From 138ced23ba57efd34237b816a25b83fb9f04417d Mon Sep 17 00:00:00 2001 From: Gal Leibovich Date: Sun, 8 Sep 2019 12:53:49 +0300 Subject: [PATCH] RL in Large Discrete Action Spaces - Wolpertinger Agent (#394) * Currently this is specific to the case of discretizing a continuous action space. Can easily be adapted to other case by feeding the kNN otherwise, and removing the usage of a discretizing output action filter --- docs/_images/algorithms.png | Bin 60878 -> 64957 bytes docs/_images/wolpertinger.png | Bin 0 -> 50456 bytes docs/_modules/index.html | 1 + docs/_modules/rl_coach/agents/agent.html | 15 +- .../rl_coach/agents/clipped_ppo_agent.html | 12 +- .../rl_coach/agents/wolpertinger_agent.html | 356 ++++++++++++++++++ docs/_modules/rl_coach/base_parameters.html | 8 + docs/_modules/rl_coach/core_types.html | 6 + .../rl_coach/data_stores/nfs_data_store.html | 8 +- .../rl_coach/data_stores/s3_data_store.html | 5 +- .../exploration_policies/additive_noise.html | 4 +- .../exploration_policy.html | 5 +- .../truncated_normal.html | 3 - .../partial_discrete_action_space_map.html | 3 +- .../rl_coach/memories/backend/redis.html | 11 +- .../differentiable_neural_dictionary.html | 4 +- .../kubernetes_orchestrator.html | 70 +++- docs/_modules/rl_coach/spaces.html | 6 +- docs/_sources/components/agents/index.rst.txt | 6 +- .../policy_optimization/wolpertinger.rst.txt | 56 +++ docs/components/agents/index.html | 14 +- .../policy_optimization/wolpertinger.html | 276 ++++++++++++++ docs/components/spaces.html | 2 +- docs/features/benchmarks.html | 5 +- docs/features/environments.html | 1 - docs/genindex.html | 9 + docs/objects.inv | Bin 3694 -> 3726 bytes docs/searchindex.js | 2 +- docs/test.html | 2 +- docs_raw/source/_static/img/algorithms.png | Bin 60878 -> 64957 bytes .../_static/img/design_imgs/wolpertinger.png | Bin 0 -> 50456 bytes docs_raw/source/algorithms.xml | 2 +- docs_raw/source/components/agents/index.rst | 6 +- .../policy_optimization/wolpertinger.rst | 56 +++ docs_raw/source/diagrams.xml | 2 +- rl_coach/agents/agent.py | 2 +- rl_coach/agents/wolpertinger_agent.py | 131 +++++++ rl_coach/architectures/head_parameters.py | 11 + .../tensorflow_components/heads/__init__.py | 6 +- .../heads/wolpertinger_actor_head.py | 59 +++ .../exploration_policies/additive_noise.py | 4 +- .../exploration_policy.py | 5 +- .../partial_discrete_action_space_map.py | 3 +- .../differentiable_neural_dictionary.py | 4 +- rl_coach/presets/Mujoco_Wolpertinger.py | 57 +++ rl_coach/spaces.py | 6 +- 46 files changed, 1193 insertions(+), 51 deletions(-) create mode 100644 docs/_images/wolpertinger.png create mode 100644 docs/_modules/rl_coach/agents/wolpertinger_agent.html create mode 100644 docs/_sources/components/agents/policy_optimization/wolpertinger.rst.txt create mode 100644 docs/components/agents/policy_optimization/wolpertinger.html create mode 100644 docs_raw/source/_static/img/design_imgs/wolpertinger.png create mode 100644 docs_raw/source/components/agents/policy_optimization/wolpertinger.rst create mode 100644 rl_coach/agents/wolpertinger_agent.py create mode 100644 rl_coach/architectures/tensorflow_components/heads/wolpertinger_actor_head.py create mode 100644 rl_coach/presets/Mujoco_Wolpertinger.py diff --git 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zD>8s;F{5u6_98hx5+d}Y<1pC2io96jC^==$Dy+r4r5X)>FW}`9VU7U)$w?_mmWdk# F{0GtY&cXly literal 0 HcmV?d00001 diff --git a/docs/_modules/index.html b/docs/_modules/index.html index b774d7e53..5654895fd 100644 --- a/docs/_modules/index.html +++ b/docs/_modules/index.html @@ -202,6 +202,7 @@

All modules for which code is available

  • rl_coach.agents.soft_actor_critic_agent
  • rl_coach.agents.td3_agent
  • rl_coach.agents.value_optimization_agent
    • +
  • rl_coach.agents.wolpertinger_agent
  • rl_coach.architectures.architecture
  • rl_coach.architectures.network_wrapper
  • rl_coach.base_parameters
    • diff --git a/docs/_modules/rl_coach/agents/agent.html b/docs/_modules/rl_coach/agents/agent.html index 49d4a8a71..0f566aa18 100644 --- a/docs/_modules/rl_coach/agents/agent.html +++ b/docs/_modules/rl_coach/agents/agent.html @@ -756,6 +756,9 @@

    Source code for rl_coach.agents.agent

     
         if self.phase != RunPhase.TEST:
             if isinstance(self.memory, EpisodicExperienceReplay):
+                if self.ap.algorithm.override_episode_rewards_with_the_last_transition_reward:
+                    for t in self.current_episode_buffer.transitions:
+                        t.reward = self.current_episode_buffer.transitions[-1].reward
                 self.call_memory('store_episode', self.current_episode_buffer)
             elif self.ap.algorithm.store_transitions_only_when_episodes_are_terminated:
                 for transition in self.current_episode_buffer.transitions:
@@ -910,7 +913,8 @@ 
    Source code for rl_coach.agents.agent
                     # update counters
                 self.training_iteration += 1
                 if self.pre_network_filter is not None:
-                    batch = self.pre_network_filter.filter(batch, update_internal_state=False, deep_copy=False)
+                    update_internal_state = self.ap.algorithm.update_pre_network_filters_state_on_train
+                    batch = self.pre_network_filter.filter(batch, update_internal_state=update_internal_state, deep_copy=False)
 
                 # if the batch returned empty then there are not enough samples in the replay buffer -> skip
                 # training step
@@ -1020,7 +1024,8 @@ 
    Source code for rl_coach.agents.agent
                     # informed action
                 if self.pre_network_filter is not None:
                     # before choosing an action, first use the pre_network_filter to filter out the current state
-                    update_filter_internal_state = self.phase is not RunPhase.TEST
+                    update_filter_internal_state = self.ap.algorithm.update_pre_network_filters_state_on_inference and \
+                                                   self.phase is not RunPhase.TEST
                     curr_state = self.run_pre_network_filter_for_inference(self.curr_state, update_filter_internal_state)
 
                 else:
@@ -1048,6 +1053,10 @@ 
    Source code for rl_coach.agents.agent
             :return: The filtered state
         """
         dummy_env_response = EnvResponse(next_state=state, reward=0, game_over=False)
+
+        # TODO actually we only want to run the observation filters. No point in running the reward filters as the
+        #  filtered reward is being ignored anyway (and it might unncecessarily affect the reward filters' internal
+        #  state).
         return self.pre_network_filter.filter(dummy_env_response,
                                               update_internal_state=update_filter_internal_state)[0].next_state
    
 
@@ -1177,7 +1186,7 @@ 
    Source code for rl_coach.agents.agent
             """
         Allows setting a directive for the agent to follow. This is useful in hierarchy structures, where the agent
         has another master agent that is controlling it. In such cases, the master agent can define the goals for the
-        slave agent, define it's observation, possible actions, etc. The directive type is defined by the agent
+        slave agent, define its observation, possible actions, etc. The directive type is defined by the agent
         in-action-space.
 
         :param action: The action that should be set as the directive
diff --git a/docs/_modules/rl_coach/agents/clipped_ppo_agent.html b/docs/_modules/rl_coach/agents/clipped_ppo_agent.html
index 9a3f3b14d..ba808c25d 100644
--- a/docs/_modules/rl_coach/agents/clipped_ppo_agent.html
+++ b/docs/_modules/rl_coach/agents/clipped_ppo_agent.html
@@ -295,7 +295,9 @@ 
    Source code for rl_coach.agents.clipped_ppo_agent
    self.optimization_epochs = 10
         self.normalization_stats = None
         self.clipping_decay_schedule = ConstantSchedule(1)
-        self.act_for_full_episodes = True
    
+        self.act_for_full_episodes = True
+        self.update_pre_network_filters_state_on_train = True
+        self.update_pre_network_filters_state_on_inference = False
    
 
 
 class ClippedPPOAgentParameters(AgentParameters):
@@ -486,7 +488,9 @@ 
    Source code for rl_coach.agents.clipped_ppo_agent
    network.set_is_training(True)
 
             dataset = self.memory.transitions
-            dataset = self.pre_network_filter.filter(dataset, deep_copy=False)
+            update_internal_state = self.ap.algorithm.update_pre_network_filters_state_on_train
+            dataset = self.pre_network_filter.filter(dataset, deep_copy=False,
+                                                     update_internal_state=update_internal_state)
             batch = Batch(dataset)
 
             for training_step in range(self.ap.algorithm.num_consecutive_training_steps):
@@ -512,7 +516,9 @@ 
    Source code for rl_coach.agents.clipped_ppo_agent
    def run_pre_network_filter_for_inference(self, state: StateType, update_internal_state: bool=False):
         dummy_env_response = EnvResponse(next_state=state, reward=0, game_over=False)
-        return self.pre_network_filter.filter(dummy_env_response, update_internal_state=False)[0].next_state
+        update_internal_state = self.ap.algorithm.update_pre_network_filters_state_on_inference
+        return self.pre_network_filter.filter(
+            dummy_env_response, update_internal_state=update_internal_state)[0].next_state
 
     def choose_action(self, curr_state):
         self.ap.algorithm.clipping_decay_schedule.step()
diff --git a/docs/_modules/rl_coach/agents/wolpertinger_agent.html b/docs/_modules/rl_coach/agents/wolpertinger_agent.html
new file mode 100644
index 000000000..67dd6cdef
--- /dev/null
+++ b/docs/_modules/rl_coach/agents/wolpertinger_agent.html
@@ -0,0 +1,356 @@
+
+
+
+
+  
+
+  
+  
+  
+  
+  rl_coach.agents.wolpertinger_agent — Reinforcement Learning Coach 0.12.0 documentation
+  
+
+  
+  
+  
+  
+
+  
+  
+  
+    
+      
+        
+        
+        
+        
+        
+    
+    
+
+    
+
+  
+  
+  
+  
+    
+    
+    
+
+
+
+
+
+   
+  
    
+    
+    
+
+    
    
+
+      
+      
+
+
+      
    
+        
+        
    
+        
+          
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
    
+
+  
      
+    
+      
    • Docs »
      • 
+        
+          
    • Module code »
      • 
+        
+      
    • rl_coach.agents.wolpertinger_agent
      • 
+    
+    
+      
    • 
+        
+      
      • 
+    
+  
    
+
+  
+  
    
+
    
+          
    
+           
    
+            
+  
    Source code for rl_coach.agents.wolpertinger_agent
    +#
+# Copyright (c) 2019 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.
+#
+
+import copy
+from typing import Union
+from collections import OrderedDict
+import numpy as np
+
+from rl_coach.agents.ddpg_agent import DDPGAlgorithmParameters, DDPGActorNetworkParameters, \
+    DDPGCriticNetworkParameters, DDPGAgent
+from rl_coach.base_parameters import AgentParameters
+from rl_coach.core_types import ActionInfo
+from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
+from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
+from rl_coach.memories.non_episodic.differentiable_neural_dictionary import AnnoyDictionary
+from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace
+from rl_coach.architectures.head_parameters import WolpertingerActorHeadParameters
+
+
+class WolpertingerCriticNetworkParameters(DDPGCriticNetworkParameters):
+    def __init__(self, use_batchnorm=False):
+        super().__init__(use_batchnorm=use_batchnorm)
+
+
+class WolpertingerActorNetworkParameters(DDPGActorNetworkParameters):
+    def __init__(self, use_batchnorm=False):
+        super().__init__()
+        self.heads_parameters = [WolpertingerActorHeadParameters(batchnorm=use_batchnorm)]
+
+
+
    [docs]class WolpertingerAlgorithmParameters(DDPGAlgorithmParameters):
+    def __init__(self):
+        super().__init__()
+        self.action_embedding_width = 1
+        self.k = 1
    
+        
+
+class WolpertingerAgentParameters(AgentParameters):
+    def __init__(self, use_batchnorm=False):
+        exploration_params = AdditiveNoiseParameters()
+        exploration_params.noise_as_percentage_from_action_space = False
+
+        super().__init__(algorithm=WolpertingerAlgorithmParameters(),
+                         exploration=exploration_params,
+                         memory=EpisodicExperienceReplayParameters(),
+                         networks=OrderedDict(
+                             [("actor", WolpertingerActorNetworkParameters(use_batchnorm=use_batchnorm)),
+                              ("critic", WolpertingerCriticNetworkParameters(use_batchnorm=use_batchnorm))]))
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.wolpertinger_agent:WolpertingerAgent'
+
+
+# Deep Reinforcement Learning in Large Discrete Action Spaces - https://arxiv.org/pdf/1512.07679.pdf
+class WolpertingerAgent(DDPGAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent'] = None):
+        super().__init__(agent_parameters, parent)
+
+    def learn_from_batch(self, batch):
+        # replay buffer holds the actions in the discrete manner, as the agent is expected to act with discrete actions
+        # with the BoxDiscretization output filter. But DDPG needs to work on continuous actions, thus converting to
+        # continuous actions. This is actually a duplicate since this filtering is also done before applying actions on
+        # the environment. So might want to somehow reuse that conversion. Maybe can hold this information in the info
+        # dictionary of the transition.
+
+        output_action_filter = \
+            list(self.output_filter.action_filters.values())[0]
+        continuous_actions = []
+        for action in batch.actions():
+            continuous_actions.append(output_action_filter.filter(action))
+        batch._actions = np.array(continuous_actions).squeeze()
+
+        return super().learn_from_batch(batch)
+
+    def train(self):
+        return super().train()
+
+    def choose_action(self, curr_state):
+        if not isinstance(self.spaces.action, DiscreteActionSpace):
+            raise ValueError("WolpertingerAgent works only for discrete control problems")
+
+        # convert to batch so we can run it through the network
+        tf_input_state = self.prepare_batch_for_inference(curr_state, 'actor')
+        actor_network = self.networks['actor'].online_network
+        critic_network = self.networks['critic'].online_network
+        proto_action = actor_network.predict(tf_input_state)
+        proto_action = np.expand_dims(self.exploration_policy.get_action(proto_action), 0)
+
+        nn_action_embeddings, indices, _, _ = self.knn_tree.query(keys=proto_action, k=self.ap.algorithm.k)
+
+        # now move the actions through the critic and choose the one with the highest q value
+        critic_inputs = copy.copy(tf_input_state)
+        critic_inputs['observation'] = np.tile(critic_inputs['observation'], (self.ap.algorithm.k, 1))
+        critic_inputs['action'] = nn_action_embeddings[0]
+        q_values = critic_network.predict(critic_inputs)[0]
+        action = int(indices[0][np.argmax(q_values)])
+        self.action_signal.add_sample(action)
+        return ActionInfo(action=action, action_value=0)
+
+    def init_environment_dependent_modules(self):
+        super().init_environment_dependent_modules()
+        self.knn_tree = self.get_initialized_knn()
+
+    # TODO - ideally the knn should not be defined here, but somehow be defined by the user in the preset
+    def get_initialized_knn(self):
+        num_actions = len(self.spaces.action.actions)
+        action_max_abs_range = self.spaces.action.filtered_action_space.max_abs_range if \
+            (hasattr(self.spaces.action, 'filtered_action_space') and
+             isinstance(self.spaces.action.filtered_action_space, BoxActionSpace)) \
+            else 1.0
+        keys = np.expand_dims((np.arange(num_actions) / (num_actions - 1) - 0.5) * 2, 1) * action_max_abs_range
+        values = np.expand_dims(np.arange(num_actions), 1)
+        knn_tree = AnnoyDictionary(dict_size=num_actions, key_width=self.ap.algorithm.action_embedding_width)
+        knn_tree.add(keys, values, force_rebuild_tree=True)
+
+        return knn_tree
+
+
    
+
+           
    
+           
+          
    
+          
    
+  
+
+  
    
+
+  
    
+    
    
+        © Copyright 2018-2019, Intel AI Lab
+
+    
    
+  
    
+  Built with Sphinx using a theme provided by Read the Docs. 
+
+
    
+
+        
    
+      
    
+
+    
    
+
+  
    
+  
+
+
+  
+
+  
+  
+    
+   
+
+
+
\ No newline at end of file
diff --git a/docs/_modules/rl_coach/base_parameters.html b/docs/_modules/rl_coach/base_parameters.html
index 60aac7f0a..045363d11 100644
--- a/docs/_modules/rl_coach/base_parameters.html
+++ b/docs/_modules/rl_coach/base_parameters.html
@@ -396,6 +396,14 @@ 
    Source code for rl_coach.base_parameters
             # Support for parameter noise
         self.supports_parameter_noise = False
 
+        # Override, in retrospective, all the episode rewards with the last reward in the episode
+        # (sometimes useful for sparse, end of the episode, rewards problems)
+        self.override_episode_rewards_with_the_last_transition_reward = False
+
+        # Filters - TODO consider creating a FilterParameters class and initialize the filters with it
+        self.update_pre_network_filters_state_on_train = False
+        self.update_pre_network_filters_state_on_inference = True
+        
 
 
    [docs]class PresetValidationParameters(Parameters):
     def __init__(self,
diff --git a/docs/_modules/rl_coach/core_types.html b/docs/_modules/rl_coach/core_types.html
index 78d96603f..abf47114b 100644
--- a/docs/_modules/rl_coach/core_types.html
+++ b/docs/_modules/rl_coach/core_types.html
@@ -298,6 +298,12 @@ 
    Source code for rl_coach.core_types
         def __init__(self, num_steps):
         super().__init__(num_steps)
 
+    def __truediv__(self, other):
+        if isinstance(other, EnvironmentSteps):
+            return math.ceil(self.num_steps / other.num_steps)
+        else:
+            super().__truediv__(self, other)
+
 
 class Time(StepMethod):
     def __init__(self, num_steps):
diff --git a/docs/_modules/rl_coach/data_stores/nfs_data_store.html b/docs/_modules/rl_coach/data_stores/nfs_data_store.html
index a67fe7a24..25785f7ae 100644
--- a/docs/_modules/rl_coach/data_stores/nfs_data_store.html
+++ b/docs/_modules/rl_coach/data_stores/nfs_data_store.html
@@ -200,15 +200,17 @@ 
    Source code for rl_coach.data_stores.nfs_data_store
    import uuid
 
-from rl_coach.data_stores.data_store import DataStore, DataStoreParameters
+from rl_coach.data_stores.data_store import DataStoreParameters
+from rl_coach.data_stores.checkpoint_data_store import CheckpointDataStore
 
 
 class NFSDataStoreParameters(DataStoreParameters):
-    def __init__(self, ds_params, deployed=False, server=None, path=None):
+    def __init__(self, ds_params, deployed=False, server=None, path=None, checkpoint_dir: str=""):
         super().__init__(ds_params.store_type, ds_params.orchestrator_type, ds_params.orchestrator_params)
         self.namespace = "default"
         if "namespace" in ds_params.orchestrator_params:
             self.namespace = ds_params.orchestrator_params["namespace"]
+        self.checkpoint_dir = checkpoint_dir
         self.name = None
         self.pvc_name = None
         self.pv_name = None
@@ -221,7 +223,7 @@ 
    Source code for rl_coach.data_stores.nfs_data_store
    self.path = path
 
 
-
    [docs]class NFSDataStore(DataStore):
+
    [docs]class NFSDataStore(CheckpointDataStore):
     """
     An implementation of data store which uses NFS for storing policy checkpoints when using Coach in distributed mode.
     The policy checkpoints are written by the trainer and read by the rollout worker.
diff --git a/docs/_modules/rl_coach/data_stores/s3_data_store.html b/docs/_modules/rl_coach/data_stores/s3_data_store.html
index 64fc0b96d..c2e4b9bba 100644
--- a/docs/_modules/rl_coach/data_stores/s3_data_store.html
+++ b/docs/_modules/rl_coach/data_stores/s3_data_store.html
@@ -198,7 +198,8 @@ 
    Source code for rl_coach.data_stores.s3_data_store
    #
 
 
-from rl_coach.data_stores.data_store import DataStore, DataStoreParameters
+from rl_coach.data_stores.data_store import DataStoreParameters
+from rl_coach.data_stores.checkpoint_data_store import CheckpointDataStore
 from minio import Minio
 from minio.error import ResponseError
 from configparser import ConfigParser, Error
@@ -222,7 +223,7 @@ 
    Source code for rl_coach.data_stores.s3_data_store
    self.expt_dir = expt_dir
 
 
-
    [docs]class S3DataStore(DataStore):
+
    [docs]class S3DataStore(CheckpointDataStore):
     """
     An implementation of the data store using S3 for storing policy checkpoints when using Coach in distributed mode.
     The policy checkpoints are written by the trainer and read by the rollout worker.
diff --git a/docs/_modules/rl_coach/exploration_policies/additive_noise.html b/docs/_modules/rl_coach/exploration_policies/additive_noise.html
index 44e2dc344..92eb352af 100644
--- a/docs/_modules/rl_coach/exploration_policies/additive_noise.html
+++ b/docs/_modules/rl_coach/exploration_policies/additive_noise.html
@@ -245,7 +245,9 @@ 
    Source code for rl_coach.exploration_policies.additive_noise
    self.evaluation_noise = evaluation_noise
         self.noise_as_percentage_from_action_space = noise_as_percentage_from_action_space
 
-        if not isinstance(action_space, BoxActionSpace):
+        if not isinstance(action_space, BoxActionSpace) and \
+                (hasattr(action_space, 'filtered_action_space') and not
+                 isinstance(action_space.filtered_action_space, BoxActionSpace)):
             raise ValueError("Additive noise exploration works only for continuous controls."
                              "The given action space is of type: {}".format(action_space.__class__.__name__))
 
diff --git a/docs/_modules/rl_coach/exploration_policies/exploration_policy.html b/docs/_modules/rl_coach/exploration_policies/exploration_policy.html
index f5ffd7d6c..faa458365 100644
--- a/docs/_modules/rl_coach/exploration_policies/exploration_policy.html
+++ b/docs/_modules/rl_coach/exploration_policies/exploration_policy.html
@@ -298,7 +298,10 @@ 
    Source code for rl_coach.exploration_policies.exploration_policy
    """
         :param action_space: the action space used by the environment
         """
-        assert isinstance(action_space, BoxActionSpace) or isinstance(action_space, GoalsSpace)
+        assert isinstance(action_space, BoxActionSpace) or \
+               (hasattr(action_space, 'filtered_action_space') and
+                 isinstance(action_space.filtered_action_space, BoxActionSpace)) or \
+               isinstance(action_space, GoalsSpace)
         super().__init__(action_space)
 
    
 
diff --git a/docs/_modules/rl_coach/exploration_policies/truncated_normal.html b/docs/_modules/rl_coach/exploration_policies/truncated_normal.html
index 7d331988b..56d84e766 100644
--- a/docs/_modules/rl_coach/exploration_policies/truncated_normal.html
+++ b/docs/_modules/rl_coach/exploration_policies/truncated_normal.html
@@ -271,9 +271,6 @@ 
    Source code for rl_coach.exploration_policies.truncated_normal
    else:
             action_values_std = current_noise
 
-        # scale the noise to the action space range
-        action_values_std = current_noise * (self.action_space.high - self.action_space.low)
-
         # extract the mean values
         if isinstance(action_values, list):
             # the action values are expected to be a list with the action mean and optionally the action stdev
diff --git a/docs/_modules/rl_coach/filters/action/partial_discrete_action_space_map.html b/docs/_modules/rl_coach/filters/action/partial_discrete_action_space_map.html
index 0fae9ce5c..4e4837b56 100644
--- a/docs/_modules/rl_coach/filters/action/partial_discrete_action_space_map.html
+++ b/docs/_modules/rl_coach/filters/action/partial_discrete_action_space_map.html
@@ -231,7 +231,8 @@ 
    Source code for rl_coach.filters.action.partial_discrete_action_space_mapdef get_unfiltered_action_space(self, output_action_space: ActionSpace) -> DiscreteActionSpace:
         self.output_action_space = output_action_space
-        self.input_action_space = DiscreteActionSpace(len(self.target_actions), self.descriptions)
+        self.input_action_space = DiscreteActionSpace(len(self.target_actions), self.descriptions,
+                                                      filtered_action_space=output_action_space)
         return self.input_action_space
 
     def filter(self, action: ActionType) -> ActionType:
diff --git a/docs/_modules/rl_coach/memories/backend/redis.html b/docs/_modules/rl_coach/memories/backend/redis.html
index c904f4dd3..7567160f6 100644
--- a/docs/_modules/rl_coach/memories/backend/redis.html
+++ b/docs/_modules/rl_coach/memories/backend/redis.html
@@ -261,11 +261,18 @@ 
    Source code for rl_coach.memories.backend.redis
    <
         """
         if 'namespace' not in self.params.orchestrator_params:
             self.params.orchestrator_params['namespace'] = "default"
-        from kubernetes import client
+        from kubernetes import client, config
 
         container = client.V1Container(
             name=self.redis_server_name,
             image='redis:4-alpine',
+            resources=client.V1ResourceRequirements(
+                limits={
+                    "cpu": "8",
+                    "memory": "4Gi"
+                    # "nvidia.com/gpu": "0",
+                }
+            ),
         )
         template = client.V1PodTemplateSpec(
             metadata=client.V1ObjectMeta(labels={'app': self.redis_server_name}),
@@ -288,8 +295,10 @@ 
    Source code for rl_coach.memories.backend.redis
    <
             spec=deployment_spec
         )
 
+        config.load_kube_config()
         api_client = client.AppsV1Api()
         try:
+            print(self.params.orchestrator_params)
             api_client.create_namespaced_deployment(self.params.orchestrator_params['namespace'], deployment)
         except client.rest.ApiException as e:
             print("Got exception: %s\n while creating redis-server", e)
diff --git a/docs/_modules/rl_coach/memories/non_episodic/differentiable_neural_dictionary.html b/docs/_modules/rl_coach/memories/non_episodic/differentiable_neural_dictionary.html
index 2a5d1f9d1..063e0e480 100644
--- a/docs/_modules/rl_coach/memories/non_episodic/differentiable_neural_dictionary.html
+++ b/docs/_modules/rl_coach/memories/non_episodic/differentiable_neural_dictionary.html
@@ -240,7 +240,7 @@ 
    Source code for rl_coach.memories.non_episodic.differentiable_neural_diction
 
         self.built_capacity = 0
 
-    def add(self, keys, values, additional_data=None):
+    def add(self, keys, values, additional_data=None, force_rebuild_tree=False):
         if not additional_data:
             additional_data = [None] * len(keys)
 
@@ -279,7 +279,7 @@ 
    Source code for rl_coach.memories.non_episodic.differentiable_neural_diction
         if len(self.buffered_indices) >= self.min_update_size:
             self.min_update_size = max(self.initial_update_size, int(self.curr_size * 0.02))
             self._rebuild_index()
-        elif self.rebuild_on_every_update:
+        elif force_rebuild_tree or self.rebuild_on_every_update:
             self._rebuild_index()
 
         self.current_timestamp += 1
diff --git a/docs/_modules/rl_coach/orchestrators/kubernetes_orchestrator.html b/docs/_modules/rl_coach/orchestrators/kubernetes_orchestrator.html
index b8c99db50..71d144feb 100644
--- a/docs/_modules/rl_coach/orchestrators/kubernetes_orchestrator.html
+++ b/docs/_modules/rl_coach/orchestrators/kubernetes_orchestrator.html
@@ -307,6 +307,11 @@ 
    Source code for rl_coach.orchestrators.kubernetes_orchestrator
            """
 
         self.memory_backend.deploy()
+
+        if self.params.data_store_params.store_type == "redis":
+            self.data_store.params.redis_address = self.memory_backend.params.redis_address
+            self.data_store.params.redis_port = self.memory_backend.params.redis_port
+
         if not self.data_store.deploy():
             return False
         if self.params.data_store_params.store_type == "nfs":
@@ -329,6 +334,8 @@ 
    Source code for rl_coach.orchestrators.kubernetes_orchestrator
    trainer_params.command += ['--data_store_params', json.dumps(self.params.data_store_params.__dict__)]
         name = "{}-{}".format(trainer_params.run_type, uuid.uuid4())
 
+        # TODO: instead of defining each container and template spec from scratch, loaded default
+        # configuration and modify them as necessary depending on the store type
         if self.params.data_store_params.store_type == "nfs":
             container = k8sclient.V1Container(
                 name=name,
@@ -354,7 +361,7 @@ 
    Source code for rl_coach.orchestrators.kubernetes_orchestrator
    restart_policy='Never'
                 ),
             )
-        else:
+        elif self.params.data_store_params.store_type == "s3":
             container = k8sclient.V1Container(
                 name=name,
                 image=trainer_params.image,
@@ -373,6 +380,34 @@ 
    Source code for rl_coach.orchestrators.kubernetes_orchestrator
    restart_policy='Never'
                 ),
             )
+        elif self.params.data_store_params.store_type == "redis":
+            container = k8sclient.V1Container(
+                name=name,
+                image=trainer_params.image,
+                command=trainer_params.command,
+                args=trainer_params.arguments,
+                image_pull_policy='Always',
+                stdin=True,
+                tty=True,
+                resources=k8sclient.V1ResourceRequirements(
+                    limits={
+                        "cpu": "40",
+                        "memory": "4Gi",
+                        "nvidia.com/gpu": "1",
+                    }
+                ),
+            )
+            template = k8sclient.V1PodTemplateSpec(
+                metadata=k8sclient.V1ObjectMeta(labels={'app': name}),
+                spec=k8sclient.V1PodSpec(
+                    containers=[container],
+                    restart_policy='Never'
+                ),
+            )
+        else:
+            raise ValueError("unexpected store_type {}. expected 's3', 'nfs', 'redis'".format(
+                self.params.data_store_params.store_type
+            ))
 
         job_spec = k8sclient.V1JobSpec(
             completions=1,
@@ -404,12 +439,17 @@ 
    Source code for rl_coach.orchestrators.kubernetes_orchestrator
    if not worker_params:
             return False
 
+        # At this point, the memory backend and data store have been deployed and in the process,
+        # these parameters have been updated to include things like the hostname and port the
+        # service can be found at.
         worker_params.command += ['--memory_backend_params', json.dumps(self.params.memory_backend_parameters.__dict__)]
         worker_params.command += ['--data_store_params', json.dumps(self.params.data_store_params.__dict__)]
         worker_params.command += ['--num_workers', '{}'.format(worker_params.num_replicas)]
 
         name = "{}-{}".format(worker_params.run_type, uuid.uuid4())
 
+        # TODO: instead of defining each container and template spec from scratch, loaded default
+        # configuration and modify them as necessary depending on the store type
         if self.params.data_store_params.store_type == "nfs":
             container = k8sclient.V1Container(
                 name=name,
@@ -435,7 +475,7 @@ 
    Source code for rl_coach.orchestrators.kubernetes_orchestrator
    restart_policy='Never'
                 ),
             )
-        else:
+        elif self.params.data_store_params.store_type == "s3":
             container = k8sclient.V1Container(
                 name=name,
                 image=worker_params.image,
@@ -454,6 +494,32 @@ 
    Source code for rl_coach.orchestrators.kubernetes_orchestrator
    restart_policy='Never'
                 )
             )
+        elif self.params.data_store_params.store_type == "redis":
+            container = k8sclient.V1Container(
+                name=name,
+                image=worker_params.image,
+                command=worker_params.command,
+                args=worker_params.arguments,
+                image_pull_policy='Always',
+                stdin=True,
+                tty=True,
+                resources=k8sclient.V1ResourceRequirements(
+                    limits={
+                        "cpu": "8",
+                        "memory": "4Gi",
+                        # "nvidia.com/gpu": "0",
+                    }
+                ),
+            )
+            template = k8sclient.V1PodTemplateSpec(
+                metadata=k8sclient.V1ObjectMeta(labels={'app': name}),
+                spec=k8sclient.V1PodSpec(
+                    containers=[container],
+                    restart_policy='Never'
+                )
+            )
+        else:
+            raise ValueError('unexpected store type {}'.format(self.params.data_store_params.store_type))
 
         job_spec = k8sclient.V1JobSpec(
             completions=worker_params.num_replicas,
diff --git a/docs/_modules/rl_coach/spaces.html b/docs/_modules/rl_coach/spaces.html
index 2e890d9af..30472c33d 100644
--- a/docs/_modules/rl_coach/spaces.html
+++ b/docs/_modules/rl_coach/spaces.html
@@ -568,7 +568,8 @@ 
    Source code for rl_coach.spaces
         """
     A discrete action space with action indices as actions
     """
-    def __init__(self, num_actions: int, descriptions: Union[None, List, Dict]=None, default_action: np.ndarray=None):
+    def __init__(self, num_actions: int, descriptions: Union[None, List, Dict]=None, default_action: np.ndarray=None,
+                 filtered_action_space=None):
         super().__init__(1, low=0, high=num_actions-1, descriptions=descriptions)
         # the number of actions is mapped to high
 
@@ -578,6 +579,9 @@ 
    Source code for rl_coach.spaces
             else:
             self.default_action = default_action
 
+        if filtered_action_space is not None:
+            self.filtered_action_space = filtered_action_space
+
     @property
     def actions(self) -> List[ActionType]:
         return list(range(0, int(self.high[0]) + 1))
diff --git a/docs/_sources/components/agents/index.rst.txt b/docs/_sources/components/agents/index.rst.txt
index ca21713b4..c958768bf 100644
--- a/docs/_sources/components/agents/index.rst.txt
+++ b/docs/_sources/components/agents/index.rst.txt
@@ -21,8 +21,6 @@ A detailed description of those algorithms can be found by navigating to each of
    imitation/cil
    policy_optimization/cppo
    policy_optimization/ddpg
-   policy_optimization/td3
-   policy_optimization/sac
    other/dfp
    value_optimization/double_dqn
    value_optimization/dqn
@@ -36,6 +34,10 @@ A detailed description of those algorithms can be found by navigating to each of
    policy_optimization/ppo
    value_optimization/rainbow
    value_optimization/qr_dqn
+   policy_optimization/sac
+   policy_optimization/td3
+   policy_optimization/wolpertinger
+
 
 
 .. autoclass:: rl_coach.base_parameters.AgentParameters
diff --git a/docs/_sources/components/agents/policy_optimization/wolpertinger.rst.txt b/docs/_sources/components/agents/policy_optimization/wolpertinger.rst.txt
new file mode 100644
index 000000000..5aa57d2a8
--- /dev/null
+++ b/docs/_sources/components/agents/policy_optimization/wolpertinger.rst.txt
@@ -0,0 +1,56 @@
+Wolpertinger
+=============
+
+**Actions space:** Discrete
+
+**References:** `Deep Reinforcement Learning in Large Discrete Action Spaces `_
+
+Network Structure
+-----------------
+
+.. image:: /_static/img/design_imgs/wolpertinger.png
+   :align: center
+
+Algorithm Description
+---------------------
+Choosing an action
+++++++++++++++++++
+
+Pass the current states through the actor network, and get a proto action :math:`\mu`.
+While in training phase, use a continuous exploration policy, such as the a gaussian noise,
+to add exploration noise to the proto action. Then, pass the proto action to a k-NN tree to find actual valid
+action candidates, which are in the surrounding neighborhood of the proto action. Those actions are then passed to the
+critic to evaluate their goodness, and eventually the discrete index of the action with the highest Q value is chosen.
+When testing, the same flow is used, but no exploration noise is added.
+
+Training the network
+++++++++++++++++++++
+
+Training the network is exactly the same as in DDPG. Unlike when choosing the action, the proto action is not passed
+through the k-NN tree. It is being passed directly to the critic.
+
+Start by sampling a batch of transitions from the experience replay.
+
+* To train the **critic network**, use the following targets:
+
+  :math:`y_t=r(s_t,a_t )+\gamma \cdot Q(s_{t+1},\mu(s_{t+1} ))`
+
+  First run the actor target network, using the next states as the inputs, and get :math:`\mu (s_{t+1} )`.
+  Next, run the critic target network using the next states and :math:`\mu (s_{t+1} )`, and use the output to
+  calculate :math:`y_t` according to the equation above. To train the network, use the current states and actions
+  as the inputs, and :math:`y_t` as the targets.
+
+* To train the **actor network**, use the following equation:
+
+  :math:`\nabla_{\theta^\mu } J \approx E_{s_t \tilde{} \rho^\beta } [\nabla_a Q(s,a)|_{s=s_t,a=\mu (s_t ) } \cdot \nabla_{\theta^\mu} \mu(s)|_{s=s_t} ]`
+
+  Use the actor's online network to get the action mean values using the current states as the inputs.
+  Then, use the critic online network in order to get the gradients of the critic output with respect to the
+  action mean values :math:`\nabla _a Q(s,a)|_{s=s_t,a=\mu(s_t ) }`.
+  Using the chain rule, calculate the gradients of the actor's output, with respect to the actor weights,
+  given :math:`\nabla_a Q(s,a)`. Finally, apply those gradients to the actor network.
+
+After every training step, do a soft update of the critic and actor target networks' weights from the online networks.
+
+
+.. autoclass:: rl_coach.agents.wolpertinger_agent.WolpertingerAlgorithmParameters
\ No newline at end of file
diff --git a/docs/components/agents/index.html b/docs/components/agents/index.html
index 357caadf7..7c90e3f78 100644
--- a/docs/components/agents/index.html
+++ b/docs/components/agents/index.html
@@ -117,8 +117,6 @@
 
  • Conditional Imitation Learning
    • 
 
  • Clipped Proximal Policy Optimization
    • 
 
  • Deep Deterministic Policy Gradient
    • 
-
  • Twin Delayed Deep Deterministic Policy Gradient
    • 
-
  • Soft Actor-Critic
    • 
 
  • Direct Future Prediction
    • 
 
  • Double DQN
    • 
 
  • Deep Q Networks
    • 
@@ -132,6 +130,9 @@
 
  • Proximal Policy Optimization
    • 
 
  • Rainbow
    • 
 
  • Quantile Regression DQN
    • 
+
  • Soft Actor-Critic
    • 
+
  • Twin Delayed Deep Deterministic Policy Gradient
    • 
+
  • Wolpertinger
    • 
 
 
 
  • Architectures
    • 
@@ -226,8 +227,6 @@ 
    AgentsConditional Imitation Learning
 
  • Clipped Proximal Policy Optimization
    • 
 
  • Deep Deterministic Policy Gradient
    • 
-
  • Twin Delayed Deep Deterministic Policy Gradient
    • 
-
  • Soft Actor-Critic
    • 
 
  • Direct Future Prediction
    • 
 
  • Double DQN
    • 
 
  • Deep Q Networks
    • 
@@ -241,6 +240,9 @@ 
    AgentsProximal Policy Optimization
 
  • Rainbow
    • 
 
  • Quantile Regression DQN
    • 
+
  • Soft Actor-Critic
    • 
+
  • Twin Delayed Deep Deterministic Policy Gradient
    • 
+
  • Wolpertinger
    • 
 
 
    
 
    
@@ -512,7 +514,7 @@ 
    Agents
 
    
-prepare_batch_for_inference(states: Union[Dict[str, numpy.ndarray], List[Dict[str, numpy.ndarray]]], network_name: str) → Dict[str, numpy.array][source]¶
    
+prepare_batch_for_inference(states: Union[Dict[str, numpy.ndarray], List[Dict[str, numpy.ndarray]]], network_name: str) → Dict[str, numpy.core.multiarray.array][source]¶
 
    Convert curr_state into input tensors tensorflow is expecting. i.e. if we have several inputs states, stack all
 observations together, measurements together, etc.
    
 
    
@@ -652,7 +654,7 @@ 
    Agentsset_incoming_directive(action: Union[int, float, numpy.ndarray, List]) → None[source]¶
 
    Allows setting a directive for the agent to follow. This is useful in hierarchy structures, where the agent
 has another master agent that is controlling it. In such cases, the master agent can define the goals for the
-slave agent, define it’s observation, possible actions, etc. The directive type is defined by the agent
+slave agent, define its observation, possible actions, etc. The directive type is defined by the agent
 in-action-space.
    
 
    
 
    Parameters
    
diff --git a/docs/components/agents/policy_optimization/wolpertinger.html b/docs/components/agents/policy_optimization/wolpertinger.html
new file mode 100644
index 000000000..cda2378bb
--- /dev/null
+++ b/docs/components/agents/policy_optimization/wolpertinger.html
@@ -0,0 +1,276 @@
+
+
+
+
+  
+
+  
+  
+  
+  
+  Wolpertinger — Reinforcement Learning Coach 0.12.0 documentation
+  
+
+  
+  
+  
+  
+
+  
+  
+  
+    
+      
+        
+        
+        
+        
+        
+    
+    
+
+    
+
+  
+  
+  
+  
+    
+    
+    
+
+
+
+
+
+   
+  
    
+    
+    
+
+    
    
+
+      
+      
+
+
+      
    
+        
+        
    
+        
+          
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
    
+
+  
+
+  
+  
    
+
    
+          
    
+           
    
+            
+  
    
+
    Wolpertinger¶
    
+
    Actions space: Discrete
    
+
    References: Deep Reinforcement Learning in Large Discrete Action Spaces
    
+
    
+
    Network Structure¶
    
+../../../_images/wolpertinger.png
+
    
+
    
+
    Algorithm Description¶
    
+
    
+
    Choosing an action¶
    
+
    Pass the current states through the actor network, and get a proto action \(\mu\).
+While in training phase, use a continuous exploration policy, such as the a gaussian noise,
+to add exploration noise to the proto action. Then, pass the proto action to a k-NN tree to find actual valid
+action candidates, which are in the surrounding neighborhood of the proto action. Those actions are then passed to the
+critic to evaluate their goodness, and eventually the discrete index of the action with the highest Q value is chosen.
+When testing, the same flow is used, but no exploration noise is added.
    
+
    
+
    
+
    Training the network¶
    
+
    Training the network is exactly the same as in DDPG. Unlike when choosing the action, the proto action is not passed
+through the k-NN tree. It is being passed directly to the critic.
    
+
    Start by sampling a batch of transitions from the experience replay.
    
+
      
+
    • To train the critic network, use the following targets:
      
+
      \(y_t=r(s_t,a_t )+\gamma \cdot Q(s_{t+1},\mu(s_{t+1} ))\)
      
+
      First run the actor target network, using the next states as the inputs, and get \(\mu (s_{t+1} )\).
+Next, run the critic target network using the next states and \(\mu (s_{t+1} )\), and use the output to
+calculate \(y_t\) according to the equation above. To train the network, use the current states and actions
+as the inputs, and \(y_t\) as the targets.
      
+
      • 
+
    • To train the actor network, use the following equation:
      
+
      \(\nabla_{\theta^\mu } J \approx E_{s_t \tilde{} \rho^\beta } [\nabla_a Q(s,a)|_{s=s_t,a=\mu (s_t ) } \cdot \nabla_{\theta^\mu} \mu(s)|_{s=s_t} ]\)
      
+
      Use the actor’s online network to get the action mean values using the current states as the inputs.
+Then, use the critic online network in order to get the gradients of the critic output with respect to the
+action mean values \(\nabla _a Q(s,a)|_{s=s_t,a=\mu(s_t ) }\).
+Using the chain rule, calculate the gradients of the actor’s output, with respect to the actor weights,
+given \(\nabla_a Q(s,a)\). Finally, apply those gradients to the actor network.
      
+
      • 
+
    
+
    After every training step, do a soft update of the critic and actor target networks’ weights from the online networks.
    
+
    
+
    
+class rl_coach.agents.wolpertinger_agent.WolpertingerAlgorithmParameters[source]¶
    
+
    
+
+
    
+
    
+
    
+
+
+           
    
+           
+          
    
+          
    
+  
+
+  
    
+
+  
    
+    
    
+        © Copyright 2018-2019, Intel AI Lab
+
+    
    
+  
    
+  Built with Sphinx using a theme provided by Read the Docs. 
+
+
    
+
+        
    
+      
    
+
+    
    
+
+  
    
+  
+
+
+  
+
+  
+  
+    
+   
+
+
+
\ No newline at end of file
diff --git a/docs/components/spaces.html b/docs/components/spaces.html
index a62653c56..753e3342a 100644
--- a/docs/components/spaces.html
+++ b/docs/components/spaces.html
@@ -442,7 +442,7 @@ 
    BoxActionSpace¶
    
 
    
 
    
-class rl_coach.spaces.DiscreteActionSpace(num_actions: int, descriptions: Union[None, List, Dict] = None, default_action: numpy.ndarray = None)[source]¶
    
+class rl_coach.spaces.DiscreteActionSpace(num_actions: int, descriptions: Union[None, List, Dict] = None, default_action: numpy.ndarray = None, filtered_action_space=None)[source]¶
 
    A discrete action space with action indices as actions
    
 
    
 
diff --git a/docs/features/benchmarks.html b/docs/features/benchmarks.html
index 073faab6e..845d42c1e 100644
--- a/docs/features/benchmarks.html
+++ b/docs/features/benchmarks.html
@@ -37,7 +37,7 @@
   
     
     
-    
+    
     
     
 
@@ -95,7 +95,6 @@
 
  • Algorithms
    • 
 
  • Environments
    • 
 
  • Benchmarks
    • 
-
  • Batch Reinforcement Learning
    • 
 
 
 
  • Selecting an Algorithm
    • 
@@ -221,7 +220,7 @@ 
    Benchmarks
       
-        
+        
       
       
         
diff --git a/docs/features/environments.html b/docs/features/environments.html
index 9d4622c9c..7b3a89a9a 100644
--- a/docs/features/environments.html
+++ b/docs/features/environments.html
@@ -95,7 +95,6 @@
 
  • Algorithms
    • 
 
  • Environments
    • 
 
  • Benchmarks
    • 
-
  • Batch Reinforcement Learning
    • 
 
 
 
  • Selecting an Algorithm
    • 
diff --git a/docs/genindex.html b/docs/genindex.html
index 28d3d71e2..bd8eb34cf 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -206,6 +206,7 @@ 
    Index
    
  | T
  | U
  | V
+ | W
  
 
    
 
    A
    
@@ -956,6 +957,14 @@ 
    V
    
   
 
 
+
    W
    
+
+  
+
    
+
 
 
            
    
diff --git a/docs/objects.inv b/docs/objects.inv
index b2a5dbc17ad41c3036f18db4f4468a41fdf0cace..a793fa39a37b25e1a3293d9b7e4e1b895d20370e 100644
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\ No newline at end of file
diff --git a/docs/test.html b/docs/test.html
index f95618feb..079823669 100644
--- a/docs/test.html
+++ b/docs/test.html
@@ -439,7 +439,7 @@ 
    test¶
 
 
    
 
    
-prepare_batch_for_inference(states: Union[Dict[str, numpy.ndarray], List[Dict[str, numpy.ndarray]]], network_name: str) → Dict[str, numpy.array]¶
    
+prepare_batch_for_inference(states: Union[Dict[str, numpy.ndarray], List[Dict[str, numpy.ndarray]]], network_name: str) → Dict[str, numpy.core.multiarray.array]¶
 
    Convert curr_state into input tensors tensorflow is expecting. i.e. if we have several inputs states, stack all
 observations together, measurements together, etc.
    
 
    
diff --git a/docs_raw/source/_static/img/algorithms.png b/docs_raw/source/_static/img/algorithms.png
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F{0GtY&cXly

literal 0
HcmV?d00001

diff --git a/docs_raw/source/algorithms.xml b/docs_raw/source/algorithms.xml
index e6f68c9e8..377709c38 100644
--- a/docs_raw/source/algorithms.xml
+++ b/docs_raw/source/algorithms.xml
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/docs_raw/source/components/agents/index.rst b/docs_raw/source/components/agents/index.rst
index ca21713b4..c958768bf 100644
--- a/docs_raw/source/components/agents/index.rst
+++ b/docs_raw/source/components/agents/index.rst
@@ -21,8 +21,6 @@ A detailed description of those algorithms can be found by navigating to each of
    imitation/cil
    policy_optimization/cppo
    policy_optimization/ddpg
-   policy_optimization/td3
-   policy_optimization/sac
    other/dfp
    value_optimization/double_dqn
    value_optimization/dqn
@@ -36,6 +34,10 @@ A detailed description of those algorithms can be found by navigating to each of
    policy_optimization/ppo
    value_optimization/rainbow
    value_optimization/qr_dqn
+   policy_optimization/sac
+   policy_optimization/td3
+   policy_optimization/wolpertinger
+
 
 
 .. autoclass:: rl_coach.base_parameters.AgentParameters
diff --git a/docs_raw/source/components/agents/policy_optimization/wolpertinger.rst b/docs_raw/source/components/agents/policy_optimization/wolpertinger.rst
new file mode 100644
index 000000000..5aa57d2a8
--- /dev/null
+++ b/docs_raw/source/components/agents/policy_optimization/wolpertinger.rst
@@ -0,0 +1,56 @@
+Wolpertinger
+=============
+
+**Actions space:** Discrete
+
+**References:** `Deep Reinforcement Learning in Large Discrete Action Spaces `_
+
+Network Structure
+-----------------
+
+.. image:: /_static/img/design_imgs/wolpertinger.png
+   :align: center
+
+Algorithm Description
+---------------------
+Choosing an action
+++++++++++++++++++
+
+Pass the current states through the actor network, and get a proto action :math:`\mu`.
+While in training phase, use a continuous exploration policy, such as the a gaussian noise,
+to add exploration noise to the proto action. Then, pass the proto action to a k-NN tree to find actual valid
+action candidates, which are in the surrounding neighborhood of the proto action. Those actions are then passed to the
+critic to evaluate their goodness, and eventually the discrete index of the action with the highest Q value is chosen.
+When testing, the same flow is used, but no exploration noise is added.
+
+Training the network
+++++++++++++++++++++
+
+Training the network is exactly the same as in DDPG. Unlike when choosing the action, the proto action is not passed
+through the k-NN tree. It is being passed directly to the critic.
+
+Start by sampling a batch of transitions from the experience replay.
+
+* To train the **critic network**, use the following targets:
+
+  :math:`y_t=r(s_t,a_t )+\gamma \cdot Q(s_{t+1},\mu(s_{t+1} ))`
+
+  First run the actor target network, using the next states as the inputs, and get :math:`\mu (s_{t+1} )`.
+  Next, run the critic target network using the next states and :math:`\mu (s_{t+1} )`, and use the output to
+  calculate :math:`y_t` according to the equation above. To train the network, use the current states and actions
+  as the inputs, and :math:`y_t` as the targets.
+
+* To train the **actor network**, use the following equation:
+
+  :math:`\nabla_{\theta^\mu } J \approx E_{s_t \tilde{} \rho^\beta } [\nabla_a Q(s,a)|_{s=s_t,a=\mu (s_t ) } \cdot \nabla_{\theta^\mu} \mu(s)|_{s=s_t} ]`
+
+  Use the actor's online network to get the action mean values using the current states as the inputs.
+  Then, use the critic online network in order to get the gradients of the critic output with respect to the
+  action mean values :math:`\nabla _a Q(s,a)|_{s=s_t,a=\mu(s_t ) }`.
+  Using the chain rule, calculate the gradients of the actor's output, with respect to the actor weights,
+  given :math:`\nabla_a Q(s,a)`. Finally, apply those gradients to the actor network.
+
+After every training step, do a soft update of the critic and actor target networks' weights from the online networks.
+
+
+.. autoclass:: rl_coach.agents.wolpertinger_agent.WolpertingerAlgorithmParameters
\ No newline at end of file
diff --git a/docs_raw/source/diagrams.xml b/docs_raw/source/diagrams.xml
index 15f067f58..9b5e64a9d 100644
--- a/docs_raw/source/diagrams.xml
+++ b/docs_raw/source/diagrams.xml
@@ -1 +1 @@
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
\ No newline at end of file
diff --git a/rl_coach/agents/agent.py b/rl_coach/agents/agent.py
index 3db0aaf4e..5d12e0b5e 100644
--- a/rl_coach/agents/agent.py
+++ b/rl_coach/agents/agent.py
@@ -1003,7 +1003,7 @@ def set_incoming_directive(self, action: ActionType) -> None:
         """
         Allows setting a directive for the agent to follow. This is useful in hierarchy structures, where the agent
         has another master agent that is controlling it. In such cases, the master agent can define the goals for the
-        slave agent, define it's observation, possible actions, etc. The directive type is defined by the agent
+        slave agent, define its observation, possible actions, etc. The directive type is defined by the agent
         in-action-space.
 
         :param action: The action that should be set as the directive
diff --git a/rl_coach/agents/wolpertinger_agent.py b/rl_coach/agents/wolpertinger_agent.py
new file mode 100644
index 000000000..a16b9e973
--- /dev/null
+++ b/rl_coach/agents/wolpertinger_agent.py
@@ -0,0 +1,131 @@
+#
+# Copyright (c) 2019 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.
+#
+
+import copy
+from typing import Union
+from collections import OrderedDict
+import numpy as np
+
+from rl_coach.agents.ddpg_agent import DDPGAlgorithmParameters, DDPGActorNetworkParameters, \
+    DDPGCriticNetworkParameters, DDPGAgent
+from rl_coach.base_parameters import AgentParameters
+from rl_coach.core_types import ActionInfo
+from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
+from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
+from rl_coach.memories.non_episodic.differentiable_neural_dictionary import AnnoyDictionary
+from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace
+from rl_coach.architectures.head_parameters import WolpertingerActorHeadParameters
+
+
+class WolpertingerCriticNetworkParameters(DDPGCriticNetworkParameters):
+    def __init__(self, use_batchnorm=False):
+        super().__init__(use_batchnorm=use_batchnorm)
+
+
+class WolpertingerActorNetworkParameters(DDPGActorNetworkParameters):
+    def __init__(self, use_batchnorm=False):
+        super().__init__()
+        self.heads_parameters = [WolpertingerActorHeadParameters(batchnorm=use_batchnorm)]
+
+
+class WolpertingerAlgorithmParameters(DDPGAlgorithmParameters):
+    def __init__(self):
+        super().__init__()
+        self.action_embedding_width = 1
+        self.k = 1
+        
+
+class WolpertingerAgentParameters(AgentParameters):
+    def __init__(self, use_batchnorm=False):
+        exploration_params = AdditiveNoiseParameters()
+        exploration_params.noise_as_percentage_from_action_space = False
+
+        super().__init__(algorithm=WolpertingerAlgorithmParameters(),
+                         exploration=exploration_params,
+                         memory=EpisodicExperienceReplayParameters(),
+                         networks=OrderedDict(
+                             [("actor", WolpertingerActorNetworkParameters(use_batchnorm=use_batchnorm)),
+                              ("critic", WolpertingerCriticNetworkParameters(use_batchnorm=use_batchnorm))]))
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.wolpertinger_agent:WolpertingerAgent'
+
+
+# Deep Reinforcement Learning in Large Discrete Action Spaces - https://arxiv.org/pdf/1512.07679.pdf
+class WolpertingerAgent(DDPGAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent'] = None):
+        super().__init__(agent_parameters, parent)
+
+    def learn_from_batch(self, batch):
+        # replay buffer holds the actions in the discrete manner, as the agent is expected to act with discrete actions
+        # with the BoxDiscretization output filter. But DDPG needs to work on continuous actions, thus converting to
+        # continuous actions. This is actually a duplicate since this filtering is also done before applying actions on
+        # the environment. So might want to somehow reuse that conversion. Maybe can hold this information in the info
+        # dictionary of the transition.
+
+        output_action_filter = \
+            list(self.output_filter.action_filters.values())[0]
+        continuous_actions = []
+        for action in batch.actions():
+            continuous_actions.append(output_action_filter.filter(action))
+        batch._actions = np.array(continuous_actions).squeeze()
+
+        return super().learn_from_batch(batch)
+
+    def train(self):
+        return super().train()
+
+    def choose_action(self, curr_state):
+        if not isinstance(self.spaces.action, DiscreteActionSpace):
+            raise ValueError("WolpertingerAgent works only for discrete control problems")
+
+        # convert to batch so we can run it through the network
+        tf_input_state = self.prepare_batch_for_inference(curr_state, 'actor')
+        actor_network = self.networks['actor'].online_network
+        critic_network = self.networks['critic'].online_network
+        proto_action = actor_network.predict(tf_input_state)
+        proto_action = np.expand_dims(self.exploration_policy.get_action(proto_action), 0)
+
+        nn_action_embeddings, indices, _, _ = self.knn_tree.query(keys=proto_action, k=self.ap.algorithm.k)
+
+        # now move the actions through the critic and choose the one with the highest q value
+        critic_inputs = copy.copy(tf_input_state)
+        critic_inputs['observation'] = np.tile(critic_inputs['observation'], (self.ap.algorithm.k, 1))
+        critic_inputs['action'] = nn_action_embeddings[0]
+        q_values = critic_network.predict(critic_inputs)[0]
+        action = int(indices[0][np.argmax(q_values)])
+        self.action_signal.add_sample(action)
+        return ActionInfo(action=action, action_value=0)
+
+    def init_environment_dependent_modules(self):
+        super().init_environment_dependent_modules()
+        self.knn_tree = self.get_initialized_knn()
+
+    # TODO - ideally the knn should not be defined here, but somehow be defined by the user in the preset
+    def get_initialized_knn(self):
+        num_actions = len(self.spaces.action.actions)
+        action_max_abs_range = self.spaces.action.filtered_action_space.max_abs_range if \
+            (hasattr(self.spaces.action, 'filtered_action_space') and
+             isinstance(self.spaces.action.filtered_action_space, BoxActionSpace)) \
+            else 1.0
+        keys = np.expand_dims((np.arange(num_actions) / (num_actions - 1) - 0.5) * 2, 1) * action_max_abs_range
+        values = np.expand_dims(np.arange(num_actions), 1)
+        knn_tree = AnnoyDictionary(dict_size=num_actions, key_width=self.ap.algorithm.action_embedding_width)
+        knn_tree.add(keys, values, force_rebuild_tree=True)
+
+        return knn_tree
+
diff --git a/rl_coach/architectures/head_parameters.py b/rl_coach/architectures/head_parameters.py
index ee607dd42..207ea3e63 100644
--- a/rl_coach/architectures/head_parameters.py
+++ b/rl_coach/architectures/head_parameters.py
@@ -108,6 +108,17 @@ def __init__(self, activation_function: str ='tanh', name: str='policy_head_para
         self.batchnorm = batchnorm
 
 
+class WolpertingerActorHeadParameters(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="WolpertingerActorHead", 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,
diff --git a/rl_coach/architectures/tensorflow_components/heads/__init__.py b/rl_coach/architectures/tensorflow_components/heads/__init__.py
index 03c237a84..0a83399d3 100644
--- a/rl_coach/architectures/tensorflow_components/heads/__init__.py
+++ b/rl_coach/architectures/tensorflow_components/heads/__init__.py
@@ -18,6 +18,7 @@
 from .cil_head import RegressionHead
 from .td3_v_head import TD3VHead
 from .ddpg_v_head import DDPGVHead
+from .wolpertinger_actor_head import WolpertingerActorHead
 
 __all__ = [
     'CategoricalQHead',
@@ -38,6 +39,7 @@
     'SACQHead',
     'ClassificationHead',
     'RegressionHead',
-    'TD3VHead'
-    'DDPGVHead'
+    'TD3VHead',
+    'DDPGVHead',
+    'WolpertingerActorHead'
 ]
diff --git a/rl_coach/architectures/tensorflow_components/heads/wolpertinger_actor_head.py b/rl_coach/architectures/tensorflow_components/heads/wolpertinger_actor_head.py
new file mode 100644
index 000000000..3521a95e6
--- /dev/null
+++ b/rl_coach/architectures/tensorflow_components/heads/wolpertinger_actor_head.py
@@ -0,0 +1,59 @@
+#
+# Copyright (c) 2019 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.
+#
+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
+from rl_coach.base_parameters import AgentParameters
+from rl_coach.core_types import Embedding
+from rl_coach.spaces import SpacesDefinition, BoxActionSpace
+
+
+class WolpertingerActorHead(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',
+                 batchnorm: bool=True, dense_layer=Dense, is_training=False):
+        super().__init__(agent_parameters, spaces, network_name, head_idx, loss_weight, is_local, activation_function,
+                         dense_layer=dense_layer, is_training=is_training)
+        self.name = 'wolpertinger_actor_head'
+        self.return_type = Embedding
+        self.action_embedding_width = agent_parameters.algorithm.action_embedding_width
+        self.batchnorm = batchnorm
+        self.output_scale = self.spaces.action.filtered_action_space.max_abs_range if \
+            (hasattr(self.spaces.action, 'filtered_action_space') and
+             isinstance(self.spaces.action.filtered_action_space, BoxActionSpace)) \
+            else None
+
+    def _build_module(self, input_layer):
+        # mean
+        pre_activation_policy_value = self.dense_layer(self.action_embedding_width)(input_layer,
+                                                                                    name='actor_action_embedding')
+        self.proto_action = batchnorm_activation_dropout(input_layer=pre_activation_policy_value,
+                                                         batchnorm=self.batchnorm,
+                                                         activation_function=self.activation_function,
+                                                         dropout_rate=0,
+                                                         is_training=self.is_training,
+                                                         name="BatchnormActivationDropout_0")[-1]
+        if self.output_scale is not None:
+            self.proto_action = tf.multiply(self.proto_action, self.output_scale, name='proto_action')
+
+        self.output = [self.proto_action]
+
+    def __str__(self):
+        result = [
+            'Dense (num outputs = {})'.format(self.action_embedding_width)
+        ]
+        return '\n'.join(result)
diff --git a/rl_coach/exploration_policies/additive_noise.py b/rl_coach/exploration_policies/additive_noise.py
index 8194718b4..8b67c7d3e 100644
--- a/rl_coach/exploration_policies/additive_noise.py
+++ b/rl_coach/exploration_policies/additive_noise.py
@@ -62,7 +62,9 @@ def __init__(self, action_space: ActionSpace, noise_schedule: Schedule,
         self.evaluation_noise = evaluation_noise
         self.noise_as_percentage_from_action_space = noise_as_percentage_from_action_space
 
-        if not isinstance(action_space, BoxActionSpace):
+        if not isinstance(action_space, BoxActionSpace) and \
+                (hasattr(action_space, 'filtered_action_space') and not
+                 isinstance(action_space.filtered_action_space, BoxActionSpace)):
             raise ValueError("Additive noise exploration works only for continuous controls."
                              "The given action space is of type: {}".format(action_space.__class__.__name__))
 
diff --git a/rl_coach/exploration_policies/exploration_policy.py b/rl_coach/exploration_policies/exploration_policy.py
index a34589579..688fcce5f 100644
--- a/rl_coach/exploration_policies/exploration_policy.py
+++ b/rl_coach/exploration_policies/exploration_policy.py
@@ -115,5 +115,8 @@ def __init__(self, action_space: ActionSpace):
         """
         :param action_space: the action space used by the environment
         """
-        assert isinstance(action_space, BoxActionSpace) or isinstance(action_space, GoalsSpace)
+        assert isinstance(action_space, BoxActionSpace) or \
+               (hasattr(action_space, 'filtered_action_space') and
+                 isinstance(action_space.filtered_action_space, BoxActionSpace)) or \
+               isinstance(action_space, GoalsSpace)
         super().__init__(action_space)
diff --git a/rl_coach/filters/action/partial_discrete_action_space_map.py b/rl_coach/filters/action/partial_discrete_action_space_map.py
index 2322698ca..ad6e10530 100644
--- a/rl_coach/filters/action/partial_discrete_action_space_map.py
+++ b/rl_coach/filters/action/partial_discrete_action_space_map.py
@@ -48,7 +48,8 @@ def validate_output_action_space(self, output_action_space: ActionSpace):
 
     def get_unfiltered_action_space(self, output_action_space: ActionSpace) -> DiscreteActionSpace:
         self.output_action_space = output_action_space
-        self.input_action_space = DiscreteActionSpace(len(self.target_actions), self.descriptions)
+        self.input_action_space = DiscreteActionSpace(len(self.target_actions), self.descriptions,
+                                                      filtered_action_space=output_action_space)
         return self.input_action_space
 
     def filter(self, action: ActionType) -> ActionType:
diff --git a/rl_coach/memories/non_episodic/differentiable_neural_dictionary.py b/rl_coach/memories/non_episodic/differentiable_neural_dictionary.py
index 3368ee8b9..8633118ce 100644
--- a/rl_coach/memories/non_episodic/differentiable_neural_dictionary.py
+++ b/rl_coach/memories/non_episodic/differentiable_neural_dictionary.py
@@ -57,7 +57,7 @@ def __init__(self, dict_size, key_width, new_value_shift_coefficient=0.1, batch_
 
         self.built_capacity = 0
 
-    def add(self, keys, values, additional_data=None):
+    def add(self, keys, values, additional_data=None, force_rebuild_tree=False):
         if not additional_data:
             additional_data = [None] * len(keys)
 
@@ -96,7 +96,7 @@ def add(self, keys, values, additional_data=None):
         if len(self.buffered_indices) >= self.min_update_size:
             self.min_update_size = max(self.initial_update_size, int(self.curr_size * 0.02))
             self._rebuild_index()
-        elif self.rebuild_on_every_update:
+        elif force_rebuild_tree or self.rebuild_on_every_update:
             self._rebuild_index()
 
         self.current_timestamp += 1
diff --git a/rl_coach/presets/Mujoco_Wolpertinger.py b/rl_coach/presets/Mujoco_Wolpertinger.py
new file mode 100644
index 000000000..f12e41cb3
--- /dev/null
+++ b/rl_coach/presets/Mujoco_Wolpertinger.py
@@ -0,0 +1,57 @@
+from collections import OrderedDict
+
+from rl_coach.architectures.layers import Dense
+from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters, EmbedderScheme
+from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps
+from rl_coach.environments.environment import SingleLevelSelection
+from rl_coach.environments.gym_environment import GymVectorEnvironment, mujoco_v2
+from rl_coach.filters.action import BoxDiscretization
+from rl_coach.filters.filter import OutputFilter
+from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
+from rl_coach.graph_managers.graph_manager import ScheduleParameters
+from rl_coach.agents.wolpertinger_agent import WolpertingerAgentParameters
+
+####################
+# Graph Scheduling #
+####################
+schedule_params = ScheduleParameters()
+schedule_params.improve_steps = EnvironmentSteps(2000000)
+schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(20)
+schedule_params.evaluation_steps = EnvironmentEpisodes(1)
+schedule_params.heatup_steps = EnvironmentSteps(3000)
+
+#########
+# Agent #
+#########
+agent_params = WolpertingerAgentParameters()
+agent_params.network_wrappers['actor'].input_embedders_parameters['observation'].scheme = [Dense(400)]
+agent_params.network_wrappers['actor'].middleware_parameters.scheme = [Dense(300)]
+agent_params.network_wrappers['critic'].input_embedders_parameters['observation'].scheme = [Dense(400)]
+agent_params.network_wrappers['critic'].middleware_parameters.scheme = [Dense(300)]
+agent_params.network_wrappers['critic'].input_embedders_parameters['action'].scheme = EmbedderScheme.Empty
+agent_params.output_filter = \
+    OutputFilter(
+        action_filters=OrderedDict([
+            ('discretization', BoxDiscretization(num_bins_per_dimension=int(1e6)))
+        ]),
+        is_a_reference_filter=False
+    )
+
+###############
+# Environment #
+###############
+env_params = GymVectorEnvironment(level=SingleLevelSelection(mujoco_v2))
+
+########
+# Test #
+########
+preset_validation_params = PresetValidationParameters()
+preset_validation_params.test = True
+preset_validation_params.min_reward_threshold = 500
+preset_validation_params.max_episodes_to_achieve_reward = 1000
+preset_validation_params.reward_test_level = 'inverted_pendulum'
+preset_validation_params.trace_test_levels = ['inverted_pendulum']
+
+graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
+                                    schedule_params=schedule_params, vis_params=VisualizationParameters(),
+                                    preset_validation_params=preset_validation_params)
diff --git a/rl_coach/spaces.py b/rl_coach/spaces.py
index 503598c08..5dcaa2bb1 100644
--- a/rl_coach/spaces.py
+++ b/rl_coach/spaces.py
@@ -385,7 +385,8 @@ class DiscreteActionSpace(ActionSpace):
     """
     A discrete action space with action indices as actions
     """
-    def __init__(self, num_actions: int, descriptions: Union[None, List, Dict]=None, default_action: np.ndarray=None):
+    def __init__(self, num_actions: int, descriptions: Union[None, List, Dict]=None, default_action: np.ndarray=None,
+                 filtered_action_space=None):
         super().__init__(1, low=0, high=num_actions-1, descriptions=descriptions)
         # the number of actions is mapped to high
 
@@ -395,6 +396,9 @@ def __init__(self, num_actions: int, descriptions: Union[None, List, Dict]=None,
         else:
             self.default_action = default_action
 
+        if filtered_action_space is not None:
+            self.filtered_action_space = filtered_action_space
+
     @property
     def actions(self) -> List[ActionType]:
         return list(range(0, int(self.high[0]) + 1))