Move embedder, middleware, and head parameters to framework agnostic modules. (#45)

Part of #28

rl_coach/agents/actor_critic_agent.py

@@ -20,9 +20,9 @@ import numpy as np
 import scipy.signal
 
 from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent, PolicyGradientRescaler
-from rl_coach.architectures.tensorflow_components.heads.policy_head import PolicyHeadParameters
-from rl_coach.architectures.tensorflow_components.heads.v_head import VHeadParameters
-from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.head_parameters import PolicyHeadParameters, VHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
 from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, \
     AgentParameters
 from rl_coach.exploration_policies.categorical import CategoricalParameters
@@ -31,7 +31,6 @@ from rl_coach.logger import screen
 from rl_coach.memories.episodic.single_episode_buffer import SingleEpisodeBufferParameters
 from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace
 from rl_coach.utils import last_sample
-from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
 
 
 class ActorCriticAlgorithmParameters(AlgorithmParameters):

rl_coach/agents/bc_agent.py

@@ -19,13 +19,13 @@ from typing import Union
 import numpy as np
 
 from rl_coach.agents.imitation_agent import ImitationAgent
-from rl_coach.architectures.tensorflow_components.heads.policy_head import PolicyHeadParameters
-from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
+from rl_coach.architectures.head_parameters import PolicyHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
 from rl_coach.base_parameters import AgentParameters, AlgorithmParameters, NetworkParameters, \
     MiddlewareScheme
 from rl_coach.exploration_policies.e_greedy import EGreedyParameters
 from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
-from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
 from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters
 
 

rl_coach/agents/categorical_dqn_agent.py

@@ -20,7 +20,7 @@ import numpy as np
 
 from rl_coach.agents.dqn_agent import DQNNetworkParameters, DQNAlgorithmParameters, DQNAgentParameters
 from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
-from rl_coach.architectures.tensorflow_components.heads.categorical_q_head import CategoricalQHeadParameters
+from rl_coach.architectures.head_parameters import CategoricalQHeadParameters
 from rl_coach.base_parameters import AgentParameters
 from rl_coach.core_types import StateType
 from rl_coach.exploration_policies.e_greedy import EGreedyParameters

rl_coach/agents/cil_agent.py

@@ -17,9 +17,9 @@
 from typing import Union
 
 from rl_coach.agents.imitation_agent import ImitationAgent
-from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
-from rl_coach.architectures.tensorflow_components.heads.cil_head import RegressionHeadParameters
-from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.head_parameters import RegressionHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
 from rl_coach.base_parameters import AgentParameters, MiddlewareScheme, NetworkParameters, AlgorithmParameters
 from rl_coach.exploration_policies.e_greedy import EGreedyParameters
 from rl_coach.memories.non_episodic.balanced_experience_replay import BalancedExperienceReplayParameters

rl_coach/agents/clipped_ppo_agent.py

@@ -23,12 +23,11 @@ import numpy as np
 
 from rl_coach.agents.actor_critic_agent import ActorCriticAgent
 from rl_coach.agents.policy_optimization_agent import PolicyGradientRescaler
-from rl_coach.architectures.tensorflow_components.heads.ppo_head import PPOHeadParameters
-from rl_coach.architectures.tensorflow_components.heads.v_head import VHeadParameters
-from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.head_parameters import PPOHeadParameters, VHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
 from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, \
     AgentParameters
-from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
 from rl_coach.core_types import EnvironmentSteps, Batch, EnvResponse, StateType
 from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
 from rl_coach.exploration_policies.categorical import CategoricalParameters

rl_coach/agents/ddpg_agent.py

@@ -22,10 +22,9 @@ import numpy as np
 
 from rl_coach.agents.actor_critic_agent import ActorCriticAgent
 from rl_coach.agents.agent import Agent
-from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
-from rl_coach.architectures.tensorflow_components.heads.ddpg_actor_head import DDPGActorHeadParameters
-from rl_coach.architectures.tensorflow_components.heads.v_head import VHeadParameters
-from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.head_parameters import DDPGActorHeadParameters, VHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
 from rl_coach.base_parameters import NetworkParameters, AlgorithmParameters, \
     AgentParameters, EmbedderScheme
 from rl_coach.core_types import ActionInfo, EnvironmentSteps

rl_coach/agents/dfp_agent.py

@@ -21,14 +21,13 @@ from typing import Union
 import numpy as np
 
 from rl_coach.agents.agent import Agent
+from rl_coach.architectures.head_parameters import MeasurementsPredictionHeadParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
 from rl_coach.architectures.tensorflow_components.layers import Conv2d, Dense
-from rl_coach.architectures.tensorflow_components.heads.measurements_prediction_head import \
-    MeasurementsPredictionHeadParameters
-from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
 from rl_coach.base_parameters import AlgorithmParameters, AgentParameters, NetworkParameters, \
      MiddlewareScheme
 from rl_coach.core_types import ActionInfo, EnvironmentSteps, RunPhase
-from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
 from rl_coach.exploration_policies.e_greedy import EGreedyParameters
 from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
 from rl_coach.memories.memory import MemoryGranularity

rl_coach/agents/dqn_agent.py

@@ -19,11 +19,11 @@ from typing import Union
 import numpy as np
 
 from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
-from rl_coach.architectures.tensorflow_components.heads.q_head import QHeadParameters
-from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.head_parameters import QHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
 from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters, \
     MiddlewareScheme
-from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
 from rl_coach.core_types import EnvironmentSteps
 from rl_coach.exploration_policies.e_greedy import EGreedyParameters
 from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters

rl_coach/agents/human_agent.py

@@ -23,11 +23,11 @@ from pandas import to_pickle
 
 from rl_coach.agents.agent import Agent
 from rl_coach.agents.bc_agent import BCNetworkParameters
-from rl_coach.architectures.tensorflow_components.heads.policy_head import PolicyHeadParameters
-from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.head_parameters import PolicyHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
 from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, EmbedderScheme, \
     AgentParameters
-from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
 from rl_coach.core_types import ActionInfo
 from rl_coach.exploration_policies.e_greedy import EGreedyParameters
 from rl_coach.logger import screen

rl_coach/agents/n_step_q_agent.py

@@ -20,10 +20,10 @@ import numpy as np
 
 from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent
 from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
-from rl_coach.architectures.tensorflow_components.heads.q_head import QHeadParameters
-from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.head_parameters import QHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
 from rl_coach.base_parameters import AlgorithmParameters, AgentParameters, NetworkParameters
-from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
 
 from rl_coach.core_types import EnvironmentSteps
 from rl_coach.exploration_policies.e_greedy import EGreedyParameters

rl_coach/agents/naf_agent.py

@@ -19,11 +19,11 @@ from typing import Union
 import numpy as np
 
 from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
-from rl_coach.architectures.tensorflow_components.heads.naf_head import NAFHeadParameters
-from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.head_parameters import NAFHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
 from rl_coach.base_parameters import AlgorithmParameters, AgentParameters, \
     NetworkParameters
-from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
 
 from rl_coach.core_types import ActionInfo, EnvironmentSteps
 from rl_coach.exploration_policies.ou_process import OUProcessParameters

rl_coach/agents/nec_agent.py

@@ -21,10 +21,10 @@ from typing import Union
 import numpy as np
 
 from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
-from rl_coach.architectures.tensorflow_components.heads.dnd_q_head import DNDQHeadParameters
-from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.head_parameters import DNDQHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
 from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters
-from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
 
 from rl_coach.core_types import RunPhase, EnvironmentSteps, Episode, StateType
 from rl_coach.exploration_policies.e_greedy import EGreedyParameters

rl_coach/agents/policy_gradients_agent.py

@@ -19,11 +19,11 @@ from typing import Union
 import numpy as np
 
 from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent, PolicyGradientRescaler
-from rl_coach.architectures.tensorflow_components.heads.policy_head import PolicyHeadParameters
-from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.head_parameters import PolicyHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
 from rl_coach.base_parameters import NetworkParameters, AlgorithmParameters, \
     AgentParameters
-from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
 
 from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
 from rl_coach.exploration_policies.categorical import CategoricalParameters

rl_coach/agents/ppo_agent.py

@@ -22,12 +22,11 @@ import numpy as np
 
 from rl_coach.agents.actor_critic_agent import ActorCriticAgent
 from rl_coach.agents.policy_optimization_agent import PolicyGradientRescaler
-from rl_coach.architectures.tensorflow_components.heads.ppo_head import PPOHeadParameters
-from rl_coach.architectures.tensorflow_components.heads.v_head import VHeadParameters
-from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.head_parameters import PPOHeadParameters, VHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
 from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, \
     AgentParameters, DistributedTaskParameters
-from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
 
 from rl_coach.core_types import EnvironmentSteps, Batch
 from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters

rl_coach/agents/qr_dqn_agent.py

@@ -20,8 +20,7 @@ import numpy as np
 
 from rl_coach.agents.dqn_agent import DQNAgentParameters, DQNNetworkParameters, DQNAlgorithmParameters
 from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
-from rl_coach.architectures.tensorflow_components.heads.quantile_regression_q_head import \
-    QuantileRegressionQHeadParameters
+from rl_coach.architectures.head_parameters import QuantileRegressionQHeadParameters
 from rl_coach.core_types import StateType
 from rl_coach.schedules import LinearSchedule
 

rl_coach/agents/rainbow_dqn_agent.py

@@ -21,8 +21,8 @@ import numpy as np
 from rl_coach.agents.categorical_dqn_agent import CategoricalDQNAlgorithmParameters, \
     CategoricalDQNAgent, CategoricalDQNAgentParameters
 from rl_coach.agents.dqn_agent import DQNNetworkParameters
-from rl_coach.architectures.tensorflow_components.heads.rainbow_q_head import RainbowQHeadParameters
-from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
+from rl_coach.architectures.head_parameters import RainbowQHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
 from rl_coach.base_parameters import MiddlewareScheme
 from rl_coach.exploration_policies.parameter_noise import ParameterNoiseParameters
 from rl_coach.memories.non_episodic.prioritized_experience_replay import PrioritizedExperienceReplayParameters, \

rl_coach/architectures/embedder_parameters.py

@@ -0,0 +1,41 @@
+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import List, Union
+
+from rl_coach.base_parameters import EmbedderScheme, NetworkComponentParameters
+
+
+class InputEmbedderParameters(NetworkComponentParameters):
+    def __init__(self, activation_function: str='relu', scheme: Union[List, EmbedderScheme]=EmbedderScheme.Medium,
+                 batchnorm: bool=False, dropout=False, name: str='embedder', input_rescaling=None, input_offset=None,
+                 input_clipping=None, dense_layer=None, is_training=False):
+        super().__init__(dense_layer=dense_layer)
+        self.activation_function = activation_function
+        self.scheme = scheme
+        self.batchnorm = batchnorm
+        self.dropout = dropout
+
+        if input_rescaling is None:
+            input_rescaling = {'image': 255.0, 'vector': 1.0}
+        if input_offset is None:
+            input_offset = {'image': 0.0, 'vector': 0.0}
+
+        self.input_rescaling = input_rescaling
+        self.input_offset = input_offset
+        self.input_clipping = input_clipping
+        self.name = name
+        self.is_training = is_training

rl_coach/architectures/head_parameters.py

@@ -0,0 +1,173 @@
+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import Type
+
+from rl_coach.base_parameters import NetworkComponentParameters
+
+
+class HeadParameters(NetworkComponentParameters):
+    def __init__(self, parameterized_class_name: str, activation_function: str = 'relu', name: str= 'head',
+                 num_output_head_copies: int=1, rescale_gradient_from_head_by_factor: float=1.0,
+                 loss_weight: float=1.0, dense_layer=None):
+        super().__init__(dense_layer=dense_layer)
+        self.activation_function = activation_function
+        self.name = name
+        self.num_output_head_copies = num_output_head_copies
+        self.rescale_gradient_from_head_by_factor = rescale_gradient_from_head_by_factor
+        self.loss_weight = loss_weight
+        self.parameterized_class_name = parameterized_class_name
+
+
+class PPOHeadParameters(HeadParameters):
+    def __init__(self, activation_function: str ='tanh', name: str='ppo_head_params',
+                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
+                 loss_weight: float = 1.0, dense_layer=None):
+        super().__init__(parameterized_class_name="PPOHead", activation_function=activation_function, name=name,
+                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
+                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
+                         loss_weight=loss_weight)
+
+
+class VHeadParameters(HeadParameters):
+    def __init__(self, activation_function: str ='relu', name: str='v_head_params',
+                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
+                 loss_weight: float = 1.0, dense_layer=None):
+        super().__init__(parameterized_class_name="VHead", activation_function=activation_function, name=name,
+                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
+                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
+                         loss_weight=loss_weight)
+
+
+class CategoricalQHeadParameters(HeadParameters):
+    def __init__(self, activation_function: str ='relu', name: str='categorical_q_head_params',
+                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
+                 loss_weight: float = 1.0, dense_layer=None):
+        super().__init__(parameterized_class_name="CategoricalQHead", activation_function=activation_function, name=name,
+                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
+                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
+                         loss_weight=loss_weight)
+
+
+class RegressionHeadParameters(HeadParameters):
+    def __init__(self, activation_function: str ='relu', name: str='q_head_params',
+                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
+                 loss_weight: float = 1.0, dense_layer=None, scheme=None):
+        super().__init__(parameterized_class_name="RegressionHead", activation_function=activation_function, name=name,
+                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
+                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
+                         loss_weight=loss_weight)
+
+
+class DDPGActorHeadParameters(HeadParameters):
+    def __init__(self, activation_function: str ='tanh', name: str='policy_head_params', batchnorm: bool=True,
+                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
+                 loss_weight: float = 1.0, dense_layer=None):
+        super().__init__(parameterized_class_name="DDPGActor", activation_function=activation_function, name=name,
+                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
+                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
+                         loss_weight=loss_weight)
+        self.batchnorm = batchnorm
+
+
+class DNDQHeadParameters(HeadParameters):
+    def __init__(self, activation_function: str ='relu', name: str='dnd_q_head_params',
+                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
+                 loss_weight: float = 1.0, dense_layer=None):
+        super().__init__(parameterized_class_name="DNDQHead", activation_function=activation_function, name=name,
+                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
+                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
+                         loss_weight=loss_weight)
+
+
+class DuelingQHeadParameters(HeadParameters):
+    def __init__(self, activation_function: str ='relu', name: str='dueling_q_head_params',
+                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
+                 loss_weight: float = 1.0, dense_layer=None):
+        super().__init__(parameterized_class_name="DuelingQHead", activation_function=activation_function, name=name,
+                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
+                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
+                         loss_weight=loss_weight)
+
+
+class MeasurementsPredictionHeadParameters(HeadParameters):
+    def __init__(self, activation_function: str ='relu', name: str='measurements_prediction_head_params',
+                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
+                 loss_weight: float = 1.0, dense_layer=None):
+        super().__init__(parameterized_class_name="MeasurementsPredictionHead", activation_function=activation_function, name=name,
+                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
+                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
+                         loss_weight=loss_weight)
+
+
+class NAFHeadParameters(HeadParameters):
+    def __init__(self, activation_function: str ='tanh', name: str='naf_head_params',
+                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
+                 loss_weight: float = 1.0, dense_layer=None):
+        super().__init__(parameterized_class_name="NAFHead", activation_function=activation_function, name=name,
+                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
+                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
+                         loss_weight=loss_weight)
+
+
+class PolicyHeadParameters(HeadParameters):
+    def __init__(self, activation_function: str ='tanh', name: str='policy_head_params',
+                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
+                 loss_weight: float = 1.0, dense_layer=None):
+        super().__init__(parameterized_class_name="PolicyHead", activation_function=activation_function, name=name,
+                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
+                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
+                         loss_weight=loss_weight)
+
+
+class PPOVHeadParameters(HeadParameters):
+    def __init__(self, activation_function: str ='relu', name: str='ppo_v_head_params',
+                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
+                 loss_weight: float = 1.0, dense_layer=None):
+        super().__init__(parameterized_class_name="PPOVHead", activation_function=activation_function, name=name,
+                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
+                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
+                         loss_weight=loss_weight)
+
+
+class QHeadParameters(HeadParameters):
+    def __init__(self, activation_function: str ='relu', name: str='q_head_params',
+                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
+                 loss_weight: float = 1.0, dense_layer=None):
+        super().__init__(parameterized_class_name="QHead", activation_function=activation_function, name=name,
+                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
+                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
+                         loss_weight=loss_weight)
+
+
+class QuantileRegressionQHeadParameters(HeadParameters):
+    def __init__(self, activation_function: str ='relu', name: str='quantile_regression_q_head_params',
+                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
+                 loss_weight: float = 1.0, dense_layer=None):
+        super().__init__(parameterized_class_name="QuantileRegressionQHead", activation_function=activation_function, name=name,
+                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
+                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
+                         loss_weight=loss_weight)
+
+
+class RainbowQHeadParameters(HeadParameters):
+    def __init__(self, activation_function: str ='relu', name: str='rainbow_q_head_params',
+                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
+                 loss_weight: float = 1.0, dense_layer=None):
+        super().__init__(parameterized_class_name="RainbowQHead", activation_function=activation_function, name=name,
+                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
+                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
+                         loss_weight=loss_weight)

rl_coach/architectures/middleware_parameters.py

@@ -0,0 +1,54 @@
+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import List, Type, Union
+
+from rl_coach.base_parameters import MiddlewareScheme, NetworkComponentParameters
+
+
+class MiddlewareParameters(NetworkComponentParameters):
+    def __init__(self, parameterized_class_name: str,
+                 activation_function: str='relu', scheme: Union[List, MiddlewareScheme]=MiddlewareScheme.Medium,
+                 batchnorm: bool=False, dropout: bool=False, name='middleware', dense_layer=None, is_training=False):
+        super().__init__(dense_layer=dense_layer)
+        self.activation_function = activation_function
+        self.scheme = scheme
+        self.batchnorm = batchnorm
+        self.dropout = dropout
+        self.name = name
+        self.is_training = is_training
+        self.parameterized_class_name = parameterized_class_name
+
+
+class FCMiddlewareParameters(MiddlewareParameters):
+    def __init__(self, activation_function='relu',
+                 scheme: Union[List, MiddlewareScheme] = MiddlewareScheme.Medium,
+                 batchnorm: bool = False, dropout: bool = False,
+                 name="middleware_fc_embedder", dense_layer=None, is_training=False):
+        super().__init__(parameterized_class_name="FCMiddleware", activation_function=activation_function,
+                         scheme=scheme, batchnorm=batchnorm, dropout=dropout, name=name, dense_layer=dense_layer,
+                         is_training=is_training)
+
+
+class LSTMMiddlewareParameters(MiddlewareParameters):
+    def __init__(self, activation_function='relu', number_of_lstm_cells=256,
+                 scheme: MiddlewareScheme = MiddlewareScheme.Medium,
+                 batchnorm: bool = False, dropout: bool = False,
+                 name="middleware_lstm_embedder", dense_layer=None, is_training=False):
+        super().__init__(parameterized_class_name="LSTMMiddleware", activation_function=activation_function,
+                         scheme=scheme, batchnorm=batchnorm, dropout=dropout, name=name, dense_layer=dense_layer,
+                         is_training=is_training)
+        self.number_of_lstm_cells = number_of_lstm_cells

rl_coach/architectures/tensorflow_components/embedders/__init__.py

@@ -0,0 +1,4 @@
+from .image_embedder import ImageEmbedder
+from .vector_embedder import VectorEmbedder
+
+__all__ = ['ImageEmbedder', 'VectorEmbedder']

rl_coach/architectures/tensorflow_components/embedders/embedder.py

@@ -28,35 +28,6 @@ from rl_coach.core_types import InputEmbedding
 from rl_coach.utils import force_list
 
 
-class InputEmbedderParameters(NetworkComponentParameters):
-    def __init__(self, activation_function: str='relu', scheme: Union[List, EmbedderScheme]=EmbedderScheme.Medium,
-                 batchnorm: bool=False, dropout=False, name: str='embedder', input_rescaling=None, input_offset=None,
-                 input_clipping=None, dense_layer=Dense, is_training=False):
-        super().__init__(dense_layer=dense_layer)
-        self.activation_function = activation_function
-        self.scheme = scheme
-        self.batchnorm = batchnorm
-        self.dropout = dropout
-
-        if input_rescaling is None:
-            input_rescaling = {'image': 255.0, 'vector': 1.0}
-        if input_offset is None:
-            input_offset = {'image': 0.0, 'vector': 0.0}
-
-        self.input_rescaling = input_rescaling
-        self.input_offset = input_offset
-        self.input_clipping = input_clipping
-        self.name = name
-        self.is_training = is_training
-
-    @property
-    def path(self):
-        return {
-            "image": 'image_embedder:ImageEmbedder',
-            "vector": 'vector_embedder:VectorEmbedder'
-        }
-
-
 class InputEmbedder(object):
     """
     An input embedder is the first part of the network, which takes the input from the state and produces a vector
@@ -83,6 +54,8 @@ class InputEmbedder(object):
         self.input_offset = input_offset
         self.input_clipping = input_clipping
         self.dense_layer = dense_layer
+        if self.dense_layer is None:
+            self.dense_layer = Dense
         self.is_training = is_training
 
         # layers order is conv -> batchnorm -> activation -> dropout

rl_coach/architectures/tensorflow_components/general_network.py

@@ -20,10 +20,10 @@ from typing import Dict
 import numpy as np
 import tensorflow as tf
 
-from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.head_parameters import HeadParameters
+from rl_coach.architectures.middleware_parameters import MiddlewareParameters
 from rl_coach.architectures.tensorflow_components.architecture import TensorFlowArchitecture
-from rl_coach.architectures.tensorflow_components.heads.head import HeadParameters
-from rl_coach.architectures.tensorflow_components.middlewares.middleware import MiddlewareParameters
 from rl_coach.base_parameters import AgentParameters, EmbeddingMergerType
 from rl_coach.core_types import PredictionType
 from rl_coach.spaces import SpacesDefinition, PlanarMapsObservationSpace
@@ -136,15 +136,17 @@ class GeneralTensorFlowNetwork(TensorFlowArchitecture):
             raise ValueError("The key for the input embedder ({}) must match one of the following keys: {}"
                              .format(input_name, allowed_inputs.keys()))
 
-        type = "vector"
-        if isinstance(allowed_inputs[input_name], PlanarMapsObservationSpace):
-            type = "image"
+        mod_names = {'image': 'ImageEmbedder', 'vector': 'VectorEmbedder'}
 
-        embedder_path = 'rl_coach.architectures.tensorflow_components.embedders.' + embedder_params.path[type]
+        emb_type = "vector"
+        if isinstance(allowed_inputs[input_name], PlanarMapsObservationSpace):
+            emb_type = "image"
+
+        embedder_path = 'rl_coach.architectures.tensorflow_components.embedders:' + mod_names[emb_type]
         embedder_params_copy = copy.copy(embedder_params)
         embedder_params_copy.activation_function = self.get_activation_function(embedder_params.activation_function)
-        embedder_params_copy.input_rescaling = embedder_params_copy.input_rescaling[type]
-        embedder_params_copy.input_offset = embedder_params_copy.input_offset[type]
+        embedder_params_copy.input_rescaling = embedder_params_copy.input_rescaling[emb_type]
+        embedder_params_copy.input_offset = embedder_params_copy.input_offset[emb_type]
         embedder_params_copy.name = input_name
         module = dynamic_import_and_instantiate_module_from_params(embedder_params_copy,
                                                                    path=embedder_path,
@@ -157,25 +159,25 @@ class GeneralTensorFlowNetwork(TensorFlowArchitecture):
         :param middleware_params: the paramaeters of the middleware class
         :return: the middleware instance
         """
+        mod_name = middleware_params.parameterized_class_name
+        middleware_path = 'rl_coach.architectures.tensorflow_components.middlewares:' + mod_name
         middleware_params_copy = copy.copy(middleware_params)
         middleware_params_copy.activation_function = self.get_activation_function(middleware_params.activation_function)
-        module = dynamic_import_and_instantiate_module_from_params(middleware_params_copy)
+        module = dynamic_import_and_instantiate_module_from_params(middleware_params_copy, path=middleware_path)
         return module
 
     def get_output_head(self, head_params: HeadParameters, head_idx: int):
         """
         Given a head type, creates the head and returns it
         :param head_params: the parameters of the head to create
-        :param head_type: the path to the class of the head under the embedders directory or a full path to a head class.
-                          the path should be in the following structure: <module_path>:<class_path>
         :param head_idx: the head index
-        :param loss_weight: the weight to assign for the embedders loss
         :return: the head
         """
-
+        mod_name = head_params.parameterized_class_name
+        head_path = 'rl_coach.architectures.tensorflow_components.heads:' + mod_name
         head_params_copy = copy.copy(head_params)
         head_params_copy.activation_function = self.get_activation_function(head_params_copy.activation_function)
-        return dynamic_import_and_instantiate_module_from_params(head_params_copy, extra_kwargs={
+        return dynamic_import_and_instantiate_module_from_params(head_params_copy, path=head_path, extra_kwargs={
             'agent_parameters': self.ap, 'spaces': self.spaces, 'network_name': self.network_wrapper_name,
             'head_idx': head_idx, 'is_local': self.network_is_local})
 

rl_coach/architectures/tensorflow_components/heads/__init__.py

@@ -0,0 +1,29 @@
+from .categorical_q_head import CategoricalQHead
+from .ddpg_actor_head import DDPGActor
+from .dnd_q_head import DNDQHead
+from .dueling_q_head import DuelingQHead
+from .measurements_prediction_head import MeasurementsPredictionHead
+from .naf_head import NAFHead
+from .policy_head import PolicyHead
+from .ppo_head import PPOHead
+from .ppo_v_head import PPOVHead
+from .q_head import QHead
+from .quantile_regression_q_head import QuantileRegressionQHead
+from .rainbow_q_head import RainbowQHead
+from .v_head import VHead
+
+__all__ = [
+    'CategoricalQHead',
+    'DDPGActor',
+    'DNDQHead',
+    'DuelingQHead',
+    'MeasurementsPredictionHead',
+    'NAFHead',
+    'PolicyHead',
+    'PPOHead',
+    'PPOVHead',
+    'QHead',
+    'QuantileRegressionQHead',
+    'RainbowQHead',
+    'VHead'
+]

rl_coach/architectures/tensorflow_components/heads/categorical_q_head.py

@@ -18,22 +18,12 @@ import tensorflow as tf
 
 from rl_coach.architectures.tensorflow_components.layers import Dense
 
-from rl_coach.architectures.tensorflow_components.heads.head import Head, HeadParameters
+from rl_coach.architectures.tensorflow_components.heads.head import Head
 from rl_coach.base_parameters import AgentParameters
 from rl_coach.core_types import QActionStateValue
 from rl_coach.spaces import SpacesDefinition
 
 
-class CategoricalQHeadParameters(HeadParameters):
-    def __init__(self, activation_function: str ='relu', name: str='categorical_q_head_params',
-                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
-                 loss_weight: float = 1.0, dense_layer=Dense):
-        super().__init__(parameterized_class=CategoricalQHead, activation_function=activation_function, name=name,
-                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
-                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
-                         loss_weight=loss_weight)
-
-
 class CategoricalQHead(Head):
     def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str,
                  head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str ='relu',

rl_coach/architectures/tensorflow_components/heads/cil_head.py

@@ -16,25 +16,14 @@
 
 import tensorflow as tf
 
-from rl_coach.architectures.tensorflow_components.layers import Dense, batchnorm_activation_dropout
-
-from rl_coach.architectures.tensorflow_components.heads.head import Head, HeadParameters
+from rl_coach.architectures.tensorflow_components.layers import Dense
+from rl_coach.architectures.tensorflow_components.heads.head import Head
 from rl_coach.base_parameters import AgentParameters
 from rl_coach.core_types import QActionStateValue
 from rl_coach.spaces import SpacesDefinition, BoxActionSpace, DiscreteActionSpace
 from rl_coach.utils import force_list
 
 
-class RegressionHeadParameters(HeadParameters):
-    def __init__(self, activation_function: str ='relu', name: str='q_head_params',
-                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
-                 loss_weight: float = 1.0, dense_layer=Dense, scheme=[Dense(256), Dense(256)]):
-        super().__init__(parameterized_class=RegressionHead, activation_function=activation_function, name=name,
-                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
-                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
-                         loss_weight=loss_weight)
-
-
 class RegressionHead(Head):
     def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str,
                  head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='relu',

rl_coach/architectures/tensorflow_components/heads/ddpg_actor_head.py

@@ -17,23 +17,12 @@
 import tensorflow as tf
 
 from rl_coach.architectures.tensorflow_components.layers import batchnorm_activation_dropout, Dense
-from rl_coach.architectures.tensorflow_components.heads.head import Head, HeadParameters
+from rl_coach.architectures.tensorflow_components.heads.head import Head
 from rl_coach.base_parameters import AgentParameters
 from rl_coach.core_types import ActionProbabilities
 from rl_coach.spaces import SpacesDefinition
 
 
-class DDPGActorHeadParameters(HeadParameters):
-    def __init__(self, activation_function: str ='tanh', name: str='policy_head_params', batchnorm: bool=True,
-                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
-                 loss_weight: float = 1.0, dense_layer=Dense):
-        super().__init__(parameterized_class=DDPGActor, activation_function=activation_function, name=name,
-                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
-                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
-                         loss_weight=loss_weight)
-        self.batchnorm = batchnorm
-
-
 class DDPGActor(Head):
     def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str,
                  head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='tanh',

rl_coach/architectures/tensorflow_components/heads/dnd_q_head.py

@@ -16,23 +16,12 @@
 import tensorflow as tf
 
 from rl_coach.architectures.tensorflow_components.layers import Dense
-from rl_coach.architectures.tensorflow_components.heads.head import HeadParameters
 from rl_coach.architectures.tensorflow_components.heads.q_head import QHead
 from rl_coach.base_parameters import AgentParameters
 from rl_coach.memories.non_episodic import differentiable_neural_dictionary
 from rl_coach.spaces import SpacesDefinition
 
 
-class DNDQHeadParameters(HeadParameters):
-    def __init__(self, activation_function: str ='relu', name: str='dnd_q_head_params',
-                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
-                 loss_weight: float = 1.0, dense_layer=Dense):
-        super().__init__(parameterized_class=DNDQHead, activation_function=activation_function, name=name,
-                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
-                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
-                         loss_weight=loss_weight)
-
-
 class DNDQHead(QHead):
     def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str,
                  head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='relu',

rl_coach/architectures/tensorflow_components/heads/dueling_q_head.py

@@ -17,21 +17,11 @@
 import tensorflow as tf
 
 from rl_coach.architectures.tensorflow_components.layers import Dense
-from rl_coach.architectures.tensorflow_components.heads.head import HeadParameters
 from rl_coach.architectures.tensorflow_components.heads.q_head import QHead
 from rl_coach.base_parameters import AgentParameters
 from rl_coach.spaces import SpacesDefinition
 
 
-class DuelingQHeadParameters(HeadParameters):
-    def __init__(self, activation_function: str ='relu', name: str='dueling_q_head_params',
-                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
-                 loss_weight: float = 1.0, dense_layer=Dense):
-        super().__init__(parameterized_class=DuelingQHead, activation_function=activation_function, name=name,
-                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
-                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
-                         loss_weight=loss_weight)
-
 class DuelingQHead(QHead):
     def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str,
                  head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='relu',

rl_coach/architectures/tensorflow_components/heads/head.py

@@ -33,19 +33,6 @@ def normalized_columns_initializer(std=1.0):
     return _initializer
 
 
-class HeadParameters(NetworkComponentParameters):
-    def __init__(self, parameterized_class: Type['Head'], activation_function: str = 'relu', name: str= 'head',
-                 num_output_head_copies: int=1, rescale_gradient_from_head_by_factor: float=1.0,
-                 loss_weight: float=1.0, dense_layer=Dense):
-        super().__init__(dense_layer=dense_layer)
-        self.activation_function = activation_function
-        self.name = name
-        self.num_output_head_copies = num_output_head_copies
-        self.rescale_gradient_from_head_by_factor = rescale_gradient_from_head_by_factor
-        self.loss_weight = loss_weight
-        self.parameterized_class_name = parameterized_class.__name__
-
-
 class Head(object):
     """
     A head is the final part of the network. It takes the embedding from the middleware embedder and passes it through
@@ -74,6 +61,8 @@ class Head(object):
         self.return_type = None
         self.activation_function = activation_function
         self.dense_layer = dense_layer
+        if self.dense_layer is None:
+            self.dense_layer = Dense
 
     def __call__(self, input_layer):
         """

rl_coach/architectures/tensorflow_components/heads/measurements_prediction_head.py

@@ -17,23 +17,12 @@
 import tensorflow as tf
 
 from rl_coach.architectures.tensorflow_components.layers import Dense
-
-from rl_coach.architectures.tensorflow_components.heads.head import Head, HeadParameters
+from rl_coach.architectures.tensorflow_components.heads.head import Head
 from rl_coach.base_parameters import AgentParameters
 from rl_coach.core_types import Measurements
 from rl_coach.spaces import SpacesDefinition
 
 
-class MeasurementsPredictionHeadParameters(HeadParameters):
-    def __init__(self, activation_function: str ='relu', name: str='measurements_prediction_head_params',
-                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
-                 loss_weight: float = 1.0, dense_layer=Dense):
-        super().__init__(parameterized_class=MeasurementsPredictionHead, activation_function=activation_function, name=name,
-                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
-                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
-                         loss_weight=loss_weight)
-
-
 class MeasurementsPredictionHead(Head):
     def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str,
                  head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='relu',

rl_coach/architectures/tensorflow_components/heads/naf_head.py

@@ -17,23 +17,13 @@
 import tensorflow as tf
 
 from rl_coach.architectures.tensorflow_components.layers import Dense
-from rl_coach.architectures.tensorflow_components.heads.head import Head, HeadParameters
+from rl_coach.architectures.tensorflow_components.heads.head import Head
 from rl_coach.base_parameters import AgentParameters
 from rl_coach.core_types import QActionStateValue
 from rl_coach.spaces import BoxActionSpace
 from rl_coach.spaces import SpacesDefinition
 
 
-class NAFHeadParameters(HeadParameters):
-    def __init__(self, activation_function: str ='tanh', name: str='naf_head_params',
-                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
-                 loss_weight: float = 1.0, dense_layer=Dense):
-        super().__init__(parameterized_class=NAFHead, activation_function=activation_function, name=name,
-                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
-                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
-                         loss_weight=loss_weight)
-
-
 class NAFHead(Head):
     def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str,
                  head_idx: int = 0, loss_weight: float = 1., is_local: bool = True,activation_function: str='relu',

rl_coach/architectures/tensorflow_components/heads/policy_head.py

@@ -18,7 +18,7 @@ import numpy as np
 import tensorflow as tf
 
 from rl_coach.architectures.tensorflow_components.layers import Dense
-from rl_coach.architectures.tensorflow_components.heads.head import Head, normalized_columns_initializer, HeadParameters
+from rl_coach.architectures.tensorflow_components.heads.head import Head, normalized_columns_initializer
 from rl_coach.base_parameters import AgentParameters
 from rl_coach.core_types import ActionProbabilities
 from rl_coach.exploration_policies.continuous_entropy import ContinuousEntropyParameters
@@ -27,17 +27,6 @@ from rl_coach.spaces import SpacesDefinition
 from rl_coach.utils import eps, indent_string
 
 
-class PolicyHeadParameters(HeadParameters):
-    def __init__(self, activation_function: str ='tanh', name: str='policy_head_params',
-                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
-                 loss_weight: float = 1.0, dense_layer=Dense):
-        super().__init__(parameterized_class=PolicyHead, activation_function=activation_function, name=name,
-                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
-                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
-                         loss_weight=loss_weight)
-
-
-
 class PolicyHead(Head):
     def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str,
                  head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='tanh',

rl_coach/architectures/tensorflow_components/heads/ppo_head.py

@@ -18,7 +18,7 @@ import numpy as np
 import tensorflow as tf
 
 from rl_coach.architectures.tensorflow_components.layers import Dense
-from rl_coach.architectures.tensorflow_components.heads.head import Head, HeadParameters, normalized_columns_initializer
+from rl_coach.architectures.tensorflow_components.heads.head import Head, normalized_columns_initializer
 from rl_coach.base_parameters import AgentParameters
 from rl_coach.core_types import ActionProbabilities
 from rl_coach.spaces import BoxActionSpace, DiscreteActionSpace
@@ -26,16 +26,6 @@ from rl_coach.spaces import SpacesDefinition
 from rl_coach.utils import eps
 
 
-class PPOHeadParameters(HeadParameters):
-    def __init__(self, activation_function: str ='tanh', name: str='ppo_head_params',
-                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
-                 loss_weight: float = 1.0, dense_layer=Dense):
-        super().__init__(parameterized_class=PPOHead, activation_function=activation_function, name=name,
-                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
-                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
-                         loss_weight=loss_weight)
-
-
 class PPOHead(Head):
     def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str,
                  head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='tanh',

rl_coach/architectures/tensorflow_components/heads/ppo_v_head.py

@@ -17,23 +17,12 @@
 import tensorflow as tf
 
 from rl_coach.architectures.tensorflow_components.layers import Dense
-
-from rl_coach.architectures.tensorflow_components.heads.head import Head, normalized_columns_initializer, HeadParameters
+from rl_coach.architectures.tensorflow_components.heads.head import Head, normalized_columns_initializer
 from rl_coach.base_parameters import AgentParameters
 from rl_coach.core_types import ActionProbabilities
 from rl_coach.spaces import SpacesDefinition
 
 
-class PPOVHeadParameters(HeadParameters):
-    def __init__(self, activation_function: str ='relu', name: str='ppo_v_head_params',
-                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
-                 loss_weight: float = 1.0, dense_layer=Dense):
-        super().__init__(parameterized_class=PPOVHead, activation_function=activation_function, name=name,
-                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
-                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
-                         loss_weight=loss_weight)
-
-
 class PPOVHead(Head):
     def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str,
                  head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='relu',

rl_coach/architectures/tensorflow_components/heads/q_head.py

@@ -17,23 +17,12 @@
 import tensorflow as tf
 
 from rl_coach.architectures.tensorflow_components.layers import Dense
-
-from rl_coach.architectures.tensorflow_components.heads.head import Head, HeadParameters
+from rl_coach.architectures.tensorflow_components.heads.head import Head
 from rl_coach.base_parameters import AgentParameters
 from rl_coach.core_types import QActionStateValue
 from rl_coach.spaces import SpacesDefinition, BoxActionSpace, DiscreteActionSpace
 
 
-class QHeadParameters(HeadParameters):
-    def __init__(self, activation_function: str ='relu', name: str='q_head_params',
-                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
-                 loss_weight: float = 1.0, dense_layer=Dense):
-        super().__init__(parameterized_class=QHead, activation_function=activation_function, name=name,
-                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
-                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
-                         loss_weight=loss_weight)
-
-
 class QHead(Head):
     def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str,
                  head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='relu',

rl_coach/architectures/tensorflow_components/heads/quantile_regression_q_head.py

@@ -17,23 +17,12 @@
 import tensorflow as tf
 
 from rl_coach.architectures.tensorflow_components.layers import Dense
-
-from rl_coach.architectures.tensorflow_components.heads.head import Head, HeadParameters
+from rl_coach.architectures.tensorflow_components.heads.head import Head
 from rl_coach.base_parameters import AgentParameters
 from rl_coach.core_types import QActionStateValue
 from rl_coach.spaces import SpacesDefinition
 
 
-class QuantileRegressionQHeadParameters(HeadParameters):
-    def __init__(self, activation_function: str ='relu', name: str='quantile_regression_q_head_params',
-                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
-                 loss_weight: float = 1.0, dense_layer=Dense):
-        super().__init__(parameterized_class=QuantileRegressionQHead, activation_function=activation_function, name=name,
-                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
-                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
-                         loss_weight=loss_weight)
-
-
 class QuantileRegressionQHead(Head):
     def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str,
                  head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='relu',

rl_coach/architectures/tensorflow_components/heads/rainbow_q_head.py

@@ -17,22 +17,12 @@
 import tensorflow as tf
 
 from rl_coach.architectures.tensorflow_components.layers import Dense
-from rl_coach.architectures.tensorflow_components.heads.head import HeadParameters, Head
+from rl_coach.architectures.tensorflow_components.heads.head import Head
 from rl_coach.base_parameters import AgentParameters
 from rl_coach.core_types import QActionStateValue
 from rl_coach.spaces import SpacesDefinition
 
 
-class RainbowQHeadParameters(HeadParameters):
-    def __init__(self, activation_function: str ='relu', name: str='rainbow_q_head_params',
-                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
-                 loss_weight: float = 1.0, dense_layer=Dense):
-        super().__init__(parameterized_class=RainbowQHead, activation_function=activation_function, name=name,
-                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
-                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
-                         loss_weight=loss_weight)
-
-
 class RainbowQHead(Head):
     def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str,
                  head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='relu',

rl_coach/architectures/tensorflow_components/heads/v_head.py

@@ -17,23 +17,12 @@
 import tensorflow as tf
 
 from rl_coach.architectures.tensorflow_components.layers import Dense
-
-from rl_coach.architectures.tensorflow_components.heads.head import Head, normalized_columns_initializer, HeadParameters
+from rl_coach.architectures.tensorflow_components.heads.head import Head, normalized_columns_initializer
 from rl_coach.base_parameters import AgentParameters
 from rl_coach.core_types import VStateValue
 from rl_coach.spaces import SpacesDefinition
 
 
-class VHeadParameters(HeadParameters):
-    def __init__(self, activation_function: str ='relu', name: str='v_head_params',
-                 num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
-                 loss_weight: float = 1.0, dense_layer=Dense):
-        super().__init__(parameterized_class=VHead, activation_function=activation_function, name=name,
-                         dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
-                         rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
-                         loss_weight=loss_weight)
-
-
 class VHead(Head):
     def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str,
                  head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='relu',

rl_coach/architectures/tensorflow_components/middlewares/__init__.py

@@ -0,0 +1,4 @@
+from .fc_middleware import FCMiddleware
+from .lstm_middleware import LSTMMiddleware
+
+__all__ = ["FCMiddleware", "LSTMMiddleware"]

rl_coach/architectures/tensorflow_components/middlewares/fc_middleware.py

@@ -18,22 +18,12 @@ from typing import Union, List
 import tensorflow as tf
 
 from rl_coach.architectures.tensorflow_components.layers import batchnorm_activation_dropout, Dense
-from rl_coach.architectures.tensorflow_components.middlewares.middleware import Middleware, MiddlewareParameters
+from rl_coach.architectures.tensorflow_components.middlewares.middleware import Middleware
 from rl_coach.base_parameters import MiddlewareScheme
 from rl_coach.core_types import Middleware_FC_Embedding
 from rl_coach.utils import force_list
 
 
-class FCMiddlewareParameters(MiddlewareParameters):
-    def __init__(self, activation_function='relu',
-                 scheme: Union[List, MiddlewareScheme] = MiddlewareScheme.Medium,
-                 batchnorm: bool = False, dropout: bool = False,
-                 name="middleware_fc_embedder", dense_layer=Dense, is_training=False):
-        super().__init__(parameterized_class=FCMiddleware, activation_function=activation_function,
-                         scheme=scheme, batchnorm=batchnorm, dropout=dropout, name=name, dense_layer=dense_layer,
-                         is_training=is_training)
-
-
 class FCMiddleware(Middleware):
     def __init__(self, activation_function=tf.nn.relu,
                  scheme: MiddlewareScheme = MiddlewareScheme.Medium,

rl_coach/architectures/tensorflow_components/middlewares/lstm_middleware.py

@@ -19,23 +19,12 @@ import numpy as np
 import tensorflow as tf
 
 from rl_coach.architectures.tensorflow_components.layers import batchnorm_activation_dropout, Dense
-from rl_coach.architectures.tensorflow_components.middlewares.middleware import Middleware, MiddlewareParameters
+from rl_coach.architectures.tensorflow_components.middlewares.middleware import Middleware
 from rl_coach.base_parameters import MiddlewareScheme
 from rl_coach.core_types import Middleware_LSTM_Embedding
 from rl_coach.utils import force_list
 
 
-class LSTMMiddlewareParameters(MiddlewareParameters):
-    def __init__(self, activation_function='relu', number_of_lstm_cells=256,
-                 scheme: MiddlewareScheme = MiddlewareScheme.Medium,
-                 batchnorm: bool = False, dropout: bool = False,
-                 name="middleware_lstm_embedder", dense_layer=Dense, is_training=False):
-        super().__init__(parameterized_class=LSTMMiddleware, activation_function=activation_function,
-                         scheme=scheme, batchnorm=batchnorm, dropout=dropout, name=name, dense_layer=dense_layer,
-                         is_training=is_training)
-        self.number_of_lstm_cells = number_of_lstm_cells
-
-
 class LSTMMiddleware(Middleware):
     def __init__(self, activation_function=tf.nn.relu, number_of_lstm_cells: int=256,
                  scheme: MiddlewareScheme = MiddlewareScheme.Medium,

rl_coach/architectures/tensorflow_components/middlewares/middleware.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 #
 import copy
-from typing import Type, Union, List
 
 import tensorflow as tf
 
@@ -23,20 +22,6 @@ from rl_coach.base_parameters import MiddlewareScheme, NetworkComponentParameter
 from rl_coach.core_types import MiddlewareEmbedding
 
 
-class MiddlewareParameters(NetworkComponentParameters):
-    def __init__(self, parameterized_class: Type['Middleware'],
-                 activation_function: str='relu', scheme: Union[List, MiddlewareScheme]=MiddlewareScheme.Medium,
-                 batchnorm: bool=False, dropout: bool=False, name='middleware', dense_layer=Dense, is_training=False):
-        super().__init__(dense_layer=dense_layer)
-        self.activation_function = activation_function
-        self.scheme = scheme
-        self.batchnorm = batchnorm
-        self.dropout = dropout
-        self.name = name
-        self.is_training = is_training
-        self.parameterized_class_name = parameterized_class.__name__
-
-
 class Middleware(object):
     """
     A middleware embedder is the middle part of the network. It takes the embeddings from the input embedders,
@@ -57,6 +42,8 @@ class Middleware(object):
         self.scheme = scheme
         self.return_type = MiddlewareEmbedding
         self.dense_layer = dense_layer
+        if self.dense_layer is None:
+            self.dense_layer = Dense
         self.is_training = is_training
 
         # layers order is conv -> batchnorm -> activation -> dropout

rl_coach/presets/Atari_A3C.py

@@ -1,5 +1,5 @@
 from rl_coach.agents.actor_critic_agent import ActorCriticAgentParameters
-from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
 from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters
 from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
 from rl_coach.environments.environment import SingleLevelSelection

rl_coach/presets/Atari_A3C_LSTM.py

@@ -1,5 +1,5 @@
 from rl_coach.agents.actor_critic_agent import ActorCriticAgentParameters
-from rl_coach.architectures.tensorflow_components.middlewares.lstm_middleware import LSTMMiddlewareParameters
+from rl_coach.architectures.middleware_parameters import LSTMMiddlewareParameters
 from rl_coach.base_parameters import VisualizationParameters, MiddlewareScheme, PresetValidationParameters
 from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
 from rl_coach.environments.environment import SingleLevelSelection

rl_coach/presets/Atari_Dueling_DDQN.py

@@ -1,7 +1,7 @@
 import math
 
 from rl_coach.agents.ddqn_agent import DDQNAgentParameters
-from rl_coach.architectures.tensorflow_components.heads.dueling_q_head import DuelingQHeadParameters
+from rl_coach.architectures.head_parameters import DuelingQHeadParameters
 from rl_coach.base_parameters import VisualizationParameters, MiddlewareScheme, PresetValidationParameters
 from rl_coach.environments.environment import SingleLevelSelection
 from rl_coach.environments.gym_environment import Atari, atari_deterministic_v4, atari_schedule

rl_coach/presets/Atari_Dueling_DDQN_with_PER_OpenAI.py

@@ -1,5 +1,5 @@
 from rl_coach.agents.ddqn_agent import DDQNAgentParameters
-from rl_coach.architectures.tensorflow_components.heads.dueling_q_head import DuelingQHeadParameters
+from rl_coach.architectures.head_parameters import DuelingQHeadParameters
 from rl_coach.base_parameters import VisualizationParameters, MiddlewareScheme, PresetValidationParameters
 from rl_coach.core_types import EnvironmentSteps
 from rl_coach.environments.environment import SingleLevelSelection

rl_coach/presets/BitFlip_DQN.py

@@ -1,5 +1,5 @@
 from rl_coach.agents.dqn_agent import DQNAgentParameters
-from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
 from rl_coach.architectures.tensorflow_components.layers import Dense
 from rl_coach.base_parameters import VisualizationParameters, EmbedderScheme, \
     PresetValidationParameters

rl_coach/presets/BitFlip_DQN_HER.py

@@ -1,5 +1,5 @@
 from rl_coach.agents.dqn_agent import DQNAgentParameters
-from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
 from rl_coach.architectures.tensorflow_components.layers import Dense
 from rl_coach.base_parameters import VisualizationParameters, EmbedderScheme, \
     PresetValidationParameters

rl_coach/presets/CARLA_CIL.py

@@ -7,10 +7,10 @@ from carla.driving_benchmark.experiment_suites import CoRL2017
 from rl_coach.logger import screen
 
 from rl_coach.agents.cil_agent import CILAgentParameters
-from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
-from rl_coach.architectures.tensorflow_components.heads.cil_head import RegressionHeadParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.head_parameters import RegressionHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
 from rl_coach.architectures.tensorflow_components.layers import Conv2d, Dense, BatchnormActivationDropout
-from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
 from rl_coach.base_parameters import VisualizationParameters
 from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
 from rl_coach.environments.carla_environment import CarlaEnvironmentParameters

rl_coach/presets/CARLA_Dueling_DDQN.py

@@ -1,7 +1,7 @@
 import math
 
 from rl_coach.agents.ddqn_agent import DDQNAgentParameters
-from rl_coach.architectures.tensorflow_components.heads.dueling_q_head import DuelingQHeadParameters
+from rl_coach.architectures.head_parameters import DuelingQHeadParameters
 from rl_coach.base_parameters import VisualizationParameters, MiddlewareScheme
 from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
 from rl_coach.environments.carla_environment import CarlaEnvironmentParameters

rl_coach/presets/CartPole_Dueling_DDQN.py

@@ -1,7 +1,7 @@
 import math
 
 from rl_coach.agents.ddqn_agent import DDQNAgentParameters
-from rl_coach.architectures.tensorflow_components.heads.dueling_q_head import DuelingQHeadParameters
+from rl_coach.architectures.head_parameters import DuelingQHeadParameters
 from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters
 from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
 from rl_coach.environments.gym_environment import GymVectorEnvironment

rl_coach/presets/Doom_Basic_Dueling_DDQN.py

@@ -1,5 +1,5 @@
 from rl_coach.agents.ddqn_agent import DDQNAgentParameters
-from rl_coach.architectures.tensorflow_components.heads.dueling_q_head import DuelingQHeadParameters
+from rl_coach.architectures.head_parameters import DuelingQHeadParameters
 from rl_coach.base_parameters import VisualizationParameters
 from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
 from rl_coach.environments.doom_environment import DoomEnvironmentParameters

rl_coach/presets/ExplorationChain_Dueling_DDQN.py

@@ -1,5 +1,5 @@
 from rl_coach.agents.ddqn_agent import DDQNAgentParameters
-from rl_coach.architectures.tensorflow_components.heads.dueling_q_head import DuelingQHeadParameters
+from rl_coach.architectures.head_parameters import DuelingQHeadParameters
 from rl_coach.base_parameters import VisualizationParameters
 from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps
 from rl_coach.environments.gym_environment import GymEnvironmentParameters

rl_coach/presets/Fetch_DDPG_HER_baselines.py

@@ -1,7 +1,7 @@
 from rl_coach.agents.ddpg_agent import DDPGAgentParameters
-from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
 from rl_coach.architectures.tensorflow_components.layers import Dense
-from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
 from rl_coach.base_parameters import VisualizationParameters, EmbedderScheme, PresetValidationParameters
 from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps, TrainingSteps
 from rl_coach.environments.environment import SingleLevelSelection

rl_coach/presets/Mujoco_A3C_LSTM.py

@@ -1,7 +1,7 @@
 from rl_coach.agents.actor_critic_agent import ActorCriticAgentParameters
-from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.middleware_parameters import LSTMMiddlewareParameters
 from rl_coach.architectures.tensorflow_components.layers import Dense
-from rl_coach.architectures.tensorflow_components.middlewares.lstm_middleware import LSTMMiddlewareParameters
 from rl_coach.base_parameters import VisualizationParameters, MiddlewareScheme, PresetValidationParameters
 from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
 from rl_coach.environments.environment import SingleLevelSelection

rl_coach/presets/Pendulum_HAC.py

@@ -1,7 +1,7 @@
 import numpy as np
 
 from rl_coach.agents.hac_ddpg_agent import HACDDPGAgentParameters
-from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
 from rl_coach.architectures.tensorflow_components.layers import Dense
 from rl_coach.base_parameters import VisualizationParameters, EmbeddingMergerType, EmbedderScheme
 from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps, TrainingSteps

rl_coach/presets/Starcraft_CollectMinerals_A3C.py

@@ -1,6 +1,6 @@
 from rl_coach.agents.actor_critic_agent import ActorCriticAgentParameters
 from rl_coach.agents.policy_optimization_agent import PolicyGradientRescaler
-from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
 from rl_coach.base_parameters import VisualizationParameters
 from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
 from rl_coach.environments.starcraft2_environment import StarCraft2EnvironmentParameters

rl_coach/presets/Starcraft_CollectMinerals_Dueling_DDQN.py

@@ -1,8 +1,8 @@
 from collections import OrderedDict
 
 from rl_coach.agents.ddqn_agent import DDQNAgentParameters
-from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
-from rl_coach.architectures.tensorflow_components.heads.dueling_q_head import DuelingQHeadParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.head_parameters import DuelingQHeadParameters
 from rl_coach.base_parameters import VisualizationParameters
 from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
 from rl_coach.environments.starcraft2_environment import StarCraft2EnvironmentParameters

tutorials/0. Quick Start Guide.ipynb

@@ -134,7 +134,7 @@
     "from rl_coach.environments.gym_environment import GymVectorEnvironment\n",
     "from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager\n",
     "from rl_coach.graph_managers.graph_manager import SimpleSchedule\n",
-    "from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters\n",
+    "from rl_coach.architectures.embedder_parameters import InputEmbedderParameters\n",
     "\n",
     "# define the environment parameters\n",
     "bit_length = 10\n",

tutorials/3. Implementing a Hierarchical RL Graph.ipynb

@@ -162,9 +162,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from rl_coach.architectures.tensorflow_components.architecture import Dense\n",
+    "from rl_coach.architectures.tensorflow_components.layers import Dense\n",
     "from rl_coach.base_parameters import VisualizationParameters, EmbeddingMergerType, EmbedderScheme\n",
-    "from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters\n",
+    "from rl_coach.architectures.embedder_parameters import InputEmbedderParameters\n",
     "from rl_coach.memories.episodic.episodic_hindsight_experience_replay import HindsightGoalSelectionMethod, \\\n",
     "    EpisodicHindsightExperienceReplayParameters\n",
     "from rl_coach.memories.episodic.episodic_hrl_hindsight_experience_replay import \\\n",