Make the DecisionRequester public and customizable. (#3716)

* [API] Make the DecisionRequester public and add a delegate to its API to allow users to customize it's behavior.

- Rename Academy.AgentSetStatus to Academy.AgentPreStep and make it public.
- Fix Unity library cache issues for backwards compatibility tests.
- Collect standalone build and logs to artifacts for standalone build jobs.
- cat standalone build log if the build fails.
- Default verbose to False for standalone build test.

* disable backward compatibility test, bump communication version.

* still run training tests on latest.

* fix yml parse error.

.yamato/gym-interface-test.yml

   commands:
     - pip install pyyaml
     - python -u -m ml-agents.tests.yamato.setup_venv
-    - ./venv/bin/python ml-agents/tests/yamato/scripts/run_gym.py --env=Project/testPlayer-Basic
+    - ./venv/bin/python ml-agents/tests/yamato/scripts/run_gym.py --env=artifacts/testPlayer-Basic
   dependencies:
     - .yamato/standalone-build-test.yml#test_mac_standalone_{{ editor.version }}
   triggers:

.yamato/protobuf-generation-test.yml

         - "protobuf-definitions/*.md"
         - "protobuf-definitions/**/*.md"
   artifacts:
-    dist:
+    patch:
-        - "artifacts/*"
+        - "artifacts/*.*"

.yamato/python-ll-api-test.yml

     - python -u -m ml-agents.tests.yamato.setup_venv
     - ./venv/bin/python ml-agents/tests/yamato/scripts/run_llapi.py
   dependencies:
-    - .yamato/standalone-build-test.yml#test_mac_standalone_{{ editor.version }} --env=Project/testPlayer
+    - .yamato/standalone-build-test.yml#test_mac_standalone_{{ editor.version }}
   triggers:
     cancel_old_ci: true
     changes:

.yamato/standalone-build-test.yml

         - "com.unity.ml-agents/*.md"
         - "com.unity.ml-agents/**/*.md"
   artifacts:
+    logs:
+      paths:
+        - "artifacts/standalone_build.txt"
-        - "Project/testPlayer*/**"
+        - "artifacts/testPlayer*/**"
 {% endfor %}

.yamato/training-int-tests.yml

     # Backwards-compatibility tests.
     # If we make a breaking change to the communication protocol, these will need
     # to be disabled until the next release.
-    - python -u -m ml-agents.tests.yamato.training_int_tests --python=0.15.0
-    - python -u -m ml-agents.tests.yamato.training_int_tests --csharp=0.15.0
+    # - python -u -m ml-agents.tests.yamato.training_int_tests --python=0.15.0
+    # - python -u -m ml-agents.tests.yamato.training_int_tests --csharp=0.15.0
   dependencies:
     - .yamato/standalone-build-test.yml#test_mac_standalone_{{ editor.version }}
   triggers:
         - "com.unity.ml-agents/*.md"
         - "com.unity.ml-agents/**/*.md"
   artifacts:
-    unit:
+    logs:
+      paths:
+        - "artifacts/standalone_build.txt"
+    standalonebuild:
-        - "artifacts/**"
+        - "artifacts/testplayer*/**"
 {% endfor %}

com.unity.ml-agents/CHANGELOG.md

  - Removed the multi-agent gym option from the gym wrapper. For multi-agent scenarios, use the [Low Level Python API](Python-API.md).
  - The low level Python API has changed. You can look at the document [Low Level Python API documentation](Python-API.md) for more information. If you use `mlagents-learn` for training, this should be a transparent change.
  - Added ability to start training (initialize model weights) from a previous run ID. (#3710)
+ - The internal event `Academy.AgentSetStatus` was renamed to `Academy.AgentPreStep` and made public.
+ - The offset logic was removed from DecisionRequester.
 
 ### Minor Changes
  - Format of console output has changed slightly and now matches the name of the model/summary directory. (#3630, #3616)

com.unity.ml-agents/Runtime/Academy.cs

         /// on each side, although we may allow some flexibility in the future.
         /// This should be incremented whenever a change is made to the communication protocol.
         /// </summary>
-        const string k_ApiVersion = "0.15.0";
+        const string k_ApiVersion = "0.16.0";
 
         /// <summary>
         /// Unity package version of com.unity.ml-agents.
         // This will mark the Agent as Done if it has reached its maxSteps.
         internal event Action AgentIncrementStep;
 
-        // Signals to all the agents at each environment step along with the
-        // Academy's maxStepReached, done and stepCount values. The agents rely
-        // on this event to update their own values of max step reached and done
-        // in addition to aligning on the step count of the global episode.
-        internal event Action<int> AgentSetStatus;
+
+        /// <summary>
+        /// Signals to all of the <see cref="Agent"/>s that their step is about to begin.
+        /// This is a good time for an <see cref="Agent"/> to decide if it would like to
+        /// call <see cref="Agent.RequestDecision"/> or <see cref="Agent.RequestAction"/>
+        /// for this step.  Any other pre-step setup could be done during this even as well.
+        /// </summary>
+        public event Action<int> AgentPreStep;
 
         // Signals to all the agents at each environment step so they can send
         // their state to their Policy if they have requested a decision.
         {
             DecideAction = () => {};
             DestroyAction = () => {};
-            AgentSetStatus = i => {};
+            AgentPreStep = i => {};
             AgentSendState = () => {};
             AgentAct = () => {};
             AgentForceReset = () => {};
                 ForcedFullReset();
             }
 
-            AgentSetStatus?.Invoke(m_StepCount);
+            AgentPreStep?.Invoke(m_StepCount);
 
             m_StepCount += 1;
             m_TotalStepCount += 1;

com.unity.ml-agents/Runtime/DecisionRequester.cs

+using System;
 using UnityEngine;
 using UnityEngine.Serialization;
 
     /// at regular intervals.
     /// </summary>
     [AddComponentMenu("ML Agents/Decision Requester", (int)MenuGroup.Default)]
-    internal class DecisionRequester : MonoBehaviour
+    [RequireComponent(typeof(Agent))]
+    public class DecisionRequester : MonoBehaviour
-        /// that the Agent will request a decision every 5 Academy steps.
-        /// </summary>
+        /// that the Agent will request a decision every 5 Academy steps. /// </summary>
         [Range(1, 20)]
         [Tooltip("The frequency with which the agent requests a decision. A DecisionPeriod " +
                  "of 5 means that the Agent will request a decision every 5 Academy steps.")]
         [FormerlySerializedAs("RepeatAction")]
         public bool TakeActionsBetweenDecisions = true;
 
-        /// <summary>
-        /// Whether or not the Agent decisions should start at an offset (different for each agent).
-        /// This does not affect <see cref="DecisionPeriod"/>. Turning this on will distribute
-        /// the decision-making computations for all the agents across multiple Academy steps.
-        /// This can be valuable in scenarios where you have many agents in the scene, particularly
-        /// during the inference phase.
-        /// </summary>
-        [Tooltip("Whether or not Agent decisions should start at an offset.")]
-        public bool offsetStep;
-
+        [NonSerialized]
-        int m_Offset;
-            m_Offset = offsetStep ? gameObject.GetInstanceID() : 0;
-            Academy.Instance.AgentSetStatus += MakeRequests;
+            Debug.Assert(m_Agent != null, "Agent component was not found on this gameObject and is required.");
+            Academy.Instance.AgentPreStep += MakeRequests;
         }
 
         void OnDestroy()
-                Academy.Instance.AgentSetStatus -= MakeRequests;
+                Academy.Instance.AgentPreStep -= MakeRequests;
-        void MakeRequests(int count)
+        /// <summary>
+        /// Method that hooks into the Academy in order inform the Agent on whether or not it should request a
+        /// decision, and whether or not it should take actions between decisions.
+        /// </summary>
+        /// <param name="academyStepCount">The current step count of the academy.</param>
+        void MakeRequests(int academyStepCount)
-            if ((count + m_Offset) % DecisionPeriod == 0)
+            if (academyStepCount % DecisionPeriod == 0)
             {
                 m_Agent?.RequestDecision();
             }

com.unity.ml-agents/Tests/Editor/PublicAPI/PublicApiValidation.cs

 using MLAgents.Sensors;
 using NUnit.Framework;
 using UnityEngine;
+using UnityEngine.TestTools;
 
 namespace MLAgentsExamples
 {
             sensorComponent.observationStacks = 2;
 
             sensorComponent.CreateSensor();
-        }
-
-        class PublicApiAgent : Agent
-        {
-            public int numHeuristicCalls;
-
-            public override float[] Heuristic()
-            {
-                numHeuristicCalls++;
-                return base.Heuristic();
-            }
-        }
-
-        // Simple SensorComponent that sets up a StackingSensor
-        class StackingComponent : SensorComponent
-        {
-            public SensorComponent wrappedComponent;
-            public int numStacks;
-
-            public override ISensor CreateSensor()
-            {
-                var wrappedSensor = wrappedComponent.CreateSensor();
-                return new StackingSensor(wrappedSensor, numStacks);
-            }
-
-            public override int[] GetObservationShape()
-            {
-                int[] shape = (int[]) wrappedComponent.GetObservationShape().Clone();
-                for (var i = 0; i < shape.Length; i++)
-                {
-                    shape[i] *= numStacks;
-                }
-
-                return shape;
-            }
-        }
-
-
-        [Test]
-        public void CheckSetupAgent()
-        {
-            var gameObject = new GameObject();
-
-            var behaviorParams = gameObject.AddComponent<BehaviorParameters>();
-            behaviorParams.brainParameters.vectorObservationSize = 3;
-            behaviorParams.brainParameters.numStackedVectorObservations = 2;
-            behaviorParams.brainParameters.vectorActionDescriptions = new[] { "TestActionA", "TestActionB" };
-            behaviorParams.brainParameters.vectorActionSize = new[] { 2, 2 };
-            behaviorParams.brainParameters.vectorActionSpaceType = SpaceType.Discrete;
-            behaviorParams.behaviorName = "TestBehavior";
-            behaviorParams.TeamId = 42;
-            behaviorParams.useChildSensors = true;
-
-            var agent = gameObject.AddComponent<PublicApiAgent>();
-            // Make sure we can set the behavior type correctly after the agent is added
-            behaviorParams.behaviorType = BehaviorType.InferenceOnly;
-            // Can't actually create an Agent with InferenceOnly and no model, so change back
-            behaviorParams.behaviorType = BehaviorType.Default;
-
-            // TODO -  not internal yet
-            // var decisionRequester = gameObject.AddComponent<DecisionRequester>();
-            // decisionRequester.DecisionPeriod = 2;
-
-            var sensorComponent = gameObject.AddComponent<RayPerceptionSensorComponent3D>();
-            sensorComponent.sensorName = "ray3d";
-            sensorComponent.detectableTags = new List<string> { "Player", "Respawn" };
-            sensorComponent.raysPerDirection = 3;
-
-            // Make a StackingSensor that wraps the RayPerceptionSensorComponent3D
-            // This isn't necessarily practical, just to ensure that it can be done
-            var wrappingSensorComponent = gameObject.AddComponent<StackingComponent>();
-            wrappingSensorComponent.wrappedComponent = sensorComponent;
-            wrappingSensorComponent.numStacks = 3;
-
-            // ISensor isn't set up yet.
-            Assert.IsNull(sensorComponent.raySensor);
-
-            agent.LazyInitialize();
-            // Make sure we can set the behavior type correctly after the agent is initialized
-            // (this creates a new policy).
-            behaviorParams.behaviorType = BehaviorType.HeuristicOnly;
-
-            // Initialization should set up the sensors
-            Assert.IsNotNull(sensorComponent.raySensor);
-
-            // Let's change the inference device
-            var otherDevice = behaviorParams.inferenceDevice == InferenceDevice.CPU ? InferenceDevice.GPU : InferenceDevice.CPU;
-            agent.SetModel(behaviorParams.behaviorName, behaviorParams.model, otherDevice);
-
-            agent.AddReward(1.0f);
-
-            agent.RequestAction();
-            agent.RequestDecision();
-
-            Academy.Instance.AutomaticSteppingEnabled = false;
-            Academy.Instance.EnvironmentStep();
-
-            var actions = agent.GetAction();
-            // default Heuristic implementation should return zero actions.
-            Assert.AreEqual(new[] {0.0f, 0.0f}, actions);
-            Assert.AreEqual(1, agent.numHeuristicCalls);
         }
     }
 }

ml-agents-envs/mlagents_envs/environment.py

     # Currently we require strict equality between the communication protocol
     # on each side, although we may allow some flexibility in the future.
     # This should be incremented whenever a change is made to the communication protocol.
-    API_VERSION = "0.15.0"
+    API_VERSION = "0.16.0"
 
     # Default port that the editor listens on. If an environment executable
     # isn't specified, this port will be used.

ml-agents/tests/yamato/scripts/run_llapi.py

 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument("--env", default="Project/testPlayer")
+    parser.add_argument("--env", default="artifacts/testPlayer")
     args = parser.parse_args()
     main(args.env)

ml-agents/tests/yamato/standalone_build_tests.py

         executable_name = "testPlayer-" + executable_name
 
     returncode = run_standalone_build(
-        base_path, verbose=True, output_path=executable_name, scene_path=scene_path
+        base_path, output_path=executable_name, scene_path=scene_path
     )
 
     if returncode == 0:

ml-agents/tests/yamato/training_int_tests.py

 
 from .yamato_utils import (
     get_base_path,
+    get_base_output_path,
     run_standalone_build,
     init_venv,
     override_config_file,
     if csharp_version is not None:
         # We can't rely on the old C# code recognizing the commandline argument to set the output
         # So rename testPlayer (containing the most recent build) to something else temporarily
-        full_player_path = os.path.join("Project", "testPlayer.app")
-        temp_player_path = os.path.join("Project", "temp_testPlayer.app")
-        final_player_path = os.path.join("Project", f"testPlayer_{csharp_version}.app")
+        artifact_path = get_base_output_path()
+        full_player_path = os.path.join(artifact_path, "testPlayer.app")
+        temp_player_path = os.path.join(artifact_path, "temp_testPlayer.app")
+        final_player_path = os.path.join(
+            artifact_path, f"testPlayer_{csharp_version}.app"
+        )
 
         os.rename(full_player_path, temp_player_path)
 
     )
 
     mla_learn_cmd = (
-        f"mlagents-learn override.yaml --train --env=Project/{standalone_player_path} "
+        f"mlagents-learn override.yaml --train --env="
+        f"{os.path.join(get_base_output_path(), standalone_player_path)} "
         f"--run-id={run_id} --no-graphics --env-args -logFile -"
     )  # noqa
     res = subprocess.run(

ml-agents/tests/yamato/yamato_utils.py

 import os
+import shutil
 import subprocess
 import yaml
 from typing import List, Optional
     return os.getcwd()
 
 
+def get_base_output_path():
+    """"
+    Returns the artifact folder to use for yamato jobs.
+    """
+    return os.path.join(get_base_path(), "artifacts")
+
+
+    log_output_path: str = f"{get_base_output_path()}/standalone_build.txt",
-    Run BuildStandalonePlayerOSX test to produce a player. The location defaults to Project/testPlayer.
+    Run BuildStandalonePlayerOSX test to produce a player. The location defaults to
+    artifacts/standalone_build/testPlayer.
     """
     unity_exe = get_unity_executable_path()
     print(f"Running BuildStandalonePlayerOSX via {unity_exe}")
         "-executeMethod",
         "MLAgents.StandaloneBuildTest.BuildStandalonePlayerOSX",
     ]
-    if verbose:
-        test_args += ["-logfile", "-"]
+
+    os.makedirs(os.path.dirname(log_output_path), exist_ok=True)
+    subprocess.run(["touch", log_output_path])
+    test_args += ["-logfile", log_output_path]
+
+        output_path = os.path.join(get_base_output_path(), output_path)
+        os.makedirs(os.path.dirname(output_path), exist_ok=True)
     if scene_path is not None:
         test_args += ["--mlagents-build-scene-path", scene_path]
     print(f"{' '.join(test_args)} ...")
+
+    # Copy the default build name into the artifacts folder.
+    if output_path is None and res.returncode == 0:
+        shutil.move(
+            os.path.join(base_path, "Project", "testPlayer.app"),
+            os.path.join(get_base_output_path(), "testPlayer.app"),
+        )
+
+    # Print if we fail or want verbosity.
+    if verbose or res.returncode != 0:
+        subprocess.run(["cat", log_output_path])
+
     return res.returncode
 
 
     """
     if csharp_version is None:
         return
+
+        subprocess.check_call(f"rm -rf {csharp_dir}", shell=True)
         subprocess.check_call(
             f"git checkout {csharp_version} -- {csharp_dir}", shell=True
         )
     """
     subprocess.check_call("git reset HEAD .", shell=True)
     subprocess.check_call("git checkout -- .", shell=True)
+    # Ensure the cache isn't polluted with old compiled assemblies.
+    subprocess.check_call(f"rm -rf Project/Library", shell=True)
 
 
 def override_config_file(src_path, dest_path, **kwargs):

com.unity.ml-agents/Tests/Runtime/RuntimeAPITest.cs

+#if UNITY_INCLUDE_TESTS
+using System.Collections;
+using System.Collections.Generic;
+using MLAgents;
+using MLAgents.Policies;
+using MLAgents.Sensors;
+using NUnit.Framework;
+using UnityEngine;
+using UnityEngine.TestTools;
+
+namespace Tests
+{
+
+    public class PublicApiAgent : Agent
+    {
+        public int numHeuristicCalls;
+
+        public override float[] Heuristic()
+        {
+            numHeuristicCalls++;
+            return base.Heuristic();
+        }
+    }// Simple SensorComponent that sets up a StackingSensor
+    public class StackingComponent : SensorComponent
+    {
+        public SensorComponent wrappedComponent;
+        public int numStacks;
+
+        public override ISensor CreateSensor()
+        {
+            var wrappedSensor = wrappedComponent.CreateSensor();
+            return new StackingSensor(wrappedSensor, numStacks);
+        }
+
+        public override int[] GetObservationShape()
+        {
+            int[] shape = (int[]) wrappedComponent.GetObservationShape().Clone();
+            for (var i = 0; i < shape.Length; i++)
+            {
+                shape[i] *= numStacks;
+            }
+
+            return shape;
+        }
+    }
+
+    public class RuntimeApiTest
+    {
+        [SetUp]
+        public static void Setup()
+        {
+            Academy.Instance.AutomaticSteppingEnabled = false;
+        }
+
+        [UnityTest]
+        public IEnumerator RuntimeApiTestWithEnumeratorPasses()
+        {
+            var gameObject = new GameObject();
+
+            var behaviorParams = gameObject.AddComponent<BehaviorParameters>();
+            behaviorParams.brainParameters.vectorObservationSize = 3;
+            behaviorParams.brainParameters.numStackedVectorObservations = 2;
+            behaviorParams.brainParameters.vectorActionDescriptions = new[] { "TestActionA", "TestActionB" };
+            behaviorParams.brainParameters.vectorActionSize = new[] { 2, 2 };
+            behaviorParams.brainParameters.vectorActionSpaceType = SpaceType.Discrete;
+            behaviorParams.behaviorName = "TestBehavior";
+            behaviorParams.TeamId = 42;
+            behaviorParams.useChildSensors = true;
+
+
+            // Can't actually create an Agent with InferenceOnly and no model, so change back
+            behaviorParams.behaviorType = BehaviorType.Default;
+
+            var sensorComponent = gameObject.AddComponent<RayPerceptionSensorComponent3D>();
+            sensorComponent.sensorName = "ray3d";
+            sensorComponent.detectableTags = new List<string> { "Player", "Respawn" };
+            sensorComponent.raysPerDirection = 3;
+
+            // Make a StackingSensor that wraps the RayPerceptionSensorComponent3D
+            // This isn't necessarily practical, just to ensure that it can be done
+            var wrappingSensorComponent = gameObject.AddComponent<StackingComponent>();
+            wrappingSensorComponent.wrappedComponent = sensorComponent;
+            wrappingSensorComponent.numStacks = 3;
+
+            // ISensor isn't set up yet.
+            Assert.IsNull(sensorComponent.raySensor);
+
+
+            // Make sure we can set the behavior type correctly after the agent is initialized
+            // (this creates a new policy).
+            behaviorParams.behaviorType = BehaviorType.HeuristicOnly;
+
+            // Agent needs to be added after everything else is setup.
+            var agent = gameObject.AddComponent<PublicApiAgent>();
+
+            // DecisionRequester has to be added after Agent.
+            var decisionRequester = gameObject.AddComponent<DecisionRequester>();
+            decisionRequester.DecisionPeriod = 2;
+            decisionRequester.TakeActionsBetweenDecisions = true;
+
+
+            // Initialization should set up the sensors
+            Assert.IsNotNull(sensorComponent.raySensor);
+
+            // Let's change the inference device
+            var otherDevice = behaviorParams.inferenceDevice == InferenceDevice.CPU ? InferenceDevice.GPU : InferenceDevice.CPU;
+            agent.SetModel(behaviorParams.behaviorName, behaviorParams.model, otherDevice);
+
+            agent.AddReward(1.0f);
+
+            // skip a frame.
+            yield return null;
+
+            Academy.Instance.EnvironmentStep();
+
+            var actions = agent.GetAction();
+            // default Heuristic implementation should return zero actions.
+            Assert.AreEqual(new[] {0.0f, 0.0f}, actions);
+            Assert.AreEqual(1, agent.numHeuristicCalls);
+
+            Academy.Instance.EnvironmentStep();
+            Assert.AreEqual(1, agent.numHeuristicCalls);
+
+            Academy.Instance.EnvironmentStep();
+            Assert.AreEqual(2, agent.numHeuristicCalls);
+        }
+    }
+}
+#endif

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