removed extraneous logging imports and loggers

ml-agents-envs/mlagents_envs/communicator.py

-import logging
-
-
-logger = logging.getLogger("mlagents_envs")
 
 
 class Communicator(object):

ml-agents-envs/mlagents_envs/exception.py

-import logging
-
-logger = logging.getLogger("mlagents_envs")
-
-
 class UnityException(Exception):
     """
     Any error related to ml-agents environment.

ml-agents-envs/mlagents_envs/rpc_communicator.py

-import logging
 import grpc
 from typing import Optional
 
 from mlagents_envs.communicator_objects.unity_input_pb2 import UnityInputProto
 from mlagents_envs.communicator_objects.unity_output_pb2 import UnityOutputProto
 from .exception import UnityTimeOutException, UnityWorkerInUseException
-
-logger = logging.getLogger("mlagents_envs")
 
 
 class UnityToExternalServicerImplementation(UnityToExternalProtoServicer):

ml-agents-envs/mlagents_envs/rpc_utils.py

     NONE as COMPRESSION_NONE,
 )
 from mlagents_envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
-import logging
-
-logger = logging.getLogger("mlagents_envs")
 
 
 def agent_group_spec_from_proto(

ml-agents/mlagents/logging_util.py

 
 def create_logger(name):
     log_level = logging.INFO
+    date_format = "%Y-%m-%d %H:%M:%S"
-    logging.basicConfig(level=log_level, format=log_format, datefmt="%Y-%m-%d %H:%M:%S")
+
+    logging.basicConfig(level=log_level, format=log_format, datefmt=date_format)
-
-
-# TODO
-"""
-1. change mlagents.trainers to mlagents_trainers
-2. logger should be in lowercase in all files
-3. if logger is not used in a file then it should be deleted
-"""

ml-agents/mlagents/trainers/brain.py

-import logging
-
-
-logger = logging.getLogger("mlagents.trainers")
 
 
 class CameraResolution(NamedTuple):

ml-agents/mlagents/trainers/components/reward_signals/gail/signal.py

 from typing import Any, Dict, List
-import logging
 import numpy as np
 from mlagents.tf_utils import tf
 
 from mlagents.trainers.demo_loader import demo_to_buffer
-
-LOGGER = logging.getLogger("mlagents.trainers")
 
 
 class GAILRewardSignal(RewardSignal):

ml-agents/mlagents/trainers/components/reward_signals/reward_signal_factory.py

-import logging
-
 from mlagents.trainers.exception import UnityTrainerException
 from mlagents.trainers.components.reward_signals import RewardSignal
 from mlagents.trainers.components.reward_signals.extrinsic.signal import (
     CuriosityRewardSignal,
 )
 from mlagents.trainers.policy.tf_policy import TFPolicy
-
-logger = logging.getLogger("mlagents.trainers")
 
 
 NAME_TO_CLASS: Dict[str, Type[RewardSignal]] = {

ml-agents/mlagents/trainers/demo_loader.py

-import logging
 import os
 from typing import List, Tuple
 import numpy as np
 )
 from mlagents_envs.timers import timed, hierarchical_timer
 from google.protobuf.internal.decoder import _DecodeVarint32  # type: ignore
-
-
-logger = logging.getLogger("mlagents.trainers")
 
 
 @timed

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

 # # Unity ML-Agents Toolkit
 # ## ML-Agent Learning (Ghost Trainer)
 
-# import logging
 from typing import Deque, Dict, List, Any, cast
 
 import numpy as np
 from mlagents.trainers.trajectory import Trajectory
 from mlagents.trainers.agent_processor import AgentManagerQueue
 
-LOGGER = logging.getLogger("mlagents.trainers")
+logger = logging.getLogger("mlagents.trainers")
 
 
 class GhostTrainer(Trainer):
         Saves training statistics to Tensorboard.
         """
         opponents = np.array(self.policy_elos, dtype=np.float32)
-        LOGGER.info(
+        logger.info(
             " Learning brain {} ELO: {:0.3f}\n"
             "Mean Opponent ELO: {:0.3f}"
             " Std Opponent ELO: {:0.3f}".format(
                 x = "current"
                 self.policy_elos[-1] = self.current_elo
             self.current_opponent = -1 if x == "current" else x
-            LOGGER.debug(
+            logger.debug(
                 "Step {}: Swapping snapshot {} to id {} with {} learning".format(
                     self.get_step, x, name_behavior_id, self.learning_behavior_name
                 )

ml-agents/mlagents/trainers/models.py

-import logging
 from enum import Enum
 from typing import Callable, Dict, List, Tuple, NamedTuple
 
 from mlagents.trainers.exception import UnityTrainerException
 from mlagents.trainers.brain import CameraResolution
-
-logger = logging.getLogger("mlagents.trainers")
 
 ActivationFunction = Callable[[tf.Tensor], tf.Tensor]
 EncoderFunction = Callable[

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

-import logging
-
-
 from mlagents_envs.timers import timed
 from mlagents_envs.base_env import BatchedStepResult
 from mlagents.trainers.brain import BrainParameters
     GaussianDistribution,
     MultiCategoricalDistribution,
 )
-
-logger = logging.getLogger("mlagents.trainers")
 
 EPSILON = 1e-6  # Small value to avoid divide by zero
 

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

 import logging
 from typing import Any, Dict, List, Optional
-
-
-
 from mlagents_envs.exception import UnityException
 from mlagents.trainers.policy import Policy
 from mlagents.trainers.action_info import ActionInfo

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

-import logging
-
 import numpy as np
 from mlagents.tf_utils import tf
 from mlagents_envs.timers import timed
 from mlagents.trainers.buffer import AgentBuffer
-
-
-logger = logging.getLogger("mlagents.trainers")
 
 
 class PPOOptimizer(TFOptimizer):

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

-import logging
-
-
 from mlagents.trainers.models import ModelUtils, EncoderType
 
 LOG_STD_MAX = 2
 CONTINUOUS_TARGET_ENTROPY_SCALE = 1.0  # TODO: Make these an optional hyperparam.
-
-LOGGER = logging.getLogger("mlagents.trainers")
-
 POLICY_SCOPE = ""
 TARGET_SCOPE = "target_network"
 

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

 
 EPSILON = 1e-6  # Small value to avoid divide by zero
 
-LOGGER = logging.getLogger("mlagents.trainers")
+logger = logging.getLogger("mlagents.trainers")
 
 POLICY_SCOPE = ""
 TARGET_SCOPE = "target_network"
                 self.target_network.value_vars, self.policy_network.value_vars
             )
         ]
-        LOGGER.debug("value_vars")
+        logger.debug("value_vars")
-        LOGGER.debug("targvalue_vars")
+        logger.debug("targvalue_vars")
-        LOGGER.debug("critic_vars")
+        logger.debug("critic_vars")
-        LOGGER.debug("q_vars")
+        logger.debug("q_vars")
-        LOGGER.debug("policy_vars")
+        logger.debug("policy_vars")
         policy_vars = self.policy.get_trainable_variables()
         self.print_all_vars(policy_vars)
 
 
     def print_all_vars(self, variables):
         for _var in variables:
-            LOGGER.debug(_var)
+            logger.debug(_var)
 
     @timed
     def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:

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

 # # Unity ML-Agents Toolkit
-import logging
 from typing import Dict
 from collections import defaultdict
 
 from mlagents.trainers.exception import UnityTrainerException
 from mlagents.trainers.components.reward_signals import RewardSignalResult
-
-LOGGER = logging.getLogger("mlagents.trainers")
 
 RewardSignalResults = Dict[str, RewardSignalResult]
 

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

 from mlagents.trainers.exception import UnityTrainerException
 from mlagents_envs.timers import hierarchical_timer
 
-LOGGER = logging.getLogger("mlagents.trainers")
+logger = logging.getLogger("mlagents.trainers")
 
 
 class Trainer(abc.ABC):
                 s = sess.run(s_op)
                 self.stats_reporter.write_text(s, self.get_step)
         except Exception:
-            LOGGER.info("Could not write text summary for Tensorboard.")
+            logger.info("Could not write text summary for Tensorboard.")
             pass
 
     def _dict_to_str(self, param_dict: Dict[str, Any], num_tabs: int) -> str:
             "Environment/Cumulative Reward"
         )
         if stats_summary.num > 0:
-            LOGGER.info(
+            logger.info(
                 "{}: {}: Step: {}. "
                 "Time Elapsed: {:0.3f} s "
                 "Mean "
             )
             set_gauge(f"{self.brain_name}.mean_reward", stats_summary.mean)
         else:
-            LOGGER.info(
+            logger.info(
                 " {}: {}: Step: {}. No episode was completed since last summary. {}".format(
                     self.run_id, self.brain_name, step, is_training
                 )