remove unused imports test_hybrid

ml-agents/mlagents/trainers/tests/torch/test_hybrid.py

 from mlagents.trainers.tests.simple_test_envs import (
     SimpleEnvironment,
     MemoryEnvironment,
-    RecordEnvironment,
-from mlagents.trainers.demo_loader import write_demo
-
-from mlagents.trainers.settings import (
-    NetworkSettings,
-    SelfPlaySettings,
-    BehavioralCloningSettings,
-    GAILSettings,
-    RewardSignalType,
-    EncoderType,
-    FrameworkType,
-)
-
-from mlagents_envs.communicator_objects.demonstration_meta_pb2 import (
-    DemonstrationMetaProto,
-)
-from mlagents_envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
-from mlagents_envs.communicator_objects.space_type_pb2 import discrete, continuous
+from mlagents.trainers.settings import NetworkSettings, FrameworkType
-from mlagents.trainers.tests.check_env_trains import (
-    check_environment_trains,
-    default_reward_processor,
-)
+from mlagents.trainers.tests.check_env_trains import check_environment_trains
 
 BRAIN_NAME = "1D"
 
 
 def test_hybrid_ppo():
-    env = SimpleEnvironment(
-        [BRAIN_NAME], continuous_action_size=1, discrete_action_size=1
-    )
-    config = attr.evolve(PPO_TORCH_CONFIG)
-    check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=1.0)
-
-
-def test_conthybrid_ppo():
-    env = SimpleEnvironment(
-        [BRAIN_NAME], continuous_action_size=1, discrete_action_size=0
-    )
-    config = attr.evolve(PPO_TORCH_CONFIG)
-    check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=1.0)
-
-
-def test_dischybrid_ppo():
-    env = SimpleEnvironment(
-        [BRAIN_NAME], continuous_action_size=0, discrete_action_size=1
-    )
+    env = SimpleEnvironment([BRAIN_NAME], action_sizes=(1, 1))
     config = attr.evolve(PPO_TORCH_CONFIG)
     check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=1.0)
 
     env = SimpleEnvironment(
-        [BRAIN_NAME],
-        num_visual=num_visual,
-        num_vector=0,
-        continuous_action_size=1,
-        discrete_action_size=1,
+        [BRAIN_NAME], num_visual=num_visual, num_vector=0, action_sizes=(1, 1)
     )
     new_hyperparams = attr.evolve(
         PPO_TORCH_CONFIG.hyperparameters, learning_rate=3.0e-4
 
 
 def test_recurrent_ppo():
-    env = MemoryEnvironment(
-        [BRAIN_NAME], continuous_action_size=1, discrete_action_size=1
-    )
+    env = MemoryEnvironment([BRAIN_NAME], action_sizes=(1, 1))
     new_network_settings = attr.evolve(
         PPO_TORCH_CONFIG.network_settings,
         memory=NetworkSettings.MemorySettings(memory_size=16),
         PPO_TORCH_CONFIG,
         hyperparameters=new_hyperparams,
         network_settings=new_network_settings,
-        max_steps=100000,
+        max_steps=10000,
-
-
-# def test_3cdhybrid_ppo():
-#    env = SimpleEnvironment(
-#        [BRAIN_NAME], continuous_action_size=2, discrete_action_size=1, step_size=0.8
-#    )
-#    new_hyperparams = attr.evolve(
-#        PPO_TORCH_CONFIG.hyperparameters, batch_size=128, buffer_size=1280, beta=0.01
-#    )
-#    config = attr.evolve(
-#        PPO_TORCH_CONFIG, hyperparameters=new_hyperparams, max_steps=10000
-#    )
-#    check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=1.0)
-#
-#
-# def test_3ddhybrid_ppo():
-#    env = SimpleEnvironment(
-#        [BRAIN_NAME], continuous_action_size=1, discrete_action_size=2, step_size=0.8
-#    )
-#    new_hyperparams = attr.evolve(
-#        PPO_TORCH_CONFIG.hyperparameters, batch_size=128, buffer_size=1280, beta=0.01
-#    )
-#    config = attr.evolve(
-#        PPO_TORCH_CONFIG, hyperparameters=new_hyperparams, max_steps=10000
-#    )
-#    check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=1.0)