[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

neodroidagent/agents/numpy_agents/evolutionary/cma_agent_tf.py

@@ -1,4 +1,5 @@
 """Covariance Matrix Adaptation Evolution Strategy."""
+
 from typing import Sequence
 
 import numpy

neodroidagent/agents/numpy_agents/evolutionary/get_rid/local_runner.py

@@ -1,4 +1,5 @@
 """Provides algorithms with access to most of garage's features."""
+
 import copy
 import os
 import time

neodroidagent/agents/numpy_agents/model_based/dyna_agent.py

@@ -363,7 +363,7 @@ def _simulate_behavior(self):
             pairs = env_model.state_action_pairs_leading_to_outcome(s_sim)
 
             # add predecessors to queue if their priority exceeds thresh
-            for (_s, _a) in pairs:
+            for _s, _a in pairs:
                 self._update_queue(_s, _a)
 
     def _update(self, s, a):

neodroidagent/common/memory/transitions/transitions.py

@@ -88,7 +88,8 @@ def non_terminal_numerical(self):
 @dataclass
 class ValuedTransitionPoint(TransitionPoint):
     """
-    __slots__=['state','action','successor_state','signal','terminal',"distribution","value_estimate"]"""
+    __slots__=['state','action','successor_state','signal','terminal',"distribution","value_estimate"]
+    """
 
     __slots__ = TransitionPoint.__slots__ + ["distribution", "value_estimate"]
     state: Any

neodroidagent/entry_points/session_factory.py

@@ -37,7 +37,8 @@ def session_factory(
 ):
     r"""
     Entry point start a starting a training session with the functionality of parsing cmdline arguments and
-    confirming configuration to use before training and overwriting of default training configurations"""
+    confirming configuration to use before training and overwriting of default training configurations
+    """
 
     if config is None:
         config = {}

neodroidagent/utilities/exploration/intrinsic_signals/torch_isp/curiosity/icm.py

@@ -127,7 +127,8 @@ def __init__(
         :param signal_space: used for scaling the intrinsic reward returned by this module. Can be used to control how
         the fluctuation scale of the intrinsic signal
         :param weight: balances the importance between forward and inverse model
-        :param intrinsic_signal_factor: balances the importance between extrinsic and intrinsic signal."""
+        :param intrinsic_signal_factor: balances the importance between extrinsic and intrinsic signal.
+        """
 
         assert (
             len(observation_space.shape) == 1

samples/curriculum/experimental/difficulty.py

@@ -66,7 +66,9 @@ def color_sample(identifier):
         neodroid.Configuration(
             identifier + "ColorConfigurableG", numpy.random.sample()
         ),
-        neodroid.Configuration(identifier + "ColorConfigurableB", numpy.random.sample())
+        neodroid.Configuration(
+            identifier + "ColorConfigurableB", numpy.random.sample()
+        ),
         # neodroid.Configuration(id+'ColorConfigurableA',
         #                       numpy.random.sample()),
     ]