add base team manager

com.unity.ml-agents.extensions/Runtime/Teams/BaseTeamManager.cs

-using System.Collections.Generic;
-using Unity.MLAgents;
-using Unity.MLAgents.Sensors;
-
-        private readonly string m_Id = System.Guid.NewGuid().ToString();
+        readonly int m_Id = TeamManagerIdCounter.GetTeamManagerId();
-        public virtual void RegisterAgent(Agent agent)
-        {
-        }
+        public virtual void RegisterAgent(Agent agent) { }
-        public virtual void OnAgentDone(Agent agent, Agent.DoneReason doneReason, List<ISensor> sensors)
-        {
-            // Possible implementation - save reference to Agent's IPolicy so that we can repeatedly
-            // call IPolicy.RequestDecision on behalf of the Agent after it's dead
-            // If so, we'll need dummy sensor impls with the same shape as the originals.
-            agent.SendDoneToTrainer();
-        }
-
-        public virtual void AddTeamReward(float reward)
-        {
-
-        }
-
-        public string GetId()
+        public int GetId()
         {
             return m_Id;
         }

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

         /// <summary>
         /// Team Manager identifier.
         /// </summary>
-        public string teamManagerId;
+        public int teamManagerId;
 
         public void ClearActions()
         {
         /// </summary>
         float[] m_LegacyActionCache;
 
-        private ITeamManager m_TeamManager;
+        ITeamManager m_TeamManager;
 
         /// <summary>
         /// Called when the attached [GameObject] becomes enabled and active.
                 new int[m_ActuatorManager.NumDiscreteActions]
             );
 
-            if (m_TeamManager != null)
-            {
-                m_Info.teamManagerId = m_TeamManager.GetId();
-            }
+            m_Info.teamManagerId = m_TeamManager == null ? -1 : m_TeamManager.GetId();
 
             // The first time the Academy resets, all Agents in the scene will be
             // forced to reset through the <see cref="AgentForceReset"/> event.
             m_Info.reward = m_Reward;
             m_Info.done = true;
             m_Info.maxStepReached = doneReason == DoneReason.MaxStepReached;
+            m_Info.teamManagerId = m_TeamManager == null ? -1 : m_TeamManager.GetId();
             if (collectObservationsSensor != null)
             {
                 // Make sure the latest observations are being passed to training.
                 }
             }
             // Request the last decision with no callbacks
-            if (m_TeamManager != null)
-            {
-                // Send final observations to TeamManager if it exists.
-                // The TeamManager is responsible to keeping track of the Agent after it's
-                // done, including propagating any "posthumous" rewards.
-                m_TeamManager.OnAgentDone(this, doneReason, sensors);
-            }
-            else
-            {
-                SendDoneToTrainer();
-            }
+            // We request a decision so Python knows the Agent is done immediately
+            m_Brain?.RequestDecision(m_Info, sensors);
+            ResetSensors();
 
             // We also have to write any to any DemonstationStores so that they get the "done" flag.
             foreach (var demoWriter in DemonstrationWriters)
             m_Info.storedActions.Clear();
         }
 
-        public void SendDoneToTrainer()
-        {
-            // We request a decision so Python knows the Agent is done immediately
-            m_Brain?.RequestDecision(m_Info, sensors);
-            ResetSensors();
-        }
 
         /// <summary>
         /// Updates the Model assigned to this Agent instance.
             m_Info.done = false;
             m_Info.maxStepReached = false;
             m_Info.episodeId = m_EpisodeId;
+            m_Info.teamManagerId = m_TeamManager == null ? -1 : m_TeamManager.GetId();
 
             using (TimerStack.Instance.Scoped("RequestDecision"))
             {
         public void SetTeamManager(ITeamManager teamManager)
         {
             m_TeamManager = teamManager;
-            m_Info.teamManagerId = teamManager?.GetId();
             teamManager?.RegisterAgent(this);
         }
     }

com.unity.ml-agents/Runtime/Communicator/GrpcExtensions.cs

                 MaxStepReached = ai.maxStepReached,
                 Done = ai.done,
                 Id = ai.episodeId,
+                TeamManagerId = ai.teamManagerId,
             };
 
             if (ai.discreteActionMasks != null)

com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/AgentInfo.cs

             "ChBtYXhfc3RlcF9yZWFjaGVkGAkgASgIEgoKAmlkGAogASgFEhMKC2FjdGlv",
             "bl9tYXNrGAsgAygIEjwKDG9ic2VydmF0aW9ucxgNIAMoCzImLmNvbW11bmlj",
             "YXRvcl9vYmplY3RzLk9ic2VydmF0aW9uUHJvdG8SFwoPdGVhbV9tYW5hZ2Vy",
-            "X2lkGA4gASgJSgQIARACSgQIAhADSgQIAxAESgQIBBAFSgQIBRAGSgQIBhAH",
+            "X2lkGA4gASgFSgQIARACSgQIAhADSgQIAxAESgQIBBAFSgQIBRAGSgQIBhAH",
             "SgQIDBANQiWqAiJVbml0eS5NTEFnZW50cy5Db21tdW5pY2F0b3JPYmplY3Rz",
             "YgZwcm90bzM="));
       descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData,
 
     /// <summary>Field number for the "team_manager_id" field.</summary>
     public const int TeamManagerIdFieldNumber = 14;
-    private string teamManagerId_ = "";
+    private int teamManagerId_;
-    public string TeamManagerId {
+    public int TeamManagerId {
-        teamManagerId_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+        teamManagerId_ = value;
       }
     }
 
       if (Id != 0) hash ^= Id.GetHashCode();
       hash ^= actionMask_.GetHashCode();
       hash ^= observations_.GetHashCode();
-      if (TeamManagerId.Length != 0) hash ^= TeamManagerId.GetHashCode();
+      if (TeamManagerId != 0) hash ^= TeamManagerId.GetHashCode();
       if (_unknownFields != null) {
         hash ^= _unknownFields.GetHashCode();
       }
       }
       actionMask_.WriteTo(output, _repeated_actionMask_codec);
       observations_.WriteTo(output, _repeated_observations_codec);
-      if (TeamManagerId.Length != 0) {
-        output.WriteRawTag(114);
-        output.WriteString(TeamManagerId);
+      if (TeamManagerId != 0) {
+        output.WriteRawTag(112);
+        output.WriteInt32(TeamManagerId);
       }
       if (_unknownFields != null) {
         _unknownFields.WriteTo(output);
       }
       size += actionMask_.CalculateSize(_repeated_actionMask_codec);
       size += observations_.CalculateSize(_repeated_observations_codec);
-      if (TeamManagerId.Length != 0) {
-        size += 1 + pb::CodedOutputStream.ComputeStringSize(TeamManagerId);
+      if (TeamManagerId != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeInt32Size(TeamManagerId);
       }
       if (_unknownFields != null) {
         size += _unknownFields.CalculateSize();
       }
       actionMask_.Add(other.actionMask_);
       observations_.Add(other.observations_);
-      if (other.TeamManagerId.Length != 0) {
+      if (other.TeamManagerId != 0) {
         TeamManagerId = other.TeamManagerId;
       }
       _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
             observations_.AddEntriesFrom(input, _repeated_observations_codec);
             break;
           }
-          case 114: {
-            TeamManagerId = input.ReadString();
+          case 112: {
+            TeamManagerId = input.ReadInt32();
             break;
           }
         }

ml-agents-envs/mlagents_envs/base_env.py

     reward: float
     agent_id: AgentId
     action_mask: Optional[List[np.ndarray]]
-    team_manager_id: Optional[str]
+    team_manager_id: int
 
 
 class DecisionSteps(Mapping):
      this simulation step.
     """
 
-    def __init__(self, obs, reward, agent_id, action_mask, team_manager_id=None):
+    def __init__(self, obs, reward, agent_id, action_mask, team_manager_id):
-        self.team_manager_id: Optional[List[str]] = team_manager_id
+        self.team_manager_id: np.ndarray = team_manager_id
         self._agent_id_to_index: Optional[Dict[AgentId, int]] = None
 
     @property
             agent_mask = []
             for mask in self.action_mask:
                 agent_mask.append(mask[agent_index])
-        team_manager_id = None
-        if self.team_manager_id is not None and self.team_manager_id != "":
-            team_manager_id = self.team_manager_id[agent_index]
+        team_manager_id = self.team_manager_id[agent_index]
         return DecisionStep(
             obs=agent_obs,
             reward=self.reward[agent_index],
             reward=np.zeros(0, dtype=np.float32),
             agent_id=np.zeros(0, dtype=np.int32),
             action_mask=None,
-            team_manager_id=None,
+            team_manager_id=np.zeros(0, dtype=np.int32),
         )
 
 
     reward: float
     interrupted: bool
     agent_id: AgentId
-    team_manager_id: Optional[str]
+    team_manager_id: int
 
 
 class TerminalSteps(Mapping):
      across simulation steps.
     """
 
-    def __init__(self, obs, reward, interrupted, agent_id, team_manager_id=None):
+    def __init__(self, obs, reward, interrupted, agent_id, team_manager_id):
+        self.team_manager_id: np.ndarray = team_manager_id
-        self.team_manager_id: Optional[List[str]] = team_manager_id
 
     @property
     def agent_id_to_index(self) -> Dict[AgentId, int]:
         agent_obs = []
         for batched_obs in self.obs:
             agent_obs.append(batched_obs[agent_index])
-        team_manager_id = None
-        if self.team_manager_id is not None and self.team_manager_id != "":
-            team_manager_id = self.team_manager_id[agent_index]
+        team_manager_id = self.team_manager_id[agent_index]
         return TerminalStep(
             obs=agent_obs,
             reward=self.reward[agent_index],
             reward=np.zeros(0, dtype=np.float32),
             interrupted=np.zeros(0, dtype=np.bool),
             agent_id=np.zeros(0, dtype=np.int32),
-            team_manager_id=None,
+            team_manager_id=np.zeros(0, dtype=np.int32),
         )
 
 

ml-agents-envs/mlagents_envs/communicator_objects/agent_info_pb2.py

   name='mlagents_envs/communicator_objects/agent_info.proto',
   package='communicator_objects',
   syntax='proto3',
-  serialized_pb=_b('\n3mlagents_envs/communicator_objects/agent_info.proto\x12\x14\x63ommunicator_objects\x1a\x34mlagents_envs/communicator_objects/observation.proto\"\xea\x01\n\x0e\x41gentInfoProto\x12\x0e\n\x06reward\x18\x07 \x01(\x02\x12\x0c\n\x04\x64one\x18\x08 \x01(\x08\x12\x18\n\x10max_step_reached\x18\t \x01(\x08\x12\n\n\x02id\x18\n \x01(\x05\x12\x13\n\x0b\x61\x63tion_mask\x18\x0b \x03(\x08\x12<\n\x0cobservations\x18\r \x03(\x0b\x32&.communicator_objects.ObservationProto\x12\x17\n\x0fteam_manager_id\x18\x0e \x01(\tJ\x04\x08\x01\x10\x02J\x04\x08\x02\x10\x03J\x04\x08\x03\x10\x04J\x04\x08\x04\x10\x05J\x04\x08\x05\x10\x06J\x04\x08\x06\x10\x07J\x04\x08\x0c\x10\rB%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+  serialized_pb=_b('\n3mlagents_envs/communicator_objects/agent_info.proto\x12\x14\x63ommunicator_objects\x1a\x34mlagents_envs/communicator_objects/observation.proto\"\xea\x01\n\x0e\x41gentInfoProto\x12\x0e\n\x06reward\x18\x07 \x01(\x02\x12\x0c\n\x04\x64one\x18\x08 \x01(\x08\x12\x18\n\x10max_step_reached\x18\t \x01(\x08\x12\n\n\x02id\x18\n \x01(\x05\x12\x13\n\x0b\x61\x63tion_mask\x18\x0b \x03(\x08\x12<\n\x0cobservations\x18\r \x03(\x0b\x32&.communicator_objects.ObservationProto\x12\x17\n\x0fteam_manager_id\x18\x0e \x01(\x05J\x04\x08\x01\x10\x02J\x04\x08\x02\x10\x03J\x04\x08\x03\x10\x04J\x04\x08\x04\x10\x05J\x04\x08\x05\x10\x06J\x04\x08\x06\x10\x07J\x04\x08\x0c\x10\rB%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
   ,
   dependencies=[mlagents__envs_dot_communicator__objects_dot_observation__pb2.DESCRIPTOR,])
 
       options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
       name='team_manager_id', full_name='communicator_objects.AgentInfoProto.team_manager_id', index=6,
-      number=14, type=9, cpp_type=9, label=1,
-      has_default_value=False, default_value=_b("").decode('utf-8'),
+      number=14, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
       options=None, file=DESCRIPTOR),

ml-agents-envs/mlagents_envs/communicator_objects/agent_info_pb2.pyi

 from typing import (
     Iterable as typing___Iterable,
     Optional as typing___Optional,
-    Text as typing___Text,
 )
 
 from typing_extensions import (
     max_step_reached = ... # type: builtin___bool
     id = ... # type: builtin___int
     action_mask = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___bool]
-    team_manager_id = ... # type: typing___Text
+    team_manager_id = ... # type: builtin___int
 
     @property
     def observations(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[mlagents_envs___communicator_objects___observation_pb2___ObservationProto]: ...
         id : typing___Optional[builtin___int] = None,
         action_mask : typing___Optional[typing___Iterable[builtin___bool]] = None,
         observations : typing___Optional[typing___Iterable[mlagents_envs___communicator_objects___observation_pb2___ObservationProto]] = None,
-        team_manager_id : typing___Optional[typing___Text] = None,
+        team_manager_id : typing___Optional[builtin___int] = None,
         ) -> None: ...
     @classmethod
     def FromString(cls, s: builtin___bytes) -> AgentInfoProto: ...

ml-agents-envs/mlagents_envs/rpc_utils.py

     decision_rewards = np.array(
         [agent_info.reward for agent_info in decision_agent_info_list], dtype=np.float32
     )
-    decision_team_manager = [
-        agent_info.team_manager_id
-        for agent_info in decision_agent_info_list
-        if agent_info.team_manager_id is not None
-    ]
-    if len(decision_team_manager) == 0:
-        decision_team_manager = None
-    terminal_team_manager = [
-        agent_info.team_manager_id
-        for agent_info in terminal_agent_info_list
-        if agent_info.team_manager_id is not None
+
+    decision_team_managers = [
+        agent_info.team_manager_id for agent_info in decision_agent_info_list
+    ]
+    terminal_team_managers = [
+        agent_info.team_manager_id for agent_info in terminal_agent_info_list
-    if len(terminal_team_manager) == 0:
-        terminal_team_manager = None
 
     _raise_on_nan_and_inf(decision_rewards, "rewards")
     _raise_on_nan_and_inf(terminal_rewards, "rewards")
             decision_rewards,
             decision_agent_id,
             action_mask,
-            decision_team_manager,
+            decision_team_managers,
         ),
         TerminalSteps(
             terminal_obs_list,
-            terminal_team_manager,
+            terminal_team_managers,
         ),
     )
 

protobuf-definitions/proto/mlagents_envs/communicator_objects/agent_info.proto

     repeated bool action_mask = 11;
     reserved 12; // deprecated CustomObservationProto custom_observation = 12;
     repeated ObservationProto observations = 13;
-    string team_manager_id = 14;
+    int32 team_manager_id = 14;
 }

com.unity.ml-agents/Runtime/Actuators/ITeamManager.cs

+using System.Collections.Generic;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents
+{
+    public interface ITeamManager
+    {
+        int GetId();
+
+        void RegisterAgent(Agent agent);
+    }
+}

com.unity.ml-agents/Runtime/Actuators/ITeamManager.cs.meta

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+guid: 8b061f82569af4ffba715297f77a95ab
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 

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

+using System.Threading;
+
+namespace Unity.MLAgents
+{
+    internal static class TeamManagerIdCounter
+    {
+        static int s_Counter;
+        public static int GetTeamManagerId()
+        {
+            return Interlocked.Increment(ref s_Counter); ;
+        }
+    }
+}

com.unity.ml-agents/Runtime/TeamManagerIdCounter.cs.meta

+fileFormatVersion: 2
+guid: 06456db1475d84371b35bae4855db3c6
+MonoImporter:
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+  userData: 
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+  assetBundleVariant: 

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

-using System.Collections.Generic;
-using Unity.MLAgents.Sensors;
-
-namespace Unity.MLAgents
-{
-    public interface ITeamManager
-    {
-        string GetId();
-
-        void RegisterAgent(Agent agent);
-        // TODO not sure this is all the info we need, maybe pass a class/struct instead.
-        void OnAgentDone(Agent agent, Agent.DoneReason doneReason, List<ISensor> sensors);
-    }
-}

com.unity.ml-agents/Runtime/ITeamManager.cs.meta

-fileFormatVersion: 2
-guid: 75810d91665e4477977eb78c9b15aeb3
-timeCreated: 1610057818