Fix mypy errors in trainer code. (#3135)

.pre-commit-config.yaml

             )$
 
 -   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v0.750
-    # Currently mypy may assert after logging one message. To get all the messages at once, change repo and rev to
-    #   repo: https://github.com/chriselion/mypy
-    #   rev: 3d0b6164a9487a6c5cf9d144110b86600fd85e25
-    # This is a fork with the assert disabled, although precommit has trouble installing it sometimes.
+    rev: v0.761
     hooks:
     -   id: mypy
         name: mypy-ml-agents

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

         self.load = load
         self.multi_gpu = multi_gpu
         self.seed = seed
-        self.policy: TFPolicy = None
+        self.policy: PPOPolicy = None  # type: ignore
 
     def process_trajectory(self, trajectory: Trajectory) -> None:
         """
                     self.__class__.__name__
                 )
             )
+        if not isinstance(policy, PPOPolicy):
+            raise RuntimeError("Non-PPOPolicy passed to PPOTrainer.add_policy()")
         self.policy = policy
 
     def get_policy(self, name_behavior_id: str) -> TFPolicy:

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

 import logging
-from typing import Dict, Any, Optional
+from typing import Dict, Any, Optional, Mapping
 import numpy as np
 from mlagents.tf_utils import tf
 
         return update_stats
 
     def update_reward_signals(
-        self, reward_signal_minibatches: Dict[str, Dict], num_sequences: int
+        self, reward_signal_minibatches: Mapping[str, Dict], num_sequences: int
     ) -> Dict[str, float]:
         """
         Only update the reward signals.
         feed_dict: Dict[tf.Tensor, Any],
         update_dict: Dict[str, tf.Tensor],
         stats_needed: Dict[str, str],
-        reward_signal_minibatches: Dict[str, Dict],
+        reward_signal_minibatches: Mapping[str, Dict],
         num_sequences: int,
     ) -> None:
         """

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

         self.check_param_keys()
         self.load = load
         self.seed = seed
-        self.policy: TFPolicy = None
+        self.policy: SACPolicy = None  # type: ignore
 
         self.step = 0
         self.train_interval = (
                     self.__class__.__name__
                 )
             )
+        if not isinstance(policy, SACPolicy):
+            raise RuntimeError("Non-SACPolicy passed to SACTrainer.add_policy()")
         self.policy = policy
 
     def get_policy(self, name_behavior_id: str) -> TFPolicy: