[refactor] Make classes except Optimizer framework agnostic (#4268)

ml-agents/mlagents/trainers/agent_processor.py

     EnvironmentStats,
 )
 from mlagents.trainers.trajectory import Trajectory, AgentExperience
-from mlagents.trainers.policy.tf_policy import TFPolicy
 from mlagents.trainers.policy import Policy
 from mlagents.trainers.action_info import ActionInfo, ActionInfoOutputs
 from mlagents.trainers.stats import StatsReporter
 
     def __init__(
         self,
-        policy: TFPolicy,
+        policy: Policy,
         behavior_id: str,
         stats_reporter: StatsReporter,
         max_trajectory_length: int = sys.maxsize,
 
     def __init__(
         self,
-        policy: TFPolicy,
+        policy: Policy,
         behavior_id: str,
         stats_reporter: StatsReporter,
         max_trajectory_length: int = sys.maxsize,

ml-agents/mlagents/trainers/env_manager.py

 )
 from mlagents_envs.side_channel.stats_side_channel import EnvironmentStats
 
-from mlagents.trainers.policy.tf_policy import TFPolicy
+from mlagents.trainers.policy import Policy
 from mlagents.trainers.agent_processor import AgentManager, AgentManagerQueue
 from mlagents.trainers.action_info import ActionInfo
 from mlagents_envs.logging_util import get_logger
 
 class EnvManager(ABC):
     def __init__(self):
-        self.policies: Dict[BehaviorName, TFPolicy] = {}
+        self.policies: Dict[BehaviorName, Policy] = {}
-    def set_policy(self, brain_name: BehaviorName, policy: TFPolicy) -> None:
+    def set_policy(self, brain_name: BehaviorName, policy: Policy) -> None:
         self.policies[brain_name] = policy
         if brain_name in self.agent_managers:
             self.agent_managers[brain_name].policy = policy

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

 # ## ML-Agent Learning (Ghost Trainer)
 
 from collections import defaultdict
-from typing import Deque, Dict, DefaultDict, List, cast
+from typing import Deque, Dict, DefaultDict, List
 
 import numpy as np
 
-from mlagents.trainers.policy.tf_policy import TFPolicy
 
 from mlagents.trainers.trainer import Trainer
 from mlagents.trainers.trajectory import Trajectory
         for brain_name in self._internal_policy_queues:
             internal_policy_queue = self._internal_policy_queues[brain_name]
             try:
-                policy = cast(TFPolicy, internal_policy_queue.get_nowait())
+                policy = internal_policy_queue.get_nowait()
                 self.current_policy_snapshot[brain_name] = policy.get_weights()
             except AgentManagerQueue.Empty:
                 pass
 
     def create_policy(
         self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec
-    ) -> TFPolicy:
+    ) -> Policy:
         """
         Creates policy with the wrapped trainer's create_policy function
         The first policy encountered sets the wrapped
         return policy
 
     def add_policy(
-        self, parsed_behavior_id: BehaviorIdentifiers, policy: TFPolicy
+        self, parsed_behavior_id: BehaviorIdentifiers, policy: Policy
     ) -> None:
         """
         Adds policy to GhostTrainer.
         self._name_to_parsed_behavior_id[name_behavior_id] = parsed_behavior_id
         self.policies[name_behavior_id] = policy
 
-    def get_policy(self, name_behavior_id: str) -> TFPolicy:
+    def get_policy(self, name_behavior_id: str) -> Policy:
         """
         Gets policy associated with name_behavior_id
         :param name_behavior_id: Fully qualified behavior name

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

     Provides methods to update the Policy.
     """
 
+    def __init__(self):
+        self.reward_signals = {}
+
     @abc.abstractmethod
     def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
         """

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

 
 class TFOptimizer(Optimizer):  # pylint: disable=W0223
     def __init__(self, policy: TFPolicy, trainer_params: TrainerSettings):
+        super().__init__()
         self.sess = policy.sess
         self.policy = policy
         self.update_dict: Dict[str, tf.Tensor] = {}
         Create reward signals
         :param reward_signal_configs: Reward signal config.
         """
-        self.reward_signals = {}
         # Create reward signals
         for reward_signal, settings in reward_signal_configs.items():
             # Name reward signals by string in case we have duplicates later

ml-agents/mlagents/trainers/policy/policy.py

     @abstractmethod
     def save(self, output_filepath: str, settings: SerializationSettings) -> None:
         pass
+
+    @abstractmethod
+    def load_weights(self, values: List[np.ndarray]) -> None:
+        pass
+
+    @abstractmethod
+    def get_weights(self) -> List[np.ndarray]:
+        return []
+
+    @abstractmethod
+    def init_load_weights(self) -> None:
+        pass

ml-agents/mlagents/trainers/policy/tf_policy.py

             feed_dict[self.action_masks] = mask
         return feed_dict
 
-    def make_empty_memory(self, num_agents):
-        """
-        Creates empty memory for use with RNNs
-        :param num_agents: Number of agents.
-        :return: Numpy array of zeros.
-        """
-        return np.zeros((num_agents, self.m_size), dtype=np.float32)
-
-    def save_memories(
-        self, agent_ids: List[str], memory_matrix: Optional[np.ndarray]
-    ) -> None:
-        if memory_matrix is None:
-            return
-        for index, agent_id in enumerate(agent_ids):
-            self.memory_dict[agent_id] = memory_matrix[index, :]
-
-    def retrieve_memories(self, agent_ids: List[str]) -> np.ndarray:
-        memory_matrix = np.zeros((len(agent_ids), self.m_size), dtype=np.float32)
-        for index, agent_id in enumerate(agent_ids):
-            if agent_id in self.memory_dict:
-                memory_matrix[index, :] = self.memory_dict[agent_id]
-        return memory_matrix
-
-    def remove_memories(self, agent_ids):
-        for agent_id in agent_ids:
-            if agent_id in self.memory_dict:
-                self.memory_dict.pop(agent_id)
-
-    def make_empty_previous_action(self, num_agents):
-        """
-        Creates empty previous action for use with RNNs and discrete control
-        :param num_agents: Number of agents.
-        :return: Numpy array of zeros.
-        """
-        return np.zeros((num_agents, self.num_branches), dtype=np.int)
-
-    def save_previous_action(
-        self, agent_ids: List[str], action_matrix: Optional[np.ndarray]
-    ) -> None:
-        if action_matrix is None:
-            return
-        for index, agent_id in enumerate(agent_ids):
-            self.previous_action_dict[agent_id] = action_matrix[index, :]
-
-    def retrieve_previous_action(self, agent_ids: List[str]) -> np.ndarray:
-        action_matrix = np.zeros((len(agent_ids), self.num_branches), dtype=np.int)
-        for index, agent_id in enumerate(agent_ids):
-            if agent_id in self.previous_action_dict:
-                action_matrix[index, :] = self.previous_action_dict[agent_id]
-        return action_matrix
-
-    def remove_previous_action(self, agent_ids):
-        for agent_id in agent_ids:
-            if agent_id in self.previous_action_dict:
-                self.previous_action_dict.pop(agent_id)
-
     def get_current_step(self):
         """
         Gets current model step.

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

 from mlagents_envs.logging_util import get_logger
 from mlagents_envs.base_env import BehaviorSpec
 from mlagents.trainers.trainer.rl_trainer import RLTrainer
+from mlagents.trainers.policy import Policy
 from mlagents.trainers.policy.tf_policy import TFPolicy
 from mlagents.trainers.ppo.optimizer import PPOOptimizer
 from mlagents.trainers.trajectory import Trajectory
         )
         self.load = load
         self.seed = seed
-        self.policy: TFPolicy = None  # type: ignore
+        self.policy: Policy = None  # type: ignore
 
     def _process_trajectory(self, trajectory: Trajectory) -> None:
         """
         return policy
 
     def add_policy(
-        self, parsed_behavior_id: BehaviorIdentifiers, policy: TFPolicy
+        self, parsed_behavior_id: BehaviorIdentifiers, policy: Policy
     ) -> None:
         """
         Adds policy to trainer.
             )
         self.policy = policy
         self.policies[parsed_behavior_id.behavior_id] = policy
-        self.optimizer = PPOOptimizer(self.policy, self.trainer_settings)
+        self.optimizer = PPOOptimizer(
+            cast(TFPolicy, self.policy), self.trainer_settings
+        )
-    def get_policy(self, name_behavior_id: str) -> TFPolicy:
+    def get_policy(self, name_behavior_id: str) -> Policy:
         """
         Gets policy from trainer associated with name_behavior_id
         :param name_behavior_id: full identifier of policy

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

 from mlagents_envs.timers import timed
 from mlagents_envs.base_env import BehaviorSpec
 from mlagents.trainers.policy.tf_policy import TFPolicy
+from mlagents.trainers.policy import Policy
 from mlagents.trainers.sac.optimizer import SACOptimizer
 from mlagents.trainers.trainer.rl_trainer import RLTrainer
 from mlagents.trainers.trajectory import Trajectory, SplitObservations
 
         self.load = load
         self.seed = seed
-        self.policy: TFPolicy = None  # type: ignore
+        self.policy: Policy = None  # type: ignore
         self.optimizer: SACOptimizer = None  # type: ignore
         self.hyperparameters: SACSettings = cast(
             SACSettings, trainer_settings.hyperparameters
                 self._stats_reporter.add_stat(stat, np.mean(stat_list))
 
     def add_policy(
-        self, parsed_behavior_id: BehaviorIdentifiers, policy: TFPolicy
+        self, parsed_behavior_id: BehaviorIdentifiers, policy: Policy
     ) -> None:
         """
         Adds policy to trainer.
             )
         self.policy = policy
         self.policies[parsed_behavior_id.behavior_id] = policy
-        self.optimizer = SACOptimizer(self.policy, self.trainer_settings)
+        self.optimizer = SACOptimizer(
+            cast(TFPolicy, 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
             max(1, self.step / self.reward_signal_steps_per_update)
         )
 
-    def get_policy(self, name_behavior_id: str) -> TFPolicy:
+    def get_policy(self, name_behavior_id: str) -> Policy:
         """
         Gets policy from trainer associated with name_behavior_id
         :param name_behavior_id: full identifier of policy

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

 )
 from mlagents_envs.logging_util import get_logger
 from mlagents_envs.timers import timed
-from mlagents.trainers.optimizer.tf_optimizer import TFOptimizer
+from mlagents.trainers.optimizer import Optimizer
 from mlagents.trainers.buffer import AgentBuffer
 from mlagents.trainers.trainer import Trainer
 from mlagents.trainers.components.reward_signals import RewardSignalResult
             for agent_id in rewards:
                 rewards[agent_id] = 0
 
-    def _update_end_episode_stats(self, agent_id: str, optimizer: TFOptimizer) -> None:
+    def _update_end_episode_stats(self, agent_id: str, optimizer: Optimizer) -> None:
         for name, rewards in self.collected_rewards.items():
             if name == "environment":
                 self.stats_reporter.add_stat(

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

 
 from mlagents_envs.logging_util import get_logger
 from mlagents_envs.base_env import BehaviorSpec
-from mlagents.trainers.policy.tf_policy import TFPolicy
 from mlagents.trainers.stats import StatsReporter
 from mlagents.trainers.trajectory import Trajectory
 from mlagents.trainers.agent_processor import AgentManagerQueue
         self.step: int = 0
         self.artifact_path = artifact_path
         self.summary_freq = self.trainer_settings.summary_freq
-        self.policies: Dict[str, TFPolicy] = {}
+        self.policies: Dict[str, Policy] = {}
 
     @property
     def stats_reporter(self):
     @abc.abstractmethod
     def create_policy(
         self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec
-    ) -> TFPolicy:
+    ) -> Policy:
         """
         Creates policy
         """
     def add_policy(
-        self, parsed_behavior_id: BehaviorIdentifiers, policy: TFPolicy
+        self, parsed_behavior_id: BehaviorIdentifiers, policy: Policy
     ) -> None:
         """
         Adds policy to trainer.
     @abc.abstractmethod
-    def get_policy(self, name_behavior_id: str) -> TFPolicy:
+    def get_policy(self, name_behavior_id: str) -> Policy:
         """
         Gets policy from trainer.
         """