Remove remaining files

ml-agents-envs/mlagents_envs/base_env.py

         self.reward: np.ndarray = reward
         self.team_reward: np.ndarray = team_reward
         self.agent_id: np.ndarray = agent_id
-        self.team_manager_id: Optional[List[str]] = team_manager_id
         self.action_mask: Optional[List[np.ndarray]] = action_mask
         self.team_manager_id: np.ndarray = team_manager_id
         self._agent_id_to_index: Optional[Dict[AgentId, int]] = None
         self.agent_id: np.ndarray = agent_id
         self.team_manager_id: np.ndarray = team_manager_id
         self._agent_id_to_index: Optional[Dict[AgentId, int]] = None
-        self.team_manager_id: Optional[List[str]] = team_manager_id
 
     @property
     def agent_id_to_index(self) -> Dict[AgentId, int]:

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 (

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
-    ]
-    if len(terminal_team_manager) == 0:
-        terminal_team_manager = None
 
     decision_team_managers = [
         agent_info.team_manager_id for agent_info in decision_agent_info_list

Project/Assets/ML-Agents/Examples/PushBlock/TFModels/PushBlockSuperNoEgo.onnx.meta

-fileFormatVersion: 2
-guid: b3b681e0364fd4b3e89015a1b23ac521
-ScriptedImporter:
-  fileIDToRecycleName:
-    11400000: main obj
-    11400002: model data
-  externalObjects: {}
-  userData: 
-  assetBundleName: 
-  assetBundleVariant: 
-  script: {fileID: 11500000, guid: 683b6cb6d0a474744822c888b46772c9, type: 3}
-  optimizeModel: 1
-  forceArbitraryBatchSize: 1
-  treatErrorsAsWarnings: 0

config/ppo/WallJumpCollab.yaml

-behaviors:
-  SmallWallJump:
-    trainer_type: ppo
-    hyperparameters:
-      batch_size: 128
-      buffer_size: 2048
-      learning_rate: 0.0003
-      beta: 0.005
-      epsilon: 0.2
-      lambd: 0.95
-      num_epoch: 3
-      learning_rate_schedule: linear
-    network_settings:
-      normalize: false
-      hidden_units: 256
-      num_layers: 2
-      vis_encode_type: simple
-    reward_signals:
-      extrinsic:
-        gamma: 0.99
-        strength: 1.0
-    keep_checkpoints: 5
-    max_steps: 15000000
-    time_horizon: 128
-    summary_freq: 20000
-    threaded: true
-environment_parameters:
-  big_wall_height:
-    curriculum:
-      - name: Lesson0
-        completion_criteria:
-          measure: progress
-          behavior: SmallWallJump
-          signal_smoothing: true
-          min_lesson_length: 100
-          threshold: 0.1
-        value: 3
-      - name: Lesson1
-        completion_criteria:
-          measure: progress
-          behavior: SmallWallJump
-          signal_smoothing: true
-          min_lesson_length: 100
-          threshold: 0.3
-        value: 5
-      - name: Lesson2
-        completion_criteria:
-          measure: progress
-          behavior: SmallWallJump
-          signal_smoothing: true
-          min_lesson_length: 100
-          threshold: 0.5
-        value: 8
-      - name: Lesson3
-        completion_criteria:
-          measure: progress
-          behavior: SmallWallJump
-          signal_smoothing: true
-          min_lesson_length: 100
-          threshold: 0.7
-        value: 9
-      - name: Lesson4
-        value: 9

config/ppo/Zombie.yaml

-behaviors:
-  PushBlock:
-    trainer_type: ppo
-    hyperparameters:
-      batch_size: 1024
-      buffer_size: 10240
-      learning_rate: 0.0003
-      beta: 0.01
-      epsilon: 0.2
-      lambd: 0.95
-      num_epoch: 3
-      learning_rate_schedule: constant
-    network_settings:
-      normalize: false
-      hidden_units: 256
-      num_layers: 2
-      vis_encode_type: simple
-    reward_signals:
-      extrinsic:
-        gamma: 0.99
-        strength: 1.0
-    keep_checkpoints: 5
-    max_steps: 20000000 #2000000
-    time_horizon: 64
-    summary_freq: 10000
-    threaded: true
-env_settings:
-  num_envs: 3