Fix hypernetwork

# Conflicts:
#	Project/Assets/ML-Agents/Examples/Crawler/Prefabs/CrawlerBase.prefab
#	Project/Assets/ML-Agents/Examples/GridWorld/Prefabs/Area.prefab
#	Project/Assets/ML-Agents/Examples/GridWorld/Scenes/GridWorld.unity
#	Project/ProjectSettings/TagManager.asset
#	com.unity.ml-agents/Runtime/Sensors/CameraSensor.cs
#	com.unity.ml-agents/Runtime/Sensors/VectorSensor.cs
#	ml-agents/mlagents/trainers/torch/networks.py
#	ml-agents/mlagents/trainers/torch/utils.py

config/ppo/GridWorld.yaml

       learning_rate_schedule: linear
     network_settings:
       normalize: false
-      hidden_units: 256
+      hidden_units: 64
       num_layers: 1
       vis_encode_type: simple
     reward_signals:

config/ppo/CrawlerDynamic.yaml

       learning_rate_schedule: linear
     network_settings:
       normalize: true
-      hidden_units: 512
+      hidden_units: 256
       num_layers: 3
       vis_encode_type: simple
     reward_signals:
     time_horizon: 1000
     summary_freq: 30000
     threaded: true
+    checkpoint_interval: 1000000

Project/Assets/ML-Agents/Examples/Crawler/Scenes/CrawlerDynamicTarget.unity

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Project/Assets/ML-Agents/Examples/Crawler/Prefabs/CrawlerBase.prefab

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Project/Assets/ML-Agents/Examples/Crawler/Prefabs/DynamicPlatform.prefab

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Project/Assets/ML-Agents/Examples/Crawler/Scripts/CrawlerAgent.cs

         "Static - The agent will run towards a static target. "
     )]
     public CrawlerAgentBehaviorType typeOfCrawler;
+    VectorSensorComponent goalSensor;
 
     //Crawler Brains
     //A different brain will be used depending on the CrawlerAgentBehaviorType selected
     /// </summary>
     public override void CollectObservations(VectorSensor sensor)
     {
+        goalSensor = this.GetComponent<VectorSensorComponent>();
         var cubeForward = m_OrientationCube.transform.forward;
 
         //velocity we want to match
         //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));
+        goalSensor.GetSensor().AddObservation(m_OrientationCube.transform.InverseTransformDirection(velGoal));
-        sensor.AddObservation(m_OrientationCube.transform.InverseTransformPoint(m_Target.transform.position));
+        goalSensor.GetSensor().AddObservation(m_OrientationCube.transform.InverseTransformPoint(m_Target.transform.position));
 
         RaycastHit hit;
         float maxRaycastDist = 10;

Project/Assets/ML-Agents/Examples/GridWorld/Prefabs/Area.prefab

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Project/Assets/ML-Agents/Examples/GridWorld/Scenes/GridWorld.unity

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Project/Assets/ML-Agents/Examples/GridWorld/Scripts/GridAgent.cs

 using UnityEngine;
 using System.Linq;
 using Unity.MLAgents;
+using Unity.MLAgents.Sensors;
 using Unity.MLAgents.Actuators;
 using UnityEngine.Rendering;
 using UnityEngine.Serialization;
         "a camera to render before making a decision. Place the agentCam here if using " +
         "RenderTexture as observations.")]
     public Camera renderCamera;
+    VectorSensorComponent goalSensor;
+
+    public enum GridGoal
+    {
+        Plus,
+        Cross,
+    }
+
+    public GridGoal gridGoal;
 
     [Tooltip("Selecting will turn on action masking. Note that a model trained with action " +
         "masking turned on may not behave optimally when action masking is turned off.")]
     public override void Initialize()
     {
         m_ResetParams = Academy.Instance.EnvironmentParameters;
+    }
+
+    public override void CollectObservations(VectorSensor sensor)
+    {
+        Array values = Enum.GetValues(typeof(GridGoal));
+        int goalNum = (int)gridGoal;
+        goalSensor = this.GetComponent<VectorSensorComponent>();
+        goalSensor.GetSensor().AddOneHotObservation(goalNum, values.Length);
     }
 
     public override void WriteDiscreteActionMask(IDiscreteActionMask actionMask)
 
             if (hit.Where(col => col.gameObject.CompareTag("goal")).ToArray().Length == 1)
             {
-                SetReward(1f);
+                ProvideReward(GridGoal.Plus);
-                SetReward(-1f);
+                ProvideReward(GridGoal.Cross);
+        }
+    }
+
+    private void ProvideReward(GridGoal hitObject)
+    {
+        if (gridGoal == hitObject)
+        {
+            SetReward(1f);
+        }
+        else
+        {
+            SetReward(-1f);
         }
     }
 
     public override void OnEpisodeBegin()
     {
         area.AreaReset();
+        Array values = Enum.GetValues(typeof(GridGoal));
+        gridGoal = (GridGoal)values.GetValue(UnityEngine.Random.Range(0, values.Length));
     }
 
     public void FixedUpdate()

Project/ProjectSettings/TagManager.asset

   - symbol_O_Goal
   - purpleAgent
   - purpleGoal
+  - landmark
   - tile
   layers:
   - Default

com.unity.ml-agents/Tests/Editor/Sensor/CameraSensorTest.cs

                     var width = 24;
                     var height = 16;
                     var camera = Camera.main;
-                    var sensor = new CameraSensor(camera, width, height, grayscale, "TestCameraSensor", compression);
+                    var sensor = new CameraSensor(camera, width, height, grayscale, "TestCameraSensor", compression, ObservationType.Default);
 
                     var obsWriter = new ObservationWriter();
                     var obs = sensor.GetObservationProto(obsWriter);

com.unity.ml-agents/Tests/Editor/Sensor/StackingSensorTests.cs

         {
             // Test grayscale stacked mapping with CameraSensor
             var cameraSensor = new CameraSensor(new Camera(), 64, 64,
-                true, "grayscaleCamera", SensorCompressionType.PNG);
+                true, "grayscaleCamera", SensorCompressionType.PNG, ObservationType.Default);
             var stackedCameraSensor = new StackingSensor(cameraSensor, 2);
             Assert.AreEqual(stackedCameraSensor.GetCompressedChannelMapping(), new[] { 0, 0, 0, 1, 1, 1 });
 

com.unity.ml-agents/Runtime/Sensors/VectorSensor.cs

     /// <summary>
     /// A sensor implementation for vector observations.
     /// </summary>
-    public class VectorSensor : ISensor, IBuiltInSensor
+    public class VectorSensor : ISensor, ITypedSensor, IBuiltInSensor
     {
         // TODO use float[] instead
         // TODO allow setting float[]
+        ObservationType m_ObservationType;
 
         /// <summary>
         /// Initializes the sensor.
-        public VectorSensor(int observationSize, string name = null)
+        public VectorSensor(int observationSize, string name = null, ObservationType observationType = ObservationType.Default)
         {
             if (name == null)
             {
             m_Observations = new List<float>(observationSize);
             m_Name = name;
             m_Shape = new[] { observationSize };
+            m_ObservationType = observationType;
         }
 
         /// <inheritdoc/>
         public int[] GetObservationShape()
         {
             return m_Shape;
+        }
+
+        /// <inheritdoc/>
+        public virtual ObservationType GetObservationType()
+        {
+            return m_ObservationType;
         }
 
         /// <inheritdoc/>

com.unity.ml-agents/Runtime/Sensors/CameraSensor.cs

     /// <summary>
     /// A sensor that wraps a Camera object to generate visual observations for an agent.
     /// </summary>
-    public class CameraSensor : ISensor, IBuiltInSensor, IDimensionPropertiesSensor
+    public class CameraSensor : ISensor, ITypedSensor, IBuiltInSensor, IDimensionPropertiesSensor
     {
         Camera m_Camera;
         int m_Width;
         int[] m_Shape;
         SensorCompressionType m_CompressionType;
+        ObservationType m_ObservationType;
         static DimensionProperty[] s_DimensionProperties = new DimensionProperty[] {
             DimensionProperty.TranslationalEquivariance,
             DimensionProperty.TranslationalEquivariance,
             set { m_CompressionType = value; }
         }
 
+        public ObservationType ObservationType
+        {
+            get { return m_ObservationType; }
+            set { m_ObservationType = value; }
+        }
+
 
         /// <summary>
         /// Creates and returns the camera sensor.
         /// <param name="name">The name of the camera sensor.</param>
         /// <param name="compression">The compression to apply to the generated image.</param>
         public CameraSensor(
-            Camera camera, int width, int height, bool grayscale, string name, SensorCompressionType compression)
+            Camera camera, int width, int height, bool grayscale, string name, SensorCompressionType compression,
+            ObservationType observationType)
         {
             m_Camera = camera;
             m_Width = width;
             m_Shape = GenerateShape(width, height, grayscale);
             m_CompressionType = compression;
+            m_ObservationType = observationType;
         }
 
         /// <summary>
         public string GetName()
         {
             return m_Name;
+        }
+
+        /// <inheritdoc/>
+        public virtual ObservationType GetObservationType()
+        {
+            return m_ObservationType;
         }
 
         /// <summary>

com.unity.ml-agents/Runtime/Sensors/CameraSensorComponent.cs

             set { m_SensorName = value; }
         }
 
+        [HideInInspector, SerializeField, FormerlySerializedAs("observationType")]
+        ObservationType m_ObservationType = ObservationType.Goal;
+
         [HideInInspector, SerializeField, FormerlySerializedAs("width")]
         int m_Width = 84;
 
         }
 
         /// <summary>
+        /// The type of the observation.
+        /// </summary>
+        public ObservationType SensorObservationType
+        {
+            get { return m_ObservationType; }
+            set { m_ObservationType = value; UpdateSensor(); }
+        }
+
+        /// <summary>
-            m_Sensor = new CameraSensor(m_Camera, m_Width, m_Height, Grayscale, m_SensorName, m_Compression);
+            m_Sensor = new CameraSensor(m_Camera, m_Width, m_Height, Grayscale, m_SensorName, m_Compression, m_ObservationType);
 
             if (ObservationStacks != 1)
             {
             {
                 m_Sensor.Camera = m_Camera;
                 m_Sensor.CompressionType = m_Compression;
+                m_Sensor.ObservationType = m_ObservationType;
             }
         }
     }

com.unity.ml-agents/Runtime/Sensors/ITypedSensor.cs

     /// <summary>
     /// The ObservationType enum of the Sensor.
     /// </summary>
-    internal enum ObservationType
+    public enum ObservationType
-        Default = 0,
+        Default,
-        Goal = 1,
+        Goal,
-        Reward = 2,
+        Reward,
-        Message = 3,
+        Message,
     }
 
 
-    internal interface ITypedSensor
+    public interface ITypedSensor
     {
         /// <summary>
         /// Returns the ObservationType enum corresponding to the type of the sensor.

ml-agents/mlagents/trainers/settings.py

     LINEAR = "linear"
 
 
+class ConditioningType(Enum):
+    DEFAULT = "default"
+    HYPER = "hyper"
+    SOFT_MUL = "soft_mul"
+    SOFT_SUM = "soft_sum"
+
+
 @attr.s(auto_attribs=True)
 class NetworkSettings:
     @attr.s
     num_layers: int = 2
     vis_encode_type: EncoderType = EncoderType.SIMPLE
     memory: Optional[MemorySettings] = None
+    conditioning_type: ConditioningType = ConditioningType.DEFAULT
 
 
 @attr.s(auto_attribs=True)

ml-agents/mlagents/trainers/torch/utils.py

 from mlagents.trainers.settings import EncoderType, ScheduleType
 from mlagents.trainers.torch.attention import EntityEmbedding
 from mlagents.trainers.exception import UnityTrainerException
-from mlagents_envs.base_env import ObservationSpec, DimensionProperty
+from mlagents_envs.base_env import ObservationSpec, ObservationType, DimensionProperty
 
 
 class ModelUtils:
         h_size: int,
         vis_encode_type: EncoderType,
         normalize: bool = False,
-    ) -> Tuple[nn.ModuleList, List[int]]:
+    ) -> Tuple[nn.ModuleList, List[int], List[ObservationType]]:
         """
         Creates visual and vector encoders, along with their normalizers.
         :param observation_specs: List of ObservationSpec that represent the observation dimensions.
         """
         encoders: List[nn.Module] = []
         embedding_sizes: List[int] = []
+        obs_types: List[ObservationType] = []
         for obs_spec in observation_specs:
             encoder, embedding_size = ModelUtils.get_encoder_for_obs(
                 obs_spec, normalize, h_size, vis_encode_type
+            obs_types.append(obs_spec.observation_type)
-        x_self_size = sum(embedding_sizes)  # The size of the "self" embedding
-        if x_self_size > 0:
-            for enc in encoders:
-                if isinstance(enc, EntityEmbedding):
-                    enc.add_self_embedding(h_size)
-        return (nn.ModuleList(encoders), embedding_sizes)
+        return (nn.ModuleList(encoders), embedding_sizes, obs_types)
 
     @staticmethod
     def list_to_tensor(

ml-agents/mlagents/trainers/torch/layers.py

 from mlagents.torch_utils import torch
 import abc
+import math
+
+from mlagents.trainers.settings import ConditioningType
 
 
 class Swish(torch.nn.Module):
         return (layer_activations - mean) / (torch.sqrt(var + 1e-5))
 
 
+class ConditionalLayer(torch.nn.Module):
+    def __init__(
+        self,
+        input_size: int,
+        goal_size: int,
+        num_layers: int,
+        hidden_size: int,
+        condition_type: ConditioningType,
+    ):
+        super().__init__()
+        self.condition_type = condition_type
+        if self.condition_type != ConditioningType.HYPER:
+            self.goal_encoder = LinearEncoder(
+                goal_size, num_layers, hidden_size, final_activation=True
+            )
+            self.input_encoder = linear_layer(input_size, hidden_size)
+        else:
+            self.input_encoder = HyperNetwork(
+                input_size, hidden_size, goal_size, num_layers, hidden_size
+            )
+
+    def forward(
+        self, input_tensor: torch.Tensor, goal_tensor: torch.Tensor
+    ) -> torch.Tensor:
+        if self.condition_type == ConditioningType.SOFT_MUL:
+            return self.input_encoder(input_tensor) * self.goal_encoder(goal_tensor)
+        elif self.condition_type == ConditioningType.SOFT_SUM:
+            return self.input_encoder(input_tensor) + self.goal_encoder(goal_tensor)
+        else:
+            return self.input_encoder(input_tensor, goal_tensor)
+
+
+class ConditionalEncoder(torch.nn.Module):
+    """
+    Linear layers.
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        goal_size: int,
+        num_layers: int,
+        hidden_size: int,
+        condition_type: ConditioningType,
+        conditional_layers: int,
+        kernel_init: Initialization = Initialization.KaimingHeNormal,
+        kernel_gain: float = 1.0,
+    ):
+        super().__init__()
+        self.layers = []
+        prev_size = input_size + goal_size
+        for i in range(num_layers):
+            if num_layers - i <= conditional_layers:
+                self.layers.append(
+                    ConditionalLayer(
+                        prev_size, goal_size, 2, hidden_size, condition_type
+                    )
+                )
+            else:
+                self.layers.append(
+                    linear_layer(
+                        prev_size,
+                        hidden_size,
+                        kernel_init=kernel_init,
+                        kernel_gain=kernel_gain,
+                    )
+                )
+            self.layers.append(Swish())
+            prev_size = hidden_size
+        self.layers = torch.nn.ModuleList(self.layers)
+
+    def forward(
+        self, input_tensor: torch.Tensor, goal_tensor: torch.Tensor
+    ) -> torch.Tensor:
+        activation = torch.cat([input_tensor, goal_tensor], dim=-1)
+        for layer in self.layers:
+            if isinstance(layer, ConditionalLayer):
+                activation = layer(activation, goal_tensor)
+            else:
+                activation = layer(activation)
+        return activation
+
+
 class LinearEncoder(torch.nn.Module):
     """
     Linear layers.
         hidden_size: int,
         kernel_init: Initialization = Initialization.KaimingHeNormal,
         kernel_gain: float = 1.0,
+        final_activation: bool = True,
     ):
         super().__init__()
         self.layers = [
             )
         ]
         self.layers.append(Swish())
-        for _ in range(num_layers - 1):
+        for i in range(num_layers - 1):
             self.layers.append(
                 linear_layer(
                     hidden_size,
                 )
             )
-            self.layers.append(Swish())
+            if i < num_layers - 2 or final_activation:
+                self.layers.append(Swish())
         self.seq_layers = torch.nn.Sequential(*self.layers)
 
     def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
         lstm_out, hidden_out = self.lstm(input_tensor, hidden)
         output_mem = torch.cat(hidden_out, dim=-1)
         return lstm_out, output_mem
+
+
+class HyperNetwork(torch.nn.Module):
+    def __init__(
+        self, input_size, output_size, hyper_input_size, num_layers, layer_size
+    ):
+        super().__init__()
+        self.input_size = input_size
+        self.output_size = output_size
+
+        layer_in_size = hyper_input_size
+        layers = []
+        for _ in range(num_layers):
+            layers.append(
+                linear_layer(
+                    layer_in_size,
+                    layer_size,
+                    kernel_init=Initialization.KaimingHeNormal,
+                    kernel_gain=1.0,
+                    bias_init=Initialization.Zero,
+                )
+            )
+            layers.append(Swish())
+            layer_in_size = layer_size
+        flat_output = linear_layer(
+            layer_size,
+            input_size * output_size,
+            kernel_init=Initialization.KaimingHeNormal,
+            kernel_gain=0.1,
+            bias_init=Initialization.Zero,
+        )
+
+        # Re-initializing the weights of the last layer of the hypernetwork
+        bound = math.sqrt(1 / (layer_size * self.input_size))
+        flat_output.weight.data.uniform_(-bound, bound)
+
+        self.hypernet = torch.nn.Sequential(*layers, LayerNorm(), flat_output)
+
+        # The hypernetwork will not generate the bias of the main network layer
+        self.bias = torch.nn.Parameter(torch.zeros(output_size))
+
+    def forward(self, input_activation, hyper_input):
+        output_weights = self.hypernet(hyper_input)
+
+        output_weights = output_weights.view(-1, self.input_size, self.output_size)
+
+        result = (
+            torch.bmm(input_activation.unsqueeze(1), output_weights).squeeze(1)
+            + self.bias
+        )
+        return result

ml-agents/mlagents/trainers/torch/networks.py

 
 from mlagents.torch_utils import torch, nn
 
-from mlagents_envs.base_env import ActionSpec, ObservationSpec
+from mlagents_envs.base_env import ActionSpec, ObservationSpec, ObservationType
-from mlagents.trainers.settings import NetworkSettings
+from mlagents.trainers.settings import NetworkSettings, ConditioningType
-from mlagents.trainers.torch.layers import LSTM, LinearEncoder, Initialization
+from mlagents.trainers.torch.layers import (
+    LSTM,
+    LinearEncoder,
+    ConditionalEncoder,
+    Initialization,
+)
 from mlagents.trainers.torch.encoders import VectorInput
 from mlagents.trainers.buffer import AgentBuffer
 from mlagents.trainers.trajectory import ObsUtil
     get_zero_entities_mask,
 )
 
-
 ActivationFunction = Callable[[torch.Tensor], torch.Tensor]
 EncoderFunction = Callable[
     [torch.Tensor, int, ActivationFunction, int, str, bool], torch.Tensor
         encoded_act_size: int = 0,
     ):
         super().__init__()
+        self.conditioning_type = network_settings.conditioning_type
         self.normalize = network_settings.normalize
         self.use_lstm = network_settings.memory is not None
         self.h_size = network_settings.hidden_units
             else 0
         )
 
-        self.processors, self.embedding_sizes = ModelUtils.create_input_processors(
+        self.processors, self.embedding_sizes, self.obs_types = ModelUtils.create_input_processors(
             observation_specs,
             self.h_size,
             network_settings.vis_encode_type,
+        total_enc_size, total_goal_size = 0, 0
+        for idx, embedding_size in enumerate(self.embedding_sizes):
+            if (
+                self.obs_types[idx] == ObservationType.DEFAULT
+                or self.conditioning_type == ConditioningType.DEFAULT
+            ):
+                total_enc_size += embedding_size
+            if (
+                self.obs_types[idx] == ObservationType.GOAL
+                and self.conditioning_type != ConditioningType.DEFAULT
+            ):
+                total_goal_size += embedding_size
+        total_enc_size += encoded_act_size
+
         entity_num_max: int = 0
         var_processors = [p for p in self.processors if isinstance(p, EntityEmbedding)]
         for processor in var_processors:
                     kernel_gain=(0.125 / self.h_size) ** 0.5,
                 )
             self.rsa = ResidualSelfAttention(self.h_size, entity_num_max)
-            total_enc_size = sum(self.embedding_sizes) + self.h_size
+            total_enc_size += self.h_size
+
+        if (
+            ObservationType.GOAL in self.obs_types
+            and self.conditioning_type != ConditioningType.DEFAULT
+        ):
+            self.linear_encoder = ConditionalEncoder(
+                total_enc_size,
+                total_goal_size,
+                network_settings.num_layers,
+                self.h_size,
+                condition_type=self.conditioning_type,
+                conditional_layers=1,
+            )
-            total_enc_size = sum(self.embedding_sizes)
-
-        total_enc_size += encoded_act_size
-        self.linear_encoder = LinearEncoder(
-            total_enc_size, network_settings.num_layers, self.h_size
-        )
+            self.linear_encoder = LinearEncoder(
+                total_enc_size, network_settings.num_layers, self.h_size
+            )
 
         if self.use_lstm:
             self.lstm = LSTM(self.h_size, self.m_size)
         memories: Optional[torch.Tensor] = None,
         sequence_length: int = 1,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
-        encodes = []
+
+        obs_encodes, goal_encodes = [], []
         var_len_processor_inputs: List[Tuple[nn.Module, torch.Tensor]] = []
 
         for idx, processor in enumerate(self.processors):
                 processed_obs = processor(obs_input)
-                encodes.append(processed_obs)
+                if self.obs_types[idx] == ObservationType.DEFAULT:
+                    obs_encodes.append(processed_obs)
+                elif self.obs_types[idx] == ObservationType.GOAL:
+                    goal_encodes.append(processed_obs)
+                else:
+                    raise Exception(
+                        "TODO : Something other than a goal or observation was passed to the agent."
+                    )
-        if len(encodes) != 0:
-            encoded_self = torch.cat(encodes, dim=1)
+        if len(obs_encodes) != 0:
+            encoded_self = torch.cat(obs_encodes, dim=1)
             input_exist = True
         else:
             input_exist = False
 
         if actions is not None:
             encoded_self = torch.cat([encoded_self, actions], dim=1)
-        encoding = self.linear_encoder(encoded_self)
+
+        if self.conditioning_type == ConditioningType.DEFAULT:
+            encoded_self = torch.cat([encoded_self, torch.cat(goal_encodes, dim=-1)], dim=1)
+            goal_encodes = []
+
+        if len(obs_encodes) == 0:
+            raise Exception("No valid inputs to network.")
+
+        if len(goal_encodes) == 0:
+            encoding = self.linear_encoder(encoded_self)
+        else:
+            goal_inputs = torch.cat(goal_encodes, dim=-1)
+            encoding = self.linear_encoder(encoded_self, goal_inputs)
 
         if self.use_lstm:
             # Resize to (batch, sequence length, encoding size)

Project/Assets/ML-Agents/Examples/GoalNav/Scripts/PushBlockSettings.cs.meta

 fileFormatVersion: 2
-guid: e5ed63dbfa25542ecb8bc013adfba183
+guid: 6582fc235f21046acba3381d9e57c348
 MonoImporter:
   externalObjects: {}
   serializedVersion: 2

config/ppo/WJPB.yaml

+behaviors:
+  WJPB:
+    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: 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: 128
+    summary_freq: 20000
+    threaded: true
+environment_parameters:
+  big_wall_height:
+    curriculum:
+      - name: Lesson0 # The '-' is important as this is a list
+        completion_criteria:
+          measure: progress
+          behavior: WJPB
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.05
+        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: WJPB
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.1
+        value:
+          sampler_type: uniform
+          sampler_parameters:
+            min_value: 4.0
+            max_value: 7.0
+      - name: Lesson2
+        completion_criteria:
+          measure: progress
+          behavior: WJPB
+          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
+env_settings:
+  num_envs: 4

config/ppo/PushJump.yaml

+behaviors:
+  WJPB:
+    trainer_type: ppo
+    hyperparameters:
+      batch_size: 128
+      buffer_size: 5000
+      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: 64
+      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: 30000
+    threaded: true

config/ppo/GoalNav.yaml

+behaviors:
+  GoalNav:
+    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: 100
+      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
+    threaded: true

Project/Assets/ML-Agents/Examples/PushJump.meta

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+folderAsset: yes
+timeCreated: 1520964896
+licenseType: Free
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+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 

Project/Assets/ML-Agents/Examples/GoalNav.meta

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+DefaultImporter:
+  externalObjects: {}
+  userData: 
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com.unity.ml-agents/Editor/VectorSensorComponentEditor.cs

+using UnityEditor;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Editor
+{
+    [CustomEditor(typeof(VectorSensorComponent))]
+    [CanEditMultipleObjects]
+    internal class VectorSensorComponentEditor : UnityEditor.Editor
+    {
+        public override void OnInspectorGUI()
+        {
+            var so = serializedObject;
+            so.Update();
+
+            // Drawing the VectorSensorComponent
+            EditorGUI.BeginChangeCheck();
+
+            EditorGUI.BeginDisabledGroup(!EditorUtilities.CanUpdateModelProperties());
+            {
+                // These fields affect the sensor order or observation size,
+                // So can't be changed at runtime.
+                EditorGUILayout.PropertyField(so.FindProperty("m_observationSize"), true);
+                EditorGUILayout.PropertyField(so.FindProperty("m_ObservationType"), true);
+            }
+            EditorGUI.EndDisabledGroup();
+
+            var requireSensorUpdate = EditorGUI.EndChangeCheck();
+            so.ApplyModifiedProperties();
+
+            if (requireSensorUpdate)
+            {
+                UpdateSensor();
+            }
+        }
+
+        void UpdateSensor()
+        {
+            var sensorComponent = serializedObject.targetObject as VectorSensorComponent;
+            sensorComponent?.UpdateSensor();
+        }
+    }
+}

com.unity.ml-agents/Editor/VectorSensorComponentEditor.cs.meta

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com.unity.ml-agents/Runtime/Sensors/VectorSensorComponent.cs.meta

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+  assetBundleName: 
+  assetBundleVariant: 

com.unity.ml-agents/Runtime/Sensors/VectorSensorComponent.cs

+using UnityEngine;
+using UnityEngine.Serialization;
+
+namespace Unity.MLAgents.Sensors
+{
+    [AddComponentMenu("ML Agents/Vector Sensor", (int)MenuGroup.Sensors)]
+    public class VectorSensorComponent : SensorComponent
+    {
+        public int ObservationSize
+        {
+            get { return m_observationSize; }
+            set { m_observationSize = value; }
+        }
+
+        [HideInInspector, SerializeField]
+        int m_observationSize;
+
+        [HideInInspector, SerializeField]
+        ObservationType m_ObservationType;
+
+        VectorSensor m_sensor;
+
+        public ObservationType ObservationType
+        {
+            get { return m_ObservationType; }
+            set { m_ObservationType = value; }
+        }
+
+        /// <summary>
+        /// Creates a VectorSensor.
+        /// </summary>
+        /// <returns></returns>
+        public override ISensor CreateSensor()
+        {
+            m_sensor = new VectorSensor(m_observationSize, observationType: m_ObservationType);
+            return m_sensor;
+        }
+
+        /// <inheritdoc/>
+        public override int[] GetObservationShape()
+        {
+            return new[] { m_observationSize };
+        }
+
+        public VectorSensor GetSensor()
+        {
+            return m_sensor;
+        }
+
+
+        /// <summary>
+        /// Update fields that are safe to change on the Sensor at runtime.
+        /// </summary>
+        internal void UpdateSensor()
+        {
+            if (m_sensor != null)
+            {
+
+            }
+        }
+    }
+}

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Project/Assets/ML-Agents/Examples/GoalNav/Scripts/GoalNavAgent.cs

+//Put this script on your blue cube.
+
+using System.Collections;
+using UnityEngine;
+using Unity.MLAgents;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Actuators;
+
+public class GoalNavAgent : Agent
+{
+    /// <summary>
+    /// The ground. The bounds are used to spawn the elements.
+    /// </summary>
+    public GameObject ground;
+
+    public GameObject area;
+
+    /// <summary>
+    /// The area bounds.
+    /// </summary>
+    [HideInInspector]
+    public Bounds areaBounds;
+
+    PushBlockSettings m_PushBlockSettings;
+
+    VectorSensorComponent goalSensor;
+
+    public GameObject goalObject;
+    public GameObject obstacleObject;
+
+    Vector3 goalLoc;
+    Vector3 obstacleLoc;
+
+    public bool useVectorObs;
+
+    Rigidbody m_AgentRb;  //cached on initialization
+    Material m_GroundMaterial; //cached on Awake()
+
+    /// <summary>
+    /// We will be changing the ground material based on success/failue
+    /// </summary>
+    Renderer m_GroundRenderer;
+
+    EnvironmentParameters m_ResetParams;
+
+    void Awake()
+    {
+        m_PushBlockSettings = FindObjectOfType<PushBlockSettings>();
+    }
+
+    public override void Initialize()
+    {
+
+        // Cache the agent rigidbody
+        m_AgentRb = GetComponent<Rigidbody>();
+        // Get the ground's bounds
+        areaBounds = ground.GetComponent<Collider>().bounds;
+        // Get the ground renderer so we can change the material when a goal is scored
+        m_GroundRenderer = ground.GetComponent<Renderer>();
+        // Starting material
+        m_GroundMaterial = m_GroundRenderer.material;
+
+        m_ResetParams = Academy.Instance.EnvironmentParameters;
+
+        SetResetParameters();
+    }
+
+    public override void CollectObservations(VectorSensor sensor)
+    {
+        goalSensor = this.GetComponent<VectorSensorComponent>();
+        if (useVectorObs)
+        {
+            goalSensor.GetSensor().AddObservation(goalLoc / 10f);
+        }
+        goalSensor.GetSensor().AddObservation(obstacleLoc / 10f);
+    }
+
+
+    /// <summary>
+    /// Use the ground's bounds to pick a random spawn position.
+    /// </summary>
+    public Vector3 GetRandomSpawnPos()
+    {
+        var foundNewSpawnLocation = false;
+        var randomSpawnPos = Vector3.zero;
+        while (foundNewSpawnLocation == false)
+        {
+            var randomPosX = Random.Range(-areaBounds.extents.x * m_PushBlockSettings.spawnAreaMarginMultiplier,
+                areaBounds.extents.x * m_PushBlockSettings.spawnAreaMarginMultiplier);
+
+            var randomPosZ = Random.Range(-areaBounds.extents.z * m_PushBlockSettings.spawnAreaMarginMultiplier,
+                areaBounds.extents.z * m_PushBlockSettings.spawnAreaMarginMultiplier);
+            randomSpawnPos = ground.transform.position + new Vector3(randomPosX, 1f, randomPosZ);
+            if (Physics.CheckBox(randomSpawnPos, new Vector3(2.5f, 0.01f, 2.5f)) == false)
+            {
+                foundNewSpawnLocation = true;
+            }
+        }
+        return randomSpawnPos;
+    }
+
+    /// <summary>
+    /// Moves the agent according to the selected action.
+    /// </summary>
+    public void MoveAgent(ActionSegment<int> act)
+    {
+        var dirToGo = Vector3.zero;
+        var rotateDir = Vector3.zero;
+
+        var action = act[0];
+
+        switch (action)
+        {
+            case 1:
+                dirToGo = transform.forward * 1f;
+                break;
+            case 2:
+                dirToGo = transform.forward * -1f;
+                break;
+            case 3:
+                dirToGo = transform.right * -0.75f;
+                break;
+            case 4:
+                dirToGo = transform.right * 0.75f;
+                break;
+        }
+        //transform.Rotate(rotateDir, Time.fixedDeltaTime * 200f);
+        m_AgentRb.AddForce(dirToGo * m_PushBlockSettings.agentRunSpeed,
+            ForceMode.VelocityChange);
+    }
+
+    /// <summary>
+    /// Called every step of the engine. Here the agent takes an action.
+    /// </summary>
+    public override void OnActionReceived(ActionBuffers actionBuffers)
+
+    {
+        // Move the agent using the action.
+        MoveAgent(actionBuffers.DiscreteActions);
+
+        // Penalty given each step to encourage agent to finish task quickly.
+        AddReward(-1f / MaxStep);
+
+        CheckGoal();
+    }
+
+    public void CheckGoal()
+    {
+        var distanceGoal = Vector3.Distance(transform.localPosition, goalLoc);
+        if (distanceGoal < 1.5f)
+        {
+            Debug.Log("Found goal");
+            SetReward(1f);
+            EndEpisode();
+        }
+
+        var distanceObstacle = Vector3.Distance(transform.localPosition, obstacleLoc);
+        if (distanceObstacle < 1.5f)
+        {
+            Debug.Log("Found obstacle");
+            SetReward(-1f);
+            EndEpisode();
+        }
+    }
+
+    public override void Heuristic(in ActionBuffers actionsOut)
+    {
+        var discreteActionsOut = actionsOut.DiscreteActions;
+        discreteActionsOut[0] = 0;
+        if (Input.GetKey(KeyCode.D))
+        {
+            discreteActionsOut[0] = 4;
+        }
+        else if (Input.GetKey(KeyCode.W))
+        {
+            discreteActionsOut[0] = 1;
+        }
+        else if (Input.GetKey(KeyCode.A))
+        {
+            discreteActionsOut[0] = 3;
+        }
+        else if (Input.GetKey(KeyCode.S))
+        {
+            discreteActionsOut[0] = 2;
+        }
+    }
+
+    /// <summary>
+    /// In the editor, if "Reset On Done" is checked then AgentReset() will be
+    /// called automatically anytime we mark done = true in an agent script.
+    /// </summary>
+    public override void OnEpisodeBegin()
+    {
+        //var rotation = Random.Range(0, 4);
+        //var rotationAngle = rotation * 90f;
+        //area.transform.Rotate(new Vector3(0f, rotationAngle, 0f));
+
+        transform.position = GetRandomSpawnPos();
+        m_AgentRb.velocity = Vector3.zero;
+        m_AgentRb.angularVelocity = Vector3.zero;
+
+        goalLoc = new Vector3(Random.Range(-10f, 10f), 1f, Random.Range(-10f, 10f));
+        goalObject.transform.localPosition = goalLoc;
+
+        obstacleLoc = new Vector3(Random.Range(-10f, 10f), 1f, Random.Range(-10f, 10f));
+        obstacleObject.transform.localPosition = obstacleLoc;
+
+        SetResetParameters();
+    }
+
+
+    void SetResetParameters()
+    {
+
+    }
+}

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