Added the algorithm named ppo_transfer

ml-agents/mlagents/trainers/ppo_transfer/optimizer.py

 from mlagents.trainers.settings import TrainerSettings, PPOSettings
 
 
-class PPOOptimizer(TFOptimizer):
+class PPOTransferOptimizer(TFOptimizer):
-        The PPO optimizer has a value estimator and a loss function.
+        The PPO optimizer has a value esåtimator and a loss function.
         :param policy: A TFPolicy object that will be updated by this PPO Optimizer.
         :param trainer_params: Trainer parameters dictionary that specifies the properties of the trainer.
         """

ml-agents/mlagents/trainers/ppo_transfer/trainer.py

 from mlagents.trainers.trainer.rl_trainer import RLTrainer
 from mlagents.trainers.brain import BrainParameters
 from mlagents.trainers.policy.tf_policy import TFPolicy
-from mlagents.trainers.ppo.optimizer import PPOOptimizer
+from mlagents.trainers.ppo_transfer.optimizer import PPOTransferOptimizer
 from mlagents.trainers.trajectory import Trajectory
 from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
 from mlagents.trainers.settings import TrainerSettings, PPOSettings
 
 
-class PPOTrainer(RLTrainer):
+class PPOTransferTrainer(RLTrainer):
     """The PPOTrainer is an implementation of the PPO algorithm."""
 
     def __init__(
         :param seed: The seed the model will be initialized with
         :param artifact_path: The directory within which to store artifacts from this trainer.
         """
-        super(PPOTrainer, self).__init__(
+        super(PPOTransferTrainer, self).__init__(
             brain_name, trainer_settings, training, artifact_path, reward_buff_cap
         )
         self.hyperparameters: PPOSettings = cast(
         self.seed = seed
         self.policy: NNPolicy = None  # type: ignore
+        print("The current algorithm is PPO Transfer")
 
     def _process_trajectory(self, trajectory: Trajectory) -> None:
         """
         if not isinstance(policy, NNPolicy):
             raise RuntimeError("Non-NNPolicy passed to PPOTrainer.add_policy()")
         self.policy = policy
-        self.optimizer = PPOOptimizer(self.policy, self.trainer_settings)
+        self.optimizer = PPOTransferOptimizer(self.policy, self.trainer_settings)
         for _reward_signal in self.optimizer.reward_signals.keys():
             self.collected_rewards[_reward_signal] = defaultdict(lambda: 0)
         # Needed to resume loads properly

ml-agents/mlagents/trainers/settings.py

 class TrainerType(Enum):
     PPO: str = "ppo"
     SAC: str = "sac"
+    PPO_Transfer: str = "ppo_transfer"
-        _mapping = {TrainerType.PPO: PPOSettings, TrainerType.SAC: SACSettings}
+        _mapping = {TrainerType.PPO: PPOSettings, TrainerType.SAC: SACSettings, 
+        TrainerType.PPO_Transfer: PPOSettings}
         return _mapping[self]
 
 

ml-agents/mlagents/trainers/trainer_util.py

 from mlagents.trainers.trainer import Trainer
 from mlagents.trainers.exception import UnityTrainerException
 from mlagents.trainers.ppo.trainer import PPOTrainer
+from mlagents.trainers.ppo_transfer.trainer import PPOTransferTrainer
 from mlagents.trainers.sac.trainer import SACTrainer
 from mlagents.trainers.ghost.trainer import GhostTrainer
 from mlagents.trainers.ghost.controller import GhostController
         )
     elif trainer_type == TrainerType.SAC:
         trainer = SACTrainer(
+            brain_name,
+            min_lesson_length,
+            trainer_settings,
+            train_model,
+            load_model,
+            seed,
+            trainer_artifact_path,
+        )
+    elif trainer_type == TrainerType.PPO_Transfer:
+        trainer = PPOTransferTrainer(
             brain_name,
             min_lesson_length,
             trainer_settings,

config/ppo_transfer/3DBall.yaml

+behaviors:
+  3DBall:
+    trainer_type: ppo_transfer
+    hyperparameters:
+      batch_size: 64
+      buffer_size: 12000
+      learning_rate: 0.0003
+      beta: 0.001
+      epsilon: 0.2
+      lambd: 0.99
+      num_epoch: 3
+      learning_rate_schedule: linear
+    network_settings:
+      normalize: true
+      hidden_units: 128
+      num_layers: 2
+      vis_encode_type: simple
+    reward_signals:
+      extrinsic:
+        gamma: 0.99
+        strength: 1.0
+    keep_checkpoints: 5
+    max_steps: 500000
+    time_horizon: 1000
+    summary_freq: 12000
+    threaded: true