders removed
+WARNING: Shader Did you use #pragma only_renderers and omit this platform?
+WARNING: Shader If subshaders removal was intentional, you may have forgotten turning Fallback off?
+ERROR: Shader Standard shader is not supported on this GPU (none of subshaders/fallbacks are suitable)
+WARNING: Shader Unsupported: 'Standard' - All subshaders removed
+WARNING: Shader Did you use #pragma only_renderers and omit this platform?
+WARNING: Shader If subshaders removal was intentional, you may have forgotten turning Fallback off?
+WARNING: Shader Unsupported: 'Legacy Shaders/Diffuse' - All subshaders removed
+WARNING: Shader Did you use #pragma only_renderers and omit this platform?
+WARNING: Shader If subshaders removal was intentional, you may have forgotten turning Fallback off?
+ERROR: Shader Legacy Shaders/Diffuse shader is not supported on this GPU (none of subshaders/fallbacks are suitable)
+WARNING: Shader Unsupported: 'Legacy Shaders/Diffuse' - All subshaders removed
+WARNING: Shader Did you use #pragma only_renderers and omit this platform?
+WARNING: Shader If subshaders removal was intentional, you may have forgotten turning Fallback off?
+UnloadTime: 0.673100 ms
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MLAgents/results/BensonImitationV1/run_logs/Player-1.log

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MLAgents/results/BensonImitationV1/run_logs/Player-2.log

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MLAgents/results/BensonImitationV1/run_logs/Player-3.log

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MLAgents/results/BensonImitationV1/run_logs/Player-4.log

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MLAgents/results/BensonImitationV1/run_logs/timers.json

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MLAgents/results/BensonImitationV1/run_logs/training_status.json

@@ -0,0 +1,38 @@
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