continuous, crawler integrated, new cube

4 年前 · d813bfd5
--- a/Project/Assets/ML-Agents/Examples/Crawler/Prefabs/Platform.prefab
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--- a/Project/Assets/ML-Agents/Examples/Crawler/Scripts/CrawlerAgent.cs
+++ b/Project/Assets/ML-Agents/Examples/Crawler/Scripts/CrawlerAgent.cs
        m_JdController.SetupBodyPart(leg2Lower);
        m_JdController.SetupBodyPart(leg3Upper);
        m_JdController.SetupBodyPart(leg3Lower);
+        GetComponent<VectorSensorComponent>().CreateSensors();
+        m_DiversitySettingSensor = GetComponent<VectorSensorComponent>();
+
+    VectorSensorComponent m_DiversitySettingSensor;
+    public int m_DiversitySetting = 0;

    /// <summary>
    /// Spawns a target prefab at pos

        //Set our goal walking speed
        TargetWalkingSpeed = Random.Range(0.1f, m_maxWalkingSpeed);
+
+        m_DiversitySetting = Random.Range(0, 8);
    }

    /// <summary>
    /// </summary>
    public override void CollectObservations(VectorSensor sensor)
    {
+        m_DiversitySettingSensor.GetSensor().Reset();
+        m_DiversitySettingSensor.GetSensor().AddOneHotObservation(m_DiversitySetting, 8);
+
        var cubeForward = m_OrientationCube.transform.forward;

        //velocity we want to match
        //This reward will approach 1 if it faces the target direction perfectly and approach zero as it deviates
        var lookAtTargetReward = (Vector3.Dot(cubeForward, body.forward) + 1) * .5F;

-        AddReward(matchSpeedReward * lookAtTargetReward);
+        AddReward(2f + matchSpeedReward * lookAtTargetReward);
    }

    /// <summary>
--- a/config/sac/Crawler.yaml
+++ b/config/sac/Crawler.yaml
      hidden_units: 512
      num_layers: 3
      vis_encode_type: simple
+      goal_conditioning_type: none
    reward_signals:
      extrinsic:
        gamma: 0.995
    time_horizon: 1000
-    summary_freq: 30000
+    summary_freq: 3000
--- a/ml-agents/mlagents/trainers/sac/optimizer_torch.py
+++ b/ml-agents/mlagents/trainers/sac/optimizer_torch.py
 from contextlib import ExitStack
 from mlagents.trainers.trajectory import ObsUtil

+
+from mlagents.trainers.torch.distributions import GaussianDistribution
+
 EPSILON = 1e-6  # Small value to avoid divide by zero

 logger = get_logger(__name__)

 class DiverseNetwork(torch.nn.Module):
    EPSILON = 1e-10
-    STRENGTH = 0.1
+    STRENGTH = 1.0
+        self._use_continuous = True
        state_encoder_settings = settings
        if state_encoder_settings.memory is not None:
            state_encoder_settings.memory = None
            )
        else:
            self._encoder = NetworkBody(new_spec, state_encoder_settings)
-        self._last_layer = torch.nn.Linear(
-            state_encoder_settings.hidden_units, self.diverse_size
-        )
+        if self._use_continuous:
+            self._last_layer = GaussianDistribution(state_encoder_settings.hidden_units, self.diverse_size)
+        else:
+            self._last_layer = torch.nn.Linear(
+                state_encoder_settings.hidden_units, self.diverse_size
+            )
        self._diverse_index = -1
        self._max_index = len(specs.observation_specs)
        for i, spec in enumerate(specs.observation_specs):
-    def predict(self, obs_input, action_input, detach_action=False) -> torch.Tensor:
+    def _predict(self, obs_input, action_input, detach_action=False) -> torch.Tensor:
        # Convert to tensors
        tensor_obs = [
            obs
            hidden, _ = self._encoder.forward(tensor_obs)

        # add a VAE (like in VAIL ?)
-
-        prediction = torch.softmax(self._last_layer(hidden), dim=1)
+        if self._use_continuous:
+            prediction = self._last_layer(hidden)
+        else:
+            prediction = torch.softmax(self._last_layer(hidden), dim=1)
        return prediction

    def copy_normalization(self, thing):
        truth = obs_input[self._diverse_index]
-        prediction = self.predict(obs_input, action_input, detach_action)
-        rewards = torch.log(torch.sum((prediction * truth), dim=1) + self.EPSILON)
+        prediction = self._predict(obs_input, action_input, detach_action)
+        if self._use_continuous:
+            rewards = torch.sum(prediction.log_prob(truth), dim=1)
+        else:
+            rewards = torch.log(torch.sum((prediction * truth), dim=1) + self.EPSILON)
        return rewards

    def loss(self, obs_input, action_input, masks, detach_action=True) -> torch.Tensor:
                with torch.no_grad():
                    v_backup = (
                        min_policy_qs[name]
-                        - torch.sum(_cont_ent_coef * log_probs.continuous_tensor, dim=1)
+                        - _cont_ent_coef * torch.sum(log_probs.continuous_tensor, dim=1)
                        + self._mede_network.STRENGTH
                        * self._mede_network.rewards(obs, act)
                    )
                value_losses.append(value_loss)
        else:
+            print("MEDE not being added to V")
            disc_log_probs = log_probs.all_discrete_tensor
            branched_per_action_ent = ModelUtils.break_into_branches(
                disc_log_probs * disc_log_probs.exp(),
            all_mean_q1 = mean_q1
        if self._action_spec.continuous_size > 0:
            cont_log_probs = log_probs.continuous_tensor
-            batch_policy_loss += torch.mean(
-                _cont_ent_coef * cont_log_probs - all_mean_q1.unsqueeze(1), dim=1
-            ) - self._mede_network.STRENGTH * self._mede_network.rewards(obs, act)
+            #batch_policy_loss += torch.mean(
+            batch_policy_loss += _cont_ent_coef * torch.sum(cont_log_probs, dim=1) - all_mean_q1 - self._mede_network.STRENGTH * self._mede_network.rewards(obs, act)
        policy_loss = ModelUtils.masked_mean(batch_policy_loss, loss_masks)

        return policy_loss
        if self._action_spec.continuous_size > 0:
            with torch.no_grad():
                cont_log_probs = log_probs.continuous_tensor
-                target_current_diff = torch.sum(
-                    cont_log_probs + self.target_entropy.continuous, dim=1
-                )
+                #target_current_diff = torch.sum(
+                #    cont_log_probs + self.target_entropy.continuous, dim=1
+                #)
+
+                target_current_diff = torch.sum(cont_log_probs, dim=1) + self.target_entropy.continuous
+
            # We update all the _cont_ent_coef as one block
            entropy_loss += -1 * ModelUtils.masked_mean(
                _cont_ent_coef * target_current_diff, loss_masks
        mede_loss.backward()
        self._mede_optimizer.step()

+        with torch.no_grad():
+            mede_rewards = torch.mean(self._mede_network.rewards(current_obs, sampled_actions)).item()
        # Update target network
        ModelUtils.soft_update(self._critic, self.target_network, self.tau)
        update_stats = {
            ).item(),
            "Policy/Learning Rate": decay_lr,
            "Policy/MEDE Loss": mede_loss.item(),
+            "Policy/MEDE Rewards": mede_rewards,
        }

        return update_stats
--- a/Project/Assets/ML-Agents/Examples/Crawler/Prefabs/CubePlatform.prefab
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--- a/Project/Assets/ML-Agents/Examples/Crawler/Prefabs/CubePlatform.prefab.meta
+++ b/Project/Assets/ML-Agents/Examples/Crawler/Prefabs/CubePlatform.prefab.meta
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--- a/Project/Assets/ML-Agents/Examples/Crawler/Scenes/CubeMove.unity
+++ b/Project/Assets/ML-Agents/Examples/Crawler/Scenes/CubeMove.unity
--- a/Project/Assets/ML-Agents/Examples/Crawler/Scenes/CubeMove.unity.meta
+++ b/Project/Assets/ML-Agents/Examples/Crawler/Scenes/CubeMove.unity.meta
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--- a/Project/Assets/ML-Agents/Examples/Crawler/Scripts/CubeAgent.cs
+++ b/Project/Assets/ML-Agents/Examples/Crawler/Scripts/CubeAgent.cs
+using System;
+using UnityEngine;
+using Unity.MLAgents;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Policies;
+using Unity.MLAgents.Sensors;
+
+using Random = UnityEngine.Random;
+
+public class CubeAgent : Agent
+{
+    //float m_LateralSpeed;
+    //float m_ForwardSpeed;
+
+
+    [HideInInspector]
+    public Rigidbody agentRb;
+    BehaviorParameters m_BehaviorParameters;
+    public Vector3 initialPos;
+    public GameObject area;
+
+    int m_NumDiv = 4;
+    EnvironmentParameters m_ResetParams;
+    public Transform TargetPrefab; //Target prefab to use in Dynamic envs
+    private Transform m_Target; //Target the agent will walk towards during training.
+
+    float[] continuousDiv;
+    VectorSensorComponent m_DiversitySettingSensor;
+    public bool useContinuous = false;
+    public int m_DiversitySetting = 0;
+    [Range(-1f, 1f)]
+    public float div1;
+    [Range(-1f, 1f)]
+    public float div2;
+    [Range(-1f, 1f)]
+    public float div3;
+    [Range(-1f, 1f)]
+    public float div4;
+
+
+
+    public override void Initialize()
+    {
+        continuousDiv = new float[m_NumDiv];
+        SpawnTarget(TargetPrefab, transform.position); //spawn target
+        m_BehaviorParameters = gameObject.GetComponent<BehaviorParameters>();
+        //m_LateralSpeed = 1.0f;
+        //m_ForwardSpeed = 1.0f;
+        GetComponent<VectorSensorComponent>().CreateSensors();
+        m_DiversitySettingSensor = GetComponent<VectorSensorComponent>();
+        agentRb = GetComponent<Rigidbody>();
+
+    }
+
+
+    void SpawnTarget(Transform prefab, Vector3 pos)
+    {
+        m_Target = Instantiate(prefab, pos, Quaternion.identity, transform.parent);
+    }
+
+    public void MoveAgent(ActionBuffers actionBuffers)
+    {
+        var dirToGo = Vector3.zero;
+        var rotateDir = Vector3.zero;
+
+
+        var continuousActions = actionBuffers.ContinuousActions;
+        var forward = Mathf.Clamp(continuousActions[0], -1f, 1f);
+        var right = Mathf.Clamp(continuousActions[1], -1f, 1f);
+        var rotate = Mathf.Clamp(continuousActions[2], -1f, 1f);
+        dirToGo = transform.forward * forward;
+        dirToGo += transform.right * right;
+        rotateDir = -transform.up * rotate;
+
+        transform.Rotate(rotateDir, Time.deltaTime * 100f);
+        agentRb.AddForce(dirToGo,
+            ForceMode.VelocityChange);
+    }
+
+    public override void OnActionReceived(ActionBuffers actionBuffers)
+    {
+        MoveAgent(actionBuffers);
+    }
+
+    void OnCollisionEnter(Collision col)
+    {
+        if (col.transform.CompareTag("target"))
+        {
+            SetReward(1f);
+            //EndEpisode();
+        }
+
+    }
+
+    
+    public override void CollectObservations(VectorSensor sensor)
+    {
+        //AddReward(-1f);
+        m_DiversitySettingSensor.GetSensor().Reset();
+        if (useContinuous)
+        {
+            Array.Clear(continuousDiv, 0, m_NumDiv);
+            continuousDiv[0] = div1;
+            continuousDiv[1] = div2;
+            continuousDiv[2] = div3;
+            continuousDiv[3] = div4;
+            m_DiversitySettingSensor.GetSensor().AddObservation(continuousDiv);
+        }
+        else
+        {
+            m_DiversitySettingSensor.GetSensor().AddOneHotObservation(m_DiversitySetting, m_NumDiv);
+        }
+
+
+        //velocity we want to match
+        //var velGoal = cubeForward * TargetWalkingSpeed;
+        //ragdoll's avg vel
+        //var avgVel = GetAvgVelocity();
+
+        //current ragdoll velocity. normalized
+        //sensor.AddObservation(Vector3.Distance(velGoal, avgVel));
+        //avg body vel relative to cube
+        //vel goal relative to cube
+        //rotation delta
+
+        //Add pos of target relative to orientation cube
+        //sensor.AddObservation(Vector3.Dot(agentRb.velocity, agentRb.transform.forward) / 80f);
+        //sensor.AddObservation(Vector3.Dot(agentRb.velocity, agentRb.transform.right) / 80f);
+        //sensor.AddObservation(agentRb.transform.forward.x);
+        //sensor.AddObservation(agentRb.transform.forward.z);
+//        sensor.AddObservation(transform.InverseTransformPoint(m_Target.transform.position).x / 50f);
+//        sensor.AddObservation(transform.InverseTransformPoint(m_Target.transform.position).z / 50f);
+        //sensor.AddObservation((transform.position.x - m_Target.transform.position.x) / 50f);
+        //sensor.AddObservation((transform.position.z - m_Target.transform.position.z) / 50f);
+        //sensor.AddObservation(transform.InverseTransformPoint(m_Target.transform.position).z / 50f);
+
+        //sensor.AddObservation((transform.position.x - area.transform.position.x) / 50f);
+        //sensor.AddObservation((transform.position.z - area.transform.position.z) / 50f);
+
+    }
+
+    public override void Heuristic(in ActionBuffers actionsOut)
+    {
+        var continuousActionsOut = actionsOut.ContinuousActions;
+        if (Input.GetKey(KeyCode.D))
+        {
+            continuousActionsOut[2] = -1;
+        }
+        if (Input.GetKey(KeyCode.W))
+        {
+            continuousActionsOut[0] = 1;
+        }
+        if (Input.GetKey(KeyCode.A))
+        {
+            continuousActionsOut[2] = 1;
+        }
+        if (Input.GetKey(KeyCode.Q))
+        {
+            continuousActionsOut[1] = -1;
+        }
+        if (Input.GetKey(KeyCode.E))
+        {
+            continuousActionsOut[1] = 1;
+        }
+
+        if (Input.GetKey(KeyCode.S))
+        {
+            continuousActionsOut[0] = -1;
+        }
+
+           }
+    /// <summary>
+    /// Used to provide a "kick" to the ball.
+    /// </summary>
+    
+    public override void OnEpisodeBegin()
+    {
+        if (useContinuous)
+        {
+            Array.Clear(continuousDiv, 0, m_NumDiv);
+            div1 = Random.Range(-1f, 1f);
+            div2 = Random.Range(-1f, 1f);
+            div3 = Random.Range(-1f, 1f);
+            div4 = Random.Range(-1f, 1f);
+            continuousDiv[0] = div1;
+            continuousDiv[1] = div2;
+            continuousDiv[2] = div3;
+            continuousDiv[3] = div4;
+        }
+        else
+        {
+            m_DiversitySetting = Random.Range(0, m_NumDiv);
+        }
+
+        agentRb.velocity = Vector3.zero;
+        transform.position = new Vector3(Random.Range(-3f, 3f),
+            1f, Random.Range(-3f, 3f))
+            + area.transform.position;
+
+        transform.rotation = Quaternion.Euler(new Vector3(0f, Random.Range(0, 360)));
+    }
+
+
+}
--- a/Project/Assets/ML-Agents/Examples/Crawler/Scripts/CubeAgent.cs.meta
+++ b/Project/Assets/ML-Agents/Examples/Crawler/Scripts/CubeAgent.cs.meta
+fileFormatVersion: 2
+guid: c38fe3d08399d4266b7feff82f7a46e7
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
--- a/Project/Assets/ML-Agents/Examples/Crawler/TFModels/CubeDiv-984000.onnx
+++ b/Project/Assets/ML-Agents/Examples/Crawler/TFModels/CubeDiv-984000.onnx
--- a/Project/Assets/ML-Agents/Examples/Crawler/TFModels/CubeDiv-984000.onnx.meta
+++ b/Project/Assets/ML-Agents/Examples/Crawler/TFModels/CubeDiv-984000.onnx.meta
+fileFormatVersion: 2
+guid: 4ccca19b2537343baa8dc44f367b720f
+ScriptedImporter:
+  internalIDToNameTable: []
+  externalObjects: {}
+  serializedVersion: 2
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 
+  script: {fileID: 11500000, guid: 683b6cb6d0a474744822c888b46772c9, type: 3}
+  optimizeModel: 1
+  forceArbitraryBatchSize: 1
+  treatErrorsAsWarnings: 0
+  importMode: 1
--- a/config/sac/Cube.yaml
+++ b/config/sac/Cube.yaml
+behaviors:
+  CubeDiv:
+    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: false
+      hidden_units: 512
+      num_layers: 2
+      vis_encode_type: simple
+      goal_conditioning_type: none
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 10.0
+    keep_checkpoints: 5
+    max_steps: 5000000
+    time_horizon: 1000
+    summary_freq: 3000