Moved coach to its top level module.

coach/agents/__init__.py

@@ -13,28 +13,28 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-from agents.actor_critic_agent import ActorCriticAgent
+from coach.agents.actor_critic_agent import ActorCriticAgent
-from agents.agent import Agent
+from coach.agents.agent import Agent
-from agents.bc_agent import BCAgent
+from coach.agents.bc_agent import BCAgent
-from agents.bootstrapped_dqn_agent import BootstrappedDQNAgent
+from coach.agents.bootstrapped_dqn_agent import BootstrappedDQNAgent
-from agents.categorical_dqn_agent import CategoricalDQNAgent
+from coach.agents.categorical_dqn_agent import CategoricalDQNAgent
-from agents.clipped_ppo_agent import ClippedPPOAgent
+from coach.agents.clipped_ppo_agent import ClippedPPOAgent
-from agents.ddpg_agent import DDPGAgent
+from coach.agents.ddpg_agent import DDPGAgent
-from agents.ddqn_agent import DDQNAgent
+from coach.agents.ddqn_agent import DDQNAgent
-from agents.dfp_agent import DFPAgent
+from coach.agents.dfp_agent import DFPAgent
-from agents.dqn_agent import DQNAgent
+from coach.agents.dqn_agent import DQNAgent
-from agents.human_agent import HumanAgent
+from coach.agents.human_agent import HumanAgent
-from agents.imitation_agent import ImitationAgent
+from coach.agents.imitation_agent import ImitationAgent
-from agents.mmc_agent import MixedMonteCarloAgent
+from coach.agents.mmc_agent import MixedMonteCarloAgent
-from agents.n_step_q_agent import NStepQAgent
+from coach.agents.n_step_q_agent import NStepQAgent
-from agents.naf_agent import NAFAgent
+from coach.agents.naf_agent import NAFAgent
-from agents.nec_agent import NECAgent
+from coach.agents.nec_agent import NECAgent
-from agents.pal_agent import PALAgent
+from coach.agents.pal_agent import PALAgent
-from agents.policy_gradients_agent import PolicyGradientsAgent
+from coach.agents.policy_gradients_agent import PolicyGradientsAgent
-from agents.policy_optimization_agent import PolicyOptimizationAgent
+from coach.agents.policy_optimization_agent import PolicyOptimizationAgent
-from agents.ppo_agent import PPOAgent
+from coach.agents.ppo_agent import PPOAgent
-from agents.qr_dqn_agent import QuantileRegressionDQNAgent
+from coach.agents.qr_dqn_agent import QuantileRegressionDQNAgent
-from agents.value_optimization_agent import ValueOptimizationAgent
+from coach.agents.value_optimization_agent import ValueOptimizationAgent
 
 __all__ = [ActorCriticAgent,
            Agent,

coach/agents/actor_critic_agent.py

@@ -16,9 +16,9 @@
 import numpy as np
 from scipy import signal
 
-from agents import policy_optimization_agent as poa
+from coach.agents import policy_optimization_agent as poa
-import utils
+from coach import utils
-import logger
+from coach import logger
 
 
 # Actor Critic - https://arxiv.org/abs/1602.01783

coach/agents/agent.py

@@ -18,7 +18,7 @@ import copy
 import random
 import time
 
-import logger
+from coach import logger
 try:
     import matplotlib.pyplot as plt
 except ImportError:
@@ -29,13 +29,12 @@ from pandas.io import pickle
 from six.moves import range
 import scipy
 
-from architectures.tensorflow_components import shared_variables as sv
+from coach.architectures.tensorflow_components import shared_variables as sv
-import configurations
+from coach import configurations
-import exploration_policies as ep  # noqa, used in eval()
+from coach import exploration_policies as ep  # noqa, used in eval()
-import memories  # noqa, used in eval()
+from coach import memories  # noqa, used in eval()
-from memories import memory
+from coach.memories import memory
-import renderer
+from coach import utils
-import utils
 
 
 class Agent(object):
@@ -100,7 +99,6 @@ class Agent(object):
         self.main_network = None
         self.networks = []
         self.last_episode_images = []
-        self.renderer = renderer.Renderer()
 
         # signals
         self.signals = []
@@ -234,13 +232,6 @@ class Agent(object):
                 r, g, b = observation[:, :, 0], observation[:, :, 1], observation[:, :, 2]
                 observation = 0.2989 * r + 0.5870 * g + 0.1140 * b
 
-            # Render the processed observation which is how the agent will see it
-            # Warning: this cannot currently be done in parallel to rendering the environment
-            if self.tp.visualization.render_observation:
-                if not self.renderer.is_open:
-                    self.renderer.create_screen(observation.shape[0], observation.shape[1])
-                self.renderer.render_image(observation)
-
             return observation.astype('uint8')
         else:
             if self.tp.env.normalize_observation:

coach/agents/bc_agent.py

@@ -15,7 +15,7 @@
 #
 import numpy as np
 
-from agents import imitation_agent
+from coach.agents import imitation_agent
 
 
 # Behavioral Cloning Agent

coach/agents/bootstrapped_dqn_agent.py

@@ -15,8 +15,9 @@
 #
 import numpy as np
 
-from agents import value_optimization_agent as voa
+from coach.agents import value_optimization_agent as voa
-import utils
+from coach import utils
+
 
 # Bootstrapped DQN - https://arxiv.org/pdf/1602.04621.pdf
 class BootstrappedDQNAgent(voa.ValueOptimizationAgent):

coach/agents/categorical_dqn_agent.py

@@ -15,7 +15,7 @@
 #
 import numpy as np
 
-from agents import value_optimization_agent as voa
+from coach.agents import value_optimization_agent as voa
 
 
 # Categorical Deep Q Network - https://arxiv.org/pdf/1707.06887.pdf

coach/agents/clipped_ppo_agent.py

@@ -19,10 +19,10 @@ from random import shuffle
 
 import numpy as np
 
-from agents import actor_critic_agent as aca
+from coach.agents import actor_critic_agent as aca
-from agents import policy_optimization_agent as poa
+from coach.agents import policy_optimization_agent as poa
-import logger
+from coach import logger
-import utils
+from coach import utils
 
 
 # Clipped Proximal Policy Optimization - https://arxiv.org/abs/1707.06347

coach/agents/ddpg_agent.py

@@ -17,11 +17,11 @@ import copy
 
 import numpy as np
 
-from agents import actor_critic_agent as aca
+from coach.agents import actor_critic_agent as aca
-from agents import agent
+from coach.agents import agent
-from architectures import network_wrapper as nw
+from coach.architectures import network_wrapper as nw
-import configurations as conf
+from coach import configurations as conf
-import utils
+from coach import utils
 
 
 # Deep Deterministic Policy Gradients Network - https://arxiv.org/pdf/1509.02971.pdf

coach/agents/ddqn_agent.py

@@ -15,7 +15,7 @@
 #
 import numpy as np
 
-from agents import value_optimization_agent as voa
+from coach.agents import value_optimization_agent as voa
 
 
 # Double DQN - https://arxiv.org/abs/1509.06461

coach/agents/dfp_agent.py

@@ -15,9 +15,9 @@
 #
 import numpy as np
 
-from agents import agent
+from coach.agents import agent
-from architectures import network_wrapper as nw
+from coach.architectures import network_wrapper as nw
-import utils
+from coach import utils
 
 
 # Direct Future Prediction Agent - http://vladlen.info/papers/learning-to-act.pdf

coach/agents/distributional_dqn_agent.py

@@ -15,7 +15,7 @@
 #
 import numpy as np
 
-from agents import value_optimization_agent as voa
+from coach.agents import value_optimization_agent as voa
 
 
 # Distributional Deep Q Network - https://arxiv.org/pdf/1707.06887.pdf

coach/agents/dqn_agent.py

@@ -15,7 +15,7 @@
 #
 import numpy as np
 
-from agents import value_optimization_agent as voa
+from coach.agents import value_optimization_agent as voa
 
 
 # Deep Q Network - https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf

coach/agents/human_agent.py

@@ -19,9 +19,9 @@ import os
 import pygame
 from pandas.io import pickle
 
-from agents import agent
+from coach.agents import agent
-import logger
+from coach import logger
-import utils
+from coach import utils
 
 
 class HumanAgent(agent.Agent):

coach/agents/imitation_agent.py

@@ -15,10 +15,10 @@
 #
 import collections
 
-from agents import agent
+from coach.agents import agent
-from architectures import network_wrapper as nw
+from coach.architectures import network_wrapper as nw
-import utils
+from coach import utils
-import logging
+from coach import logger
 
 
 # Imitation Agent
@@ -55,7 +55,7 @@ class ImitationAgent(agent.Agent):
         # log to screen
         if phase == utils.RunPhase.TRAIN:
             # for the training phase - we log during the episode to visualize the progress in training
-            logging.screen.log_dict(
+            logger.screen.log_dict(
                 collections.OrderedDict([
                     ("Worker", self.task_id),
                     ("Episode", self.current_episode),
@@ -65,5 +65,5 @@ class ImitationAgent(agent.Agent):
                 prefix="Training"
             )
         else:
-            # for the evaluation phase - logging as in regular RL
+            # for the evaluation phase - logger as in regular RL
             agent.Agent.log_to_screen(self, phase)

coach/agents/mmc_agent.py

@@ -15,7 +15,7 @@
 #
 import numpy as np
 
-from agents import value_optimization_agent as voa
+from coach.agents import value_optimization_agent as voa
 
 
 class MixedMonteCarloAgent(voa.ValueOptimizationAgent):

coach/agents/n_step_q_agent.py

@@ -15,10 +15,10 @@
 #
 import numpy as np
 
-from agents import value_optimization_agent as voa
+from coach.agents import value_optimization_agent as voa
-from agents import policy_optimization_agent as poa
+from coach.agents import policy_optimization_agent as poa
-import logger
+from coach import logger
-import utils
+from coach import utils
 
 
 # N Step Q Learning Agent - https://arxiv.org/abs/1602.01783

coach/agents/naf_agent.py

@@ -15,8 +15,8 @@
 #
 import numpy as np
 
-from agents.value_optimization_agent import ValueOptimizationAgent
+from coach.agents.value_optimization_agent import ValueOptimizationAgent
-import utils
+from coach import utils
 
 
 # Normalized Advantage Functions - https://arxiv.org/pdf/1603.00748.pdf

coach/agents/nec_agent.py

@@ -13,9 +13,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-from agents import value_optimization_agent as voa
+from coach.agents import value_optimization_agent as voa
-from logger import screen
+from coach.logger import screen
-import utils
+from coach import utils
 
 
 # Neural Episodic Control - https://arxiv.org/pdf/1703.01988.pdf

coach/agents/pal_agent.py

@@ -15,7 +15,7 @@
 #
 import numpy as np
 
-from agents import value_optimization_agent as voa
+from coach.agents import value_optimization_agent as voa
 
 
 # Persistent Advantage Learning - https://arxiv.org/pdf/1512.04860.pdf

coach/agents/policy_gradients_agent.py

@@ -15,9 +15,9 @@
 #
 import numpy as np
 
-from agents import policy_optimization_agent as poa
+from coach.agents import policy_optimization_agent as poa
-import logger
+from coach import logger
-import utils
+from coach import utils
 
 
 class PolicyGradientsAgent(poa.PolicyOptimizationAgent):

coach/agents/policy_optimization_agent.py

@@ -17,10 +17,10 @@ import collections
 
 import numpy as np
 
-from agents import agent
+from coach.agents import agent
-from architectures import network_wrapper as nw
+from coach.architectures import network_wrapper as nw
-import logger
+from coach import logger
-import utils
+from coach import utils
 
 
 class PolicyGradientRescaler(utils.Enum):

coach/agents/ppo_agent.py

@@ -18,12 +18,12 @@ import copy
 
 import numpy as np
 
-from agents import actor_critic_agent as aca
+from coach.agents import actor_critic_agent as aca
-from agents import policy_optimization_agent as poa
+from coach.agents import policy_optimization_agent as poa
-from architectures import network_wrapper as nw
+from coach.architectures import network_wrapper as nw
-import configurations
+from coach import configurations
-import logger
+from coach import logger
-import utils
+from coach import utils
 
 
 # Proximal Policy Optimization - https://arxiv.org/pdf/1707.06347.pdf

coach/agents/qr_dqn_agent.py

@@ -15,7 +15,7 @@
 #
 import numpy as np
 
-from agents import value_optimization_agent as voa
+from coach.agents import value_optimization_agent as voa
 
 
 # Quantile Regression Deep Q Network - https://arxiv.org/pdf/1710.10044v1.pdf

coach/agents/value_optimization_agent.py

@@ -15,9 +15,9 @@
 #
 import numpy as np
 
-from agents import agent
+from coach.agents import agent
-from architectures import network_wrapper as nw
+from coach.architectures import network_wrapper as nw
-import utils
+from coach import utils
 
 
 class ValueOptimizationAgent(agent.Agent):

coach/architectures/__init__.py

@@ -13,7 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-import logger
+from coach import logger
 
 try:
     from architectures.tensorflow_components import general_network as ts_gn

coach/architectures/neon_components/architecture.py

@@ -16,8 +16,8 @@
 import ngraph as ng
 import numpy as np
 
-from architectures import architecture
+from coach.architectures import architecture
-import utils
+from coach import utils
 
 
 class NeonArchitecture(architecture.Architecture):

coach/architectures/neon_components/general_network.py

@@ -17,11 +17,11 @@ import ngraph as ng
 from ngraph.frontends import neon
 from ngraph.util import names as ngraph_names
 
-from architectures.neon_components import architecture
+from coach.architectures.neon_components import architecture
-from architectures.neon_components import embedders
+from coach.architectures.neon_components import embedders
-from architectures.neon_components import middleware
+from coach.architectures.neon_components import middleware
-from architectures.neon_components import heads
+from coach.architectures.neon_components import heads
-import configurations as conf
+from coach import configurations as conf
 
 
 class GeneralNeonNetwork(architecture.NeonArchitecture):

coach/architectures/neon_components/heads.py

@@ -17,8 +17,8 @@ import ngraph as ng
 from ngraph.frontends import neon
 from ngraph.util import names as ngraph_names
 
-import utils
+from coach import utils
-from architectures.neon_components import losses
+from coach.architectures.neon_components import losses
 
 
 class Head(object):

coach/architectures/network_wrapper.py

@@ -16,16 +16,16 @@
 import os
 import collections
 
-import configurations as conf
+from coach import configurations as conf
-import logger
+from coach import logger
 try:
     import tensorflow as tf
-    from architectures.tensorflow_components import general_network as tf_net  #import GeneralTensorFlowNetwork
+    from coach.architectures.tensorflow_components import general_network as tf_net
 except ImportError:
     logger.failed_imports.append("TensorFlow")
 
 try:
-    from architectures.neon_components import general_network as neon_net
+    from coach.architectures.neon_components import general_network as neon_net
 except ImportError:
     logger.failed_imports.append("Neon")
 

coach/architectures/tensorflow_components/architecture.py

@@ -17,9 +17,10 @@ import time
 
 import tensorflow as tf
 
-from architectures import architecture
+from coach.architectures import architecture
-import configurations as conf
+from coach import configurations as conf
-import utils
+from coach import utils
+
 
 def variable_summaries(var):
     """Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
@@ -36,6 +37,7 @@ def variable_summaries(var):
             tf.summary.scalar('min', tf.reduce_min(var))
             tf.summary.histogram('histogram', var)
 
+
 class TensorFlowArchitecture(architecture.Architecture):
     def __init__(self, tuning_parameters, name="", global_network=None, network_is_local=True):
         """

coach/architectures/tensorflow_components/embedders.py

@@ -15,7 +15,7 @@
 #
 import tensorflow as tf
 
-from configurations import EmbedderComplexity
+from coach.configurations import EmbedderComplexity
 
 
 class InputEmbedder(object):

coach/architectures/tensorflow_components/general_network.py

@@ -15,11 +15,11 @@
 #
 import tensorflow as tf
 
-from architectures.tensorflow_components import architecture
+from coach.architectures.tensorflow_components import architecture
-from architectures.tensorflow_components import embedders
+from coach.architectures.tensorflow_components import embedders
-from architectures.tensorflow_components import middleware
+from coach.architectures.tensorflow_components import middleware
-from architectures.tensorflow_components import heads
+from coach.architectures.tensorflow_components import heads
-import configurations as conf
+from coach import configurations as conf
 
 
 class GeneralTensorFlowNetwork(architecture.TensorFlowArchitecture):

coach/architectures/tensorflow_components/heads.py

@@ -16,7 +16,7 @@
 import tensorflow as tf
 import numpy as np
 
-import utils
+from coach import utils
 
 
 # Used to initialize weights for policy and value output layers

coach/configurations.py

@@ -14,9 +14,9 @@
 # limitations under the License.
 #
 import json
-
 import types
-import utils
+
+from coach import utils
 
 
 class Frameworks(utils.Enum):

coach/environments/__init__.py

@@ -13,10 +13,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-from environments.gym_environment_wrapper import GymEnvironmentWrapper
+from coach.environments.gym_environment_wrapper import GymEnvironmentWrapper
-from environments.doom_environment_wrapper import DoomEnvironmentWrapper
+from coach.environments.doom_environment_wrapper import DoomEnvironmentWrapper
-from environments.carla_environment_wrapper import CarlaEnvironmentWrapper
+from coach.environments.carla_environment_wrapper import CarlaEnvironmentWrapper
-import utils
+from coach import utils
 
 
 class EnvTypes(utils.Enum):

coach/environments/carla_environment_wrapper.py

@@ -6,7 +6,7 @@ import sys
 
 import numpy as np
 
-import logger
+from coach import logger
 try:
     if 'CARLA_ROOT' in os.environ:
         sys.path.append(os.path.join(os.environ.get('CARLA_ROOT'),
@@ -16,8 +16,8 @@ try:
     from carla import sensor as carla_sensor
 except ImportError:
     logger.failed_imports.append("CARLA")
-from environments import environment_wrapper as ew
+from coach.environments import environment_wrapper as ew
-import utils
+from coach import utils
 
 
 # enum of the available levels and their path

coach/environments/doom_environment_wrapper.py

@@ -13,19 +13,18 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-import enum
 import os
 
 import numpy as np
 
-import logger
+from coach import logger
 try:
     import vizdoom
 except ImportError:
     logger.failed_imports.append("ViZDoom")
 
-from environments import environment_wrapper as ew
+from coach.environments import environment_wrapper as ew
-import utils
+from coach import utils
 
 
 # enum of the available levels and their path

coach/environments/environment_wrapper.py

@@ -18,8 +18,7 @@ import time
 
 import numpy as np
 
-import renderer
+from coach import utils
-import utils
 
 
 class EnvironmentWrapper(object):
@@ -62,7 +61,6 @@ class EnvironmentWrapper(object):
         self.wait_for_explicit_human_action = False
         self.is_rendered = self.is_rendered or self.human_control
         self.game_is_open = True
-        self.renderer = renderer.Renderer()
 
     @property
     def measurements(self):
@@ -106,26 +104,6 @@ class EnvironmentWrapper(object):
         Get an action from the user keyboard
         :return: action index
         """
-        if self.wait_for_explicit_human_action:
-            while len(self.renderer.pressed_keys) == 0:
-                self.renderer.get_events()
-
-        if self.key_to_action == {}:
-            # the keys are the numbers on the keyboard corresponding to the action index
-            if len(self.renderer.pressed_keys) > 0:
-                action_idx = self.renderer.pressed_keys[0] - ord("1")
-                if 0 <= action_idx < self.action_space_size:
-                    return action_idx
-        else:
-            # the keys are mapped through the environment to more intuitive keyboard keys
-            # key = tuple(self.renderer.pressed_keys)
-            # for key in self.renderer.pressed_keys:
-            for env_keys in self.key_to_action.keys():
-                if set(env_keys) == set(self.renderer.pressed_keys):
-                    return self.key_to_action[env_keys]
-
-        # return the default action 0 so that the environment will continue running
-        return self.default_action
 
     def step(self, action_idx):
         """

coach/environments/gym_environment_wrapper.py

@@ -18,8 +18,8 @@ import random
 import gym
 import numpy as np
 
-from environments import environment_wrapper as ew
+from coach.environments import environment_wrapper as ew
-import utils
+from coach import utils
 
 
 class GymEnvironmentWrapper(ew.EnvironmentWrapper):

coach/exploration_policies/__init__.py

@@ -13,18 +13,18 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-from exploration_policies.additive_noise import AdditiveNoise
+from coach.exploration_policies.additive_noise import AdditiveNoise
-from exploration_policies.approximated_thompson_sampling_using_dropout import ApproximatedThompsonSamplingUsingDropout
+from coach.exploration_policies.approximated_thompson_sampling_using_dropout import ApproximatedThompsonSamplingUsingDropout
-from exploration_policies.bayesian import Bayesian
+from coach.exploration_policies.bayesian import Bayesian
-from exploration_policies.boltzmann import Boltzmann
+from coach.exploration_policies.boltzmann import Boltzmann
-from exploration_policies.bootstrapped import Bootstrapped
+from coach.exploration_policies.bootstrapped import Bootstrapped
-from exploration_policies.categorical import Categorical
+from coach.exploration_policies.categorical import Categorical
-from exploration_policies.continuous_entropy import ContinuousEntropy
+from coach.exploration_policies.continuous_entropy import ContinuousEntropy
-from exploration_policies.e_greedy import EGreedy
+from coach.exploration_policies.e_greedy import EGreedy
-from exploration_policies.exploration_policy import ExplorationPolicy
+from coach.exploration_policies.exploration_policy import ExplorationPolicy
-from exploration_policies.greedy import Greedy
+from coach.exploration_policies.greedy import Greedy
-from exploration_policies.ou_process import OUProcess
+from coach.exploration_policies.ou_process import OUProcess
-from exploration_policies.thompson_sampling import ThompsonSampling
+from coach.exploration_policies.thompson_sampling import ThompsonSampling
 
 
 __all__ = [AdditiveNoise,

coach/exploration_policies/additive_noise.py

@@ -15,8 +15,8 @@
 #
 import numpy as np
 
-from exploration_policies import exploration_policy
+from coach.exploration_policies import exploration_policy
-import utils
+from coach import utils
 
 
 class AdditiveNoise(exploration_policy.ExplorationPolicy):

coach/exploration_policies/approximated_thompson_sampling_using_dropout.py

@@ -15,7 +15,7 @@
 #
 import numpy as np
 
-from exploration_policies import exploration_policy
+from coach.exploration_policies import exploration_policy
 
 
 class ApproximatedThompsonSamplingUsingDropout(exploration_policy.ExplorationPolicy):

coach/exploration_policies/bayesian.py

@@ -15,8 +15,8 @@
 #
 import numpy as np
 
-from exploration_policies import exploration_policy
+from coach.exploration_policies import exploration_policy
-import utils
+from coach import utils
 
 
 class Bayesian(exploration_policy.ExplorationPolicy):

coach/exploration_policies/boltzmann.py

@@ -15,8 +15,9 @@
 #
 import numpy as np
 
-from exploration_policies import exploration_policy
+from coach.exploration_policies import exploration_policy
-import utils
+from coach import utils
+
 
 class Boltzmann(exploration_policy.ExplorationPolicy):
     def __init__(self, tuning_parameters):

coach/exploration_policies/bootstrapped.py

@@ -15,7 +15,7 @@
 #
 import numpy as np
 
-from exploration_policies import e_greedy
+from coach.exploration_policies import e_greedy
 
 
 class Bootstrapped(e_greedy.EGreedy):

coach/exploration_policies/categorical.py

@@ -15,7 +15,7 @@
 #
 import numpy as np
 
-from exploration_policies import exploration_policy
+from coach.exploration_policies import exploration_policy
 
 
 class Categorical(exploration_policy.ExplorationPolicy):

coach/exploration_policies/continuous_entropy.py

@@ -13,7 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-from exploration_policies import exploration_policy
+from coach.exploration_policies import exploration_policy
 
 
 class ContinuousEntropy(exploration_policy.ExplorationPolicy):

coach/exploration_policies/e_greedy.py

@@ -15,8 +15,8 @@
 #
 import numpy as np
 
-from exploration_policies import exploration_policy
+from coach.exploration_policies import exploration_policy
-import utils
+from coach import utils
 
 
 class EGreedy(exploration_policy.ExplorationPolicy):

coach/exploration_policies/exploration_policy.py

@@ -13,7 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-import utils
+from coach import utils
 
 
 class ExplorationPolicy(object):

coach/exploration_policies/greedy.py

@@ -15,7 +15,7 @@
 #
 import numpy as np
 
-from exploration_policies import exploration_policy
+from coach.exploration_policies import exploration_policy
 
 
 class Greedy(exploration_policy.ExplorationPolicy):

coach/exploration_policies/ou_process.py

@@ -15,11 +15,12 @@
 #
 import numpy as np
 
-from exploration_policies import exploration_policy
+from coach.exploration_policies import exploration_policy
 
 # Based on on the description in:
 # https://math.stackexchange.com/questions/1287634/implementing-ornstein-uhlenbeck-in-matlab
 
+
 # Ornstein-Uhlenbeck process
 class OUProcess(exploration_policy.ExplorationPolicy):
     def __init__(self, tuning_parameters):

coach/exploration_policies/thompson_sampling.py

@@ -15,7 +15,7 @@
 #
 import numpy as np
 
-from exploration_policies import exploration_policy
+from coach.exploration_policies import exploration_policy
 
 
 class ThompsonSampling(exploration_policy.ExplorationPolicy):

coach/memories/__init__.py

@@ -13,13 +13,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-from memories.differentiable_neural_dictionary import AnnoyDictionary
+from coach.memories.differentiable_neural_dictionary import AnnoyDictionary
-from memories.differentiable_neural_dictionary import AnnoyIndex
+from coach.memories.differentiable_neural_dictionary import AnnoyIndex
-from memories.differentiable_neural_dictionary import QDND
+from coach.memories.differentiable_neural_dictionary import QDND
-from memories.episodic_experience_replay import EpisodicExperienceReplay
+from coach.memories.episodic_experience_replay import EpisodicExperienceReplay
-from memories.memory import Episode
+from coach.memories.memory import Episode
-from memories.memory import Memory
+from coach.memories.memory import Memory
-from memories.memory import Transition
+from coach.memories.memory import Transition
 
 __all__ = [AnnoyDictionary,
            AnnoyIndex,

coach/memories/episodic_experience_replay.py

@@ -17,7 +17,7 @@ import typing
 
 import numpy as np
 
-from memories import memory
+from coach.memories import memory
 
 
 class EpisodicExperienceReplay(memory.Memory):

coach/presets.py

@@ -17,11 +17,11 @@ import ast
 import json
 import sys
 
-import agents
+from coach import agents
-import configurations as conf
+from coach import configurations as conf
-import environments as env
+from coach import environments as env
-import exploration_policies as ep
+from coach import exploration_policies as ep
-import presets
+from coach import presets
 
 
 def json_to_preset(json_path):

dashboard.py

@@ -1,972 +0,0 @@
-#
-# 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.
-#
-
-"""
-To run Coach Dashboard, run the following command:
-python3 dashboard.py
-"""
-
-import colorsys
-import datetime
-import enum
-import itertools
-import os
-import random
-
-from bokeh import palettes
-from bokeh import layouts as bl
-from bokeh import models as bm
-from bokeh.models import widgets as bw
-from bokeh import plotting as bp
-import numpy as np
-import pandas as pd
-from pandas.io import pandas_common
-import wx
-
-import utils
-
-
-class DialogApp(wx.App):
-    def getFileDialog(self):
-        with wx.FileDialog(None, "Open CSV file", wildcard="CSV files (*.csv)|*.csv",
-                           style=wx.FD_OPEN | wx.FD_FILE_MUST_EXIST | wx.FD_CHANGE_DIR | wx.FD_MULTIPLE) as fileDialog:
-            if fileDialog.ShowModal() == wx.ID_CANCEL:
-                return None  # the user changed their mind
-            else:
-                # Proceed loading the file chosen by the user
-                return fileDialog.GetPaths()
-
-    def getDirDialog(self):
-        with wx.DirDialog (None, "Choose input directory", "",
-                           style=wx.FD_OPEN | wx.FD_FILE_MUST_EXIST | wx.FD_CHANGE_DIR) as dirDialog:
-            if dirDialog.ShowModal() == wx.ID_CANCEL:
-                return None  # the user changed their mind
-            else:
-                # Proceed loading the dir chosen by the user
-                return dirDialog.GetPath()
-class Signal:
-    def __init__(self, name, parent):
-        self.name = name
-        self.full_name = "{}/{}".format(parent.filename, self.name)
-        self.selected = False
-        self.color = random.choice(palettes.Dark2[8])
-        self.line = None
-        self.bands = None
-        self.bokeh_source = parent.bokeh_source
-        self.min_val = 0
-        self.max_val = 0
-        self.axis = 'default'
-        self.sub_signals = []
-        for name in self.bokeh_source.data.keys():
-            if (len(name.split('/')) == 1 and name == self.name) or '/'.join(name.split('/')[:-1]) == self.name:
-                self.sub_signals.append(name)
-        if len(self.sub_signals) > 1:
-            self.mean_signal = utils.squeeze_list([name for name in self.sub_signals if 'Mean' in name.split('/')[-1]])
-            self.stdev_signal = utils.squeeze_list([name for name in self.sub_signals if 'Stdev' in name.split('/')[-1]])
-            self.min_signal = utils.squeeze_list([name for name in self.sub_signals if 'Min' in name.split('/')[-1]])
-            self.max_signal = utils.squeeze_list([name for name in self.sub_signals if 'Max' in name.split('/')[-1]])
-        else:
-            self.mean_signal = utils.squeeze_list(self.name)
-            self.stdev_signal = None
-            self.min_signal = None
-            self.max_signal = None
-        self.has_bollinger_bands = False
-        if self.mean_signal and self.stdev_signal and self.min_signal and self.max_signal:
-            self.has_bollinger_bands = True
-        self.show_bollinger_bands = False
-        self.bollinger_bands_source = None
-        self.update_range()
-
-    def set_color(self, color):
-        self.color = color
-        if self.line:
-            self.line.glyph.line_color = color
-        if self.bands:
-            self.bands.glyph.fill_color = color
-
-    def set_selected(self, val):
-        global current_color
-        if self.selected != val:
-            self.selected = val
-            if self.line:
-                # self.set_color(palettes.Dark2[8][current_color])
-                # current_color = (current_color + 1) % len(palettes.Dark2[8])
-                self.line.visible = self.selected
-                if self.bands:
-                    self.bands.visible = self.selected and self.show_bollinger_bands
-            elif self.selected:
-                # lazy plotting - plot only when selected for the first time
-                show_spinner()
-                self.set_color(palettes.Dark2[8][current_color])
-                current_color = (current_color + 1) % len(palettes.Dark2[8])
-                if self.has_bollinger_bands:
-                    self.set_bands_source()
-                    self.create_bands()
-                self.line = plot.line('index', self.mean_signal, source=self.bokeh_source,
-                                      line_color=self.color, line_width=2)
-                self.line.visible = True
-                hide_spinner()
-
-    def set_dash(self, dash):
-        self.line.glyph.line_dash = dash
-
-    def create_bands(self):
-        self.bands = plot.patch(x='band_x', y='band_y', source=self.bollinger_bands_source,
-                                color=self.color, fill_alpha=0.4, alpha=0.1, line_width=0)
-        self.bands.visible = self.show_bollinger_bands
-        # self.min_line = plot.line('index', self.min_signal, source=self.bokeh_source,
-        #                           line_color=self.color, line_width=3, line_dash="4 4")
-        # self.max_line = plot.line('index', self.max_signal, source=self.bokeh_source,
-        #                           line_color=self.color, line_width=3, line_dash="4 4")
-        # self.min_line.visible = self.show_bollinger_bands
-        # self.max_line.visible = self.show_bollinger_bands
-
-    def set_bands_source(self):
-        x_ticks = self.bokeh_source.data['index']
-        mean_values = self.bokeh_source.data[self.mean_signal]
-        stdev_values = self.bokeh_source.data[self.stdev_signal]
-        band_x = np.append(x_ticks, x_ticks[::-1])
-        band_y = np.append(mean_values - stdev_values, mean_values[::-1] + stdev_values[::-1])
-        source_data = {'band_x': band_x, 'band_y': band_y}
-        if self.bollinger_bands_source:
-            self.bollinger_bands_source.data = source_data
-        else:
-            self.bollinger_bands_source = bm.ColumnDataSource(source_data)
-
-    def change_bollinger_bands_state(self, new_state):
-        self.show_bollinger_bands = new_state
-        if self.bands and self.selected:
-            self.bands.visible = new_state
-            # self.min_line.visible = new_state
-            # self.max_line.visible = new_state
-
-    def update_range(self):
-        self.min_val = np.min(self.bokeh_source.data[self.mean_signal])
-        self.max_val = np.max(self.bokeh_source.data[self.mean_signal])
-
-    def set_axis(self, axis):
-        self.axis = axis
-        self.line.y_range_name = axis
-
-    def toggle_axis(self):
-        if self.axis == 'default':
-            self.set_axis('secondary')
-        else:
-            self.set_axis('default')
-
-
-class SignalsFileBase:
-    def __init__(self):
-        self.full_csv_path = ""
-        self.dir = ""
-        self.filename = ""
-        self.signals_averaging_window = 1
-        self.show_bollinger_bands = False
-        self.csv = None
-        self.bokeh_source = None
-        self.bokeh_source_orig = None
-        self.last_modified = None
-        self.signals = {}
-        self.separate_files = False
-
-    def load_csv(self):
-        pass
-
-    def update_source_and_signals(self):
-        # create bokeh data sources
-        self.bokeh_source_orig = bm.ColumnDataSource(self.csv)
-        self.bokeh_source_orig.data['index'] = self.bokeh_source_orig.data[x_axis]
-
-        if self.bokeh_source is None:
-            self.bokeh_source = bm.ColumnDataSource(self.csv)
-        else:
-            # self.bokeh_source.data = self.bokeh_source_orig.data
-            # smooth the data if necessary
-            self.change_averaging_window(self.signals_averaging_window, force=True)
-
-        # create all the signals
-        if len(self.signals.keys()) == 0:
-            self.signals = {}
-            unique_signal_names = []
-            for name in self.csv.columns:
-                if len(name.split('/')) == 1:
-                    unique_signal_names.append(name)
-                else:
-                    unique_signal_names.append('/'.join(name.split('/')[:-1]))
-            unique_signal_names = list(set(unique_signal_names))
-            for signal_name in unique_signal_names:
-                self.signals[signal_name] = Signal(signal_name, self)
-
-    def load(self):
-        self.load_csv()
-        self.update_source_and_signals()
-
-    def reload_data(self, signals):
-        # this function is a workaround to reload the data of all the signals
-        # if the data doesn't change, bokeh does not refreshes the line
-        self.change_averaging_window(self.signals_averaging_window + 1, force=True)
-        self.change_averaging_window(self.signals_averaging_window - 1, force=True)
-
-    def change_averaging_window(self, new_size, force=False, signals=None):
-        if force or self.signals_averaging_window != new_size:
-            self.signals_averaging_window = new_size
-            win = np.ones(new_size) / new_size
-            temp_data = self.bokeh_source_orig.data.copy()
-            for col in self.bokeh_source.data.keys():
-                if col == 'index' or col in x_axis_options \
-                        or (signals and not any(col in signal for signal in signals)):
-                    temp_data[col] = temp_data[col][:-new_size]
-                    continue
-                temp_data[col] = np.convolve(self.bokeh_source_orig.data[col], win, mode='same')[:-new_size]
-            self.bokeh_source.data = temp_data
-
-            # smooth bollinger bands
-            for signal in self.signals.values():
-                if signal.has_bollinger_bands:
-                    signal.set_bands_source()
-
-    def hide_all_signals(self):
-        for signal_name in self.signals.keys():
-            self.set_signal_selection(signal_name, False)
-
-    def set_signal_selection(self, signal_name, val):
-        self.signals[signal_name].set_selected(val)
-
-    def change_bollinger_bands_state(self, new_state):
-        self.show_bollinger_bands = new_state
-        for signal in self.signals.values():
-            signal.change_bollinger_bands_state(new_state)
-
-    def file_was_modified_on_disk(self):
-        pass
-
-    def get_range_of_selected_signals_on_axis(self, axis, selected_signal=None):
-        max_val = -float('inf')
-        min_val = float('inf')
-        for signal in self.signals.values():
-            if (selected_signal and signal.name == selected_signal) or (signal.selected and signal.axis == axis):
-                max_val = max(max_val, signal.max_val)
-                min_val = min(min_val, signal.min_val)
-        return min_val, max_val
-
-    def get_selected_signals(self):
-        signals = []
-        for signal in self.signals.values():
-            if signal.selected:
-                signals.append(signal)
-        return signals
-
-    def show_files_separately(self, val):
-        pass
-
-
-class SignalsFile(SignalsFileBase):
-    def __init__(self, csv_path, load=True):
-        SignalsFileBase.__init__(self)
-        self.full_csv_path = csv_path
-        self.dir, self.filename, _ = utils.break_file_path(csv_path)
-        if load:
-            self.load()
-            # this helps set the correct x axis
-            self.change_averaging_window(1, force=True)
-
-    def load_csv(self):
-        # load csv and fix sparse data.
-        # csv can be in the middle of being written so we use try - except
-        self.csv = None
-        while self.csv is None:
-            try:
-                self.csv = pd.read_csv(self.full_csv_path)
-                break
-            except pandas_common.EmptyDataError:
-                self.csv = None
-                continue
-        self.csv = self.csv.interpolate()
-        self.csv.fillna(value=0, inplace=True)
-
-        self.last_modified = os.path.getmtime(self.full_csv_path)
-
-    def file_was_modified_on_disk(self):
-        return self.last_modified != os.path.getmtime(self.full_csv_path)
-
-
-class SignalsFilesGroup(SignalsFileBase):
-    def __init__(self, csv_paths):
-        SignalsFileBase.__init__(self)
-        self.full_csv_paths = csv_paths
-        self.signals_files = []
-        if len(csv_paths) == 1 and os.path.isdir(csv_paths[0]):
-            self.signals_files = [SignalsFile(str(file), load=False) for file in add_directory_csv_files(csv_paths[0])]
-        else:
-            for csv_path in csv_paths:
-                if os.path.isdir(csv_path):
-                    self.signals_files.append(SignalsFilesGroup(add_directory_csv_files(csv_path)))
-                else:
-                    self.signals_files.append(SignalsFile(str(csv_path), load=False))
-        if len(csv_paths) == 1:
-            # get the parent directory name (since the current directory is the timestamp directory)
-            self.dir = os.path.abspath(os.path.join(os.path.dirname(csv_paths[0]), '..'))
-        else:
-            # get the common directory for all the experiments
-            self.dir = os.path.dirname(os.path.commonprefix(csv_paths))
-        self.filename = '{} - Group({})'.format(os.path.basename(self.dir), len(self.signals_files))
-        self.load()
-
-        # this helps set the correct x axis
-        self.change_averaging_window(1, force=True)
-
-    def load_csv(self):
-        corrupted_files_idx = []
-        for idx, signal_file in enumerate(self.signals_files):
-            signal_file.load_csv()
-            if not all(option in signal_file.csv.keys() for option in x_axis_options):
-                print("Warning: {} file seems to be corrupted and does contain the necessary columns "
-                      "and will not be rendered".format(signal_file.filename))
-                corrupted_files_idx.append(idx)
-
-        for file_idx in corrupted_files_idx:
-            del self.signals_files[file_idx]
-
-        # get the stats of all the columns
-        csv_group = pd.concat([signals_file.csv for signals_file in self.signals_files])
-        columns_to_remove = [s for s in csv_group.columns if '/Stdev' in s] + \
-                            [s for s in csv_group.columns if '/Min' in s] + \
-                            [s for s in csv_group.columns if '/Max' in s]
-        for col in columns_to_remove:
-            del csv_group[col]
-        csv_group = csv_group.groupby(csv_group.index)
-        self.csv_mean = csv_group.mean()
-        self.csv_mean.columns = [s + '/Mean' for s in self.csv_mean.columns]
-        self.csv_stdev = csv_group.std()
-        self.csv_stdev.columns = [s + '/Stdev' for s in self.csv_stdev.columns]
-        self.csv_min = csv_group.min()
-        self.csv_min.columns = [s + '/Min' for s in self.csv_min.columns]
-        self.csv_max = csv_group.max()
-        self.csv_max.columns = [s + '/Max' for s in self.csv_max.columns]
-
-        # get the indices from the file with the least number of indices and which is not an evaluation worker
-        file_with_min_indices = self.signals_files[0]
-        for signals_file in self.signals_files:
-            if signals_file.csv.shape[0] < file_with_min_indices.csv.shape[0] and \
-                            'Training reward' in signals_file.csv.keys():
-                file_with_min_indices = signals_file
-        self.index_columns = file_with_min_indices.csv[x_axis_options]
-
-        # concat the stats and the indices columns
-        num_rows = file_with_min_indices.csv.shape[0]
-        self.csv = pd.concat([self.index_columns, self.csv_mean.head(num_rows), self.csv_stdev.head(num_rows),
-                              self.csv_min.head(num_rows), self.csv_max.head(num_rows)], axis=1)
-
-        # remove the stat columns for the indices columns
-        columns_to_remove = [s + '/Mean' for s in x_axis_options] + \
-                            [s + '/Stdev' for s in x_axis_options] + \
-                            [s + '/Min' for s in x_axis_options] + \
-                            [s + '/Max' for s in x_axis_options]
-        for col in columns_to_remove:
-            del self.csv[col]
-
-        # remove NaNs
-        self.csv.fillna(value=0, inplace=True)  # removing this line will make bollinger bands fail
-        for key in self.csv.keys():
-            if 'Stdev' in key and 'Evaluation' not in key:
-                self.csv[key] = self.csv[key].fillna(value=0)
-
-        for signal_file in self.signals_files:
-            signal_file.update_source_and_signals()
-
-    def change_averaging_window(self, new_size, force=False, signals=None):
-        for signal_file in self.signals_files:
-            signal_file.change_averaging_window(new_size, force, signals)
-        SignalsFileBase.change_averaging_window(self, new_size, force, signals)
-
-    def set_signal_selection(self, signal_name, val):
-        self.show_files_separately(self.separate_files)
-        SignalsFileBase.set_signal_selection(self, signal_name, val)
-
-    def file_was_modified_on_disk(self):
-        for signal_file in self.signals_files:
-            if signal_file.file_was_modified_on_disk():
-                return True
-        return False
-
-    def show_files_separately(self, val):
-        self.separate_files = val
-        for signal in self.signals.values():
-            if signal.selected:
-                if val:
-                    signal.set_dash("4 4")
-                else:
-                    signal.set_dash("")
-            for signal_file in self.signals_files:
-                try:
-                    if val:
-                        signal_file.set_signal_selection(signal.name, signal.selected)
-                    else:
-                        signal_file.set_signal_selection(signal.name, False)
-                except:
-                    pass
-
-
-class RunType(enum.Enum):
-    SINGLE_FOLDER_SINGLE_FILE = 1
-    SINGLE_FOLDER_MULTIPLE_FILES = 2
-    MULTIPLE_FOLDERS_SINGLE_FILES = 3
-    MULTIPLE_FOLDERS_MULTIPLE_FILES = 4
-    UNKNOWN = 0
-
-
-class FolderType(enum.Enum):
-    SINGLE_FILE = 1
-    MULTIPLE_FILES = 2
-    MULTIPLE_FOLDERS = 3
-    EMPTY = 4
-
-dialog = DialogApp()
-
-# read data
-patches = {}
-signals_files = {}
-selected_file = None
-x_axis = 'Episode #'
-x_axis_options = ['Episode #', 'Total steps', 'Wall-Clock Time']
-current_color = 0
-
-# spinner
-root_dir = os.path.dirname(os.path.abspath(__file__))
-with open(os.path.join(root_dir, 'spinner.css'), 'r') as f:
-    spinner_style = """<style>{}</style>""".format(f.read())
-spinner_html = """<ul class="spinner"><li></li><li></li><li></li><li></li></ul>"""
-spinner = bw.Div(text="""""")
-
-# file refresh time placeholder
-refresh_info = bw.Div(text="""""", width=210)
-
-# create figures
-plot = bp.figure(plot_width=1200, plot_height=800,
-                 tools='pan,box_zoom,wheel_zoom,crosshair,undo,redo,reset,save',
-                 toolbar_location='above', x_axis_label='Episodes',
-                 x_range=bm.Range1d(0, 10000), y_range=bm.Range1d(0, 100000))
-plot.extra_y_ranges = {"secondary": bm.Range1d(start=-100, end=200)}
-plot.add_layout(bm.LinearAxis(y_range_name="secondary"), 'right')
-
-# legend
-div = bw.Div(text="""""")
-legend = bl.widgetbox([div])
-
-bokeh_legend = bm.Legend(
-    items=[("12345678901234567890123456789012345678901234567890", [])],  # 50 letters
-    # items=[("                                                  ", [])],  # 50 letters
-    location=(-20, 0), orientation="vertical",
-    border_line_color="black",
-    label_text_font_size={'value': '9pt'},
-    margin=30
-)
-plot.add_layout(bokeh_legend, "right")
-
-
-def update_axis_range(name, range_placeholder):
-    max_val = -float('inf')
-    min_val = float('inf')
-    selected_signal = None
-    if name in x_axis_options:
-        selected_signal = name
-    for signals_file in signals_files.values():
-        curr_min_val, curr_max_val = signals_file.get_range_of_selected_signals_on_axis(name, selected_signal)
-        max_val = max(max_val, curr_max_val)
-        min_val = min(min_val, curr_min_val)
-    if min_val != float('inf'):
-        range = max_val - min_val
-        range_placeholder.start = min_val - 0.1 * range
-        range_placeholder.end = max_val + 0.1 * range
-
-
-# update axes ranges
-def update_ranges():
-    update_axis_range('default', plot.y_range)
-    update_axis_range('secondary', plot.extra_y_ranges['secondary'])
-
-
-def get_all_selected_signals():
-    signals = []
-    for signals_file in signals_files.values():
-        signals += signals_file.get_selected_signals()
-    return signals
-
-
-# update legend using the legend text dictionary
-def update_legend():
-    legend_text = """<div></div>"""
-    selected_signals = get_all_selected_signals()
-    items = []
-    for signal in selected_signals:
-        side_sign = "<" if signal.axis == 'default' else ">"
-        legend_text += """<div style='color: {}'><b>{} {}</b></div>"""\
-                       .format(signal.color, side_sign, signal.full_name)
-        items.append((signal.full_name, [signal.line]))
-    div.text = legend_text
-    # the visible=false => visible=true is a hack to make the legend render again
-    bokeh_legend.visible = False
-    bokeh_legend.items = items
-    bokeh_legend.visible = True
-
-
-# select lines to display
-def select_data(args, old, new):
-    if selected_file is None:
-        return
-    show_spinner()
-    selected_signals = new
-    for signal_name in selected_file.signals.keys():
-        is_selected = signal_name in selected_signals
-        selected_file.set_signal_selection(signal_name, is_selected)
-
-    # update axes ranges
-    update_ranges()
-    update_axis_range(x_axis, plot.x_range)
-
-    # update the legend
-    update_legend()
-
-    hide_spinner()
-
-
-# add new lines to the plot
-def plot_signals(signals_file, signals):
-    for idx, signal in enumerate(signals):
-        signal.line = plot.line('index', signal.name, source=signals_file.bokeh_source,
-                                line_color=signal.color, line_width=2)
-
-
-def open_file_dialog():
-    return dialog.getFileDialog()
-
-
-def open_directory_dialog():
-    return dialog.getDirDialog()
-
-
-def show_spinner():
-    spinner.text = spinner_style + spinner_html
-
-
-def hide_spinner():
-    spinner.text = ""
-
-
-# will create a group from the files
-def create_files_group_signal(files):
-    global selected_file
-    signals_file = SignalsFilesGroup(files)
-    signals_files[signals_file.filename] = signals_file
-
-    filenames = [signals_file.filename]
-    files_selector.options += filenames
-    files_selector.value = filenames[0]
-    selected_file = signals_file
-
-
-# load files from disk as a group
-def load_files_group():
-    show_spinner()
-    files = open_file_dialog()
-    # no files selected
-    if not files or not files[0]:
-        hide_spinner()
-        return
-
-    change_displayed_doc()
-
-    if len(files) == 1:
-        create_files_signal(files)
-    else:
-        create_files_group_signal(files)
-
-    change_selected_signals_in_data_selector([""])
-    hide_spinner()
-
-
-# classify the folder as containing a single file, multiple files or only folders
-def classify_folder(dir_path):
-    files = [f for f in os.listdir(dir_path) if os.path.isfile(os.path.join(dir_path, f)) and f.endswith('.csv')]
-    folders = [d for d in os.listdir(dir_path) if os.path.isdir(os.path.join(dir_path, d))]
-    if len(files) == 1:
-        return FolderType.SINGLE_FILE
-    elif len(files) > 1:
-        return FolderType.MULTIPLE_FILES
-    elif len(folders) >= 1:
-        return FolderType.MULTIPLE_FOLDERS
-    else:
-        return FolderType.EMPTY
-
-
-# finds if this is single-threaded or multi-threaded
-def get_run_type(dir_path):
-    folder_type = classify_folder(dir_path)
-    if folder_type == FolderType.SINGLE_FILE:
-        return RunType.SINGLE_FOLDER_SINGLE_FILE
-
-    elif folder_type == FolderType.MULTIPLE_FILES:
-        return RunType.SINGLE_FOLDER_MULTIPLE_FILES
-
-    elif folder_type == FolderType.MULTIPLE_FOLDERS:
-        # folder contains sub dirs -> we assume we can classify the folder using only the first sub dir
-        sub_dirs = [d for d in os.listdir(dir_path) if os.path.isdir(os.path.join(dir_path, d))]
-
-        # checking only the first folder in the root dir for its type, since we assume that all sub dirs will share the
-        # same structure (i.e. if one is a result of multi-threaded run, so will all the other).
-        folder_type = classify_folder(os.path.join(dir_path, sub_dirs[0]))
-        if folder_type == FolderType.SINGLE_FILE:
-            folder_type = RunType.MULTIPLE_FOLDERS_SINGLE_FILES
-        elif folder_type == FolderType.MULTIPLE_FILES:
-            folder_type = RunType.MULTIPLE_FOLDERS_MULTIPLE_FILES
-    return folder_type
-
-
-# takes path to dir and recursively adds all it's files to paths
-def add_directory_csv_files(dir_path, paths=None):
-    if not paths:
-        paths = []
-
-    for p in os.listdir(dir_path):
-        path = os.path.join(dir_path, p)
-        if os.path.isdir(path):
-            # call recursively for each dir
-            paths = add_directory_csv_files(path, paths)
-        elif os.path.isfile(path) and path.endswith('.csv'):
-            # add every file to the list
-            paths.append(path)
-
-    return paths
-
-
-# create a signal file from the directory path according to the directory underlying structure
-def handle_dir(dir_path, run_type):
-    paths = add_directory_csv_files(dir_path)
-    if run_type == RunType.SINGLE_FOLDER_SINGLE_FILE:
-        create_files_signal(paths)
-    elif run_type == RunType.SINGLE_FOLDER_MULTIPLE_FILES:
-        create_files_group_signal(paths)
-    elif run_type == RunType.MULTIPLE_FOLDERS_SINGLE_FILES:
-        create_files_group_signal(paths)
-    elif run_type == RunType.MULTIPLE_FOLDERS_MULTIPLE_FILES:
-        sub_dirs = [d for d in os.listdir(dir_path) if os.path.isdir(os.path.join(dir_path, d))]
-        # for d in sub_dirs:
-        #     paths = add_directory_csv_files(os.path.join(dir_path, d))
-        #     create_files_group_signal(paths)
-        create_files_group_signal([os.path.join(dir_path, d) for d in sub_dirs])
-
-
-# load directory from disk as a group
-def load_directory_group():
-    global selected_file
-    show_spinner()
-    directory = open_directory_dialog()
-    # no files selected
-    if not directory:
-        hide_spinner()
-        return
-
-    change_displayed_doc()
-
-    handle_dir(directory, get_run_type(directory))
-
-    change_selected_signals_in_data_selector([""])
-    hide_spinner()
-
-
-def create_files_signal(files):
-    global selected_file
-    new_signal_files = []
-    for idx, file_path in enumerate(files):
-        signals_file = SignalsFile(str(file_path))
-        signals_files[signals_file.filename] = signals_file
-        new_signal_files.append(signals_file)
-
-    filenames = [f.filename for f in new_signal_files]
-
-    files_selector.options += filenames
-    files_selector.value = filenames[0]
-    selected_file = new_signal_files[0]
-
-
-# load files from disk
-def load_files():
-    show_spinner()
-    files = open_file_dialog()
-
-    # no files selected
-    if not files or not files[0]:
-        hide_spinner()
-        return
-
-    create_files_signal(files)
-    hide_spinner()
-
-    change_selected_signals_in_data_selector([""])
-
-
-def unload_file():
-    global selected_file
-    global signals_files
-    if selected_file is None:
-        return
-    selected_file.hide_all_signals()
-    del signals_files[selected_file.filename]
-    data_selector.options = [""]
-    filenames = itertools.cycle(files_selector.options)
-    files_selector.options.remove(selected_file.filename)
-    if len(files_selector.options) > 0:
-        files_selector.value = next(filenames)
-    else:
-        files_selector.value = None
-    update_legend()
-    refresh_info.text = ""
-
-
-# reload the selected csv file
-def reload_all_files(force=False):
-    for file_to_load in signals_files.values():
-        if force or file_to_load.file_was_modified_on_disk():
-            file_to_load.load()
-        refresh_info.text = "last update: " + str(datetime.datetime.now()).split(".")[0]
-
-
-# unselect the currently selected signals and then select the requested signals in the data selector
-def change_selected_signals_in_data_selector(selected_signals):
-    # the default bokeh way is not working due to a bug since Bokeh 0.12.6 (https://github.com/bokeh/bokeh/issues/6501)
-    # this will currently cause the signals to change color
-    for value in list(data_selector.value):
-        if value in data_selector.options:
-            index = data_selector.options.index(value)
-            data_selector.options.remove(value)
-            data_selector.value.remove(value)
-            data_selector.options.insert(index, value)
-    data_selector.value = selected_signals
-
-
-# change data options according to the selected file
-def change_data_selector(args, old, new):
-    global selected_file
-    if new is None:
-        selected_file = None
-        return
-    show_spinner()
-    selected_file = signals_files[new]
-    data_selector.options = sorted(list(selected_file.signals.keys()))
-    selected_signal_names = [s.name for s in selected_file.signals.values() if s.selected]
-    if not selected_signal_names:
-        selected_signal_names = [""]
-    change_selected_signals_in_data_selector(selected_signal_names)
-    averaging_slider.value = selected_file.signals_averaging_window
-    group_cb.active = [0 if selected_file.show_bollinger_bands else None]
-    group_cb.active += [1 if selected_file.separate_files else None]
-    hide_spinner()
-
-
-# smooth all the signals of the selected file
-def update_averaging(args, old, new):
-    show_spinner()
-    selected_file.change_averaging_window(new)
-    hide_spinner()
-
-
-def change_x_axis(val):
-    global x_axis
-    show_spinner()
-    x_axis = x_axis_options[val]
-    plot.xaxis.axis_label = x_axis
-    reload_all_files(force=True)
-    update_axis_range(x_axis, plot.x_range)
-    hide_spinner()
-
-
-# move the signal between the main and secondary Y axes
-def toggle_second_axis():
-    show_spinner()
-    signals = selected_file.get_selected_signals()
-    for signal in signals:
-        signal.toggle_axis()
-
-    update_ranges()
-    update_legend()
-
-    # this is just for redrawing the signals
-    selected_file.reload_data([signal.name for signal in signals])
-
-    hide_spinner()
-
-
-def toggle_group_property(new):
-    # toggle show / hide Bollinger bands
-    selected_file.change_bollinger_bands_state(0 in new)
-
-    # show a separate signal for each file in a group
-    selected_file.show_files_separately(1 in new)
-
-
-def change_displayed_doc():
-    if doc.roots[0] == landing_page:
-        doc.remove_root(landing_page)
-        doc.add_root(layout)
-
-
-# Color selection - most of these functions are taken from bokeh examples (plotting/color_sliders.py)
-def select_color(attr, old, new):
-    show_spinner()
-    signals = selected_file.get_selected_signals()
-    for signal in signals:
-        signal.set_color(rgb_to_hex(crRGBs[new['1d']['indices'][0]]))
-    hide_spinner()
-
-
-def generate_color_range(N, I):
-    HSV_tuples = [(x*1.0/N, 0.5, I) for x in range(N)]
-    RGB_tuples = map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples)
-    for_conversion = []
-    for RGB_tuple in RGB_tuples:
-        for_conversion.append((int(RGB_tuple[0]*255), int(RGB_tuple[1]*255), int(RGB_tuple[2]*255)))
-    hex_colors = [rgb_to_hex(RGB_tuple) for RGB_tuple in for_conversion]
-    return hex_colors, for_conversion
-
-
-# convert RGB tuple to hexadecimal code
-def rgb_to_hex(rgb):
-    return '#%02x%02x%02x' % rgb
-
-
-# convert hexadecimal to RGB tuple
-def hex_to_dec(hex):
-    red = ''.join(hex.strip('#')[0:2])
-    green = ''.join(hex.strip('#')[2:4])
-    blue = ''.join(hex.strip('#')[4:6])
-    return int(red, 16), int(green, 16), int(blue,16)
-
-color_resolution = 1000
-brightness = 0.75  # change to have brighter/darker colors
-crx = list(range(1, color_resolution+1))  # the resolution is 1000 colors
-cry = [5 for i in range(len(crx))]
-crcolor, crRGBs = generate_color_range(color_resolution, brightness)  # produce spectrum
-
-
-# ---------------- Build Website Layout -------------------
-
-# select file
-file_selection_button = bw.Button(label="Select Files", button_type="success", width=120)
-file_selection_button.on_click(load_files_group)
-
-files_selector_spacer = bl.Spacer(width=10)
-
-group_selection_button = bw.Button(label="Select Directory", button_type="primary", width=140)
-group_selection_button.on_click(load_directory_group)
-
-unload_file_button = bw.Button(label="Unload", button_type="danger", width=50)
-unload_file_button.on_click(unload_file)
-
-# files selection box
-files_selector = bw.Select(title="Files:", options=[], width=200)
-files_selector.on_change('value', change_data_selector)
-
-# data selection box
-data_selector = bw.MultiSelect(title="Data:", options=[], size=12)
-data_selector.on_change('value', select_data)
-
-# x axis selection box
-x_axis_selector_title = bw.Div(text="""X Axis:""")
-x_axis_selector = bw.RadioButtonGroup(labels=x_axis_options, active=0)
-x_axis_selector.on_click(change_x_axis)
-
-# toggle second axis bw.button
-toggle_second_axis_button = bw.Button(label="Toggle Second Axis", button_type="success")
-toggle_second_axis_button.on_click(toggle_second_axis)
-
-# averaging slider
-averaging_slider = bw.Slider(title="Averaging window", start=1, end=101, step=10)
-averaging_slider.on_change('value', update_averaging)
-
-# group properties checkbox
-group_cb = bw.CheckboxGroup(labels=["Show statistics bands", "Ungroup signals"], active=[])
-group_cb.on_click(toggle_group_property)
-
-# color selector
-color_selector_title = bw.Div(text="""Select Color:""")
-crsource = bm.ColumnDataSource(data=dict(x=crx, y=cry, crcolor=crcolor, RGBs=crRGBs))
-color_selector = bp.figure(x_range=(0, color_resolution), y_range=(0, 10),
-                           plot_width=300, plot_height=40,
-                           tools='tap')
-color_selector.axis.visible = False
-color_range = color_selector.rect(x='x', y='y', width=1, height=10,
-                                  color='crcolor', source=crsource)
-crsource.on_change('selected', select_color)
-color_range.nonselection_glyph = color_range.glyph
-color_selector.toolbar.logo = None
-color_selector.toolbar_location = None
-
-# title
-title = bw.Div(text="""<h1>Coach Dashboard</h1>""")
-
-# landing page
-landing_page_description = bw.Div(text="""<h3>Start by selecting an experiment file or directory to open:</h3>""")
-center = bw.Div(text="""<style>html { text-align: center; } </style>""")
-center_buttons = bw.Div(text="""<style>.bk-grid-row .bk-layout-fixed { margin: 0 auto; }</style>""", width=0)
-landing_page = bl.column(center,
-                      title,
-                      landing_page_description,
-                      bl.row(center_buttons),
-                      bl.row(file_selection_button, sizing_mode='scale_width'),
-                      bl.row(group_selection_button, sizing_mode='scale_width'),
-                      sizing_mode='scale_width')
-
-# main layout of the document
-layout = bl.row(file_selection_button, files_selector_spacer, group_selection_button, width=300)
-layout = bl.column(layout, files_selector)
-layout = bl.column(layout, bl.row(refresh_info, unload_file_button))
-layout = bl.column(layout, data_selector)
-layout = bl.column(layout, color_selector_title)
-layout = bl.column(layout, color_selector)
-layout = bl.column(layout, x_axis_selector_title)
-layout = bl.column(layout, x_axis_selector)
-layout = bl.column(layout, group_cb)
-layout = bl.column(layout, toggle_second_axis_button)
-layout = bl.column(layout, averaging_slider)
-# layout = bl.column(layout, legend)
-layout = bl.row(layout, plot)
-layout = bl.column(title, layout)
-layout = bl.column(layout, spinner)
-
-doc = bp.curdoc()
-doc.add_root(landing_page)
-
-doc.add_periodic_callback(reload_all_files, 20000)
-plot.y_range = bm.Range1d(0, 100)
-plot.extra_y_ranges['secondary'] = bm.Range1d(0, 100)
-
-# show load file dialog immediately on start
-#doc.add_timeout_callback(load_files, 1000)
-
-if __name__ == "__main__":
-    # find an open port and run the server
-    import socket
-    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-    port = 12345
-    while True:
-        try:
-            s.bind(("127.0.0.1", port))
-            break
-        except socket.error as e:
-            if e.errno == 98:
-                port += 1
-    s.close()
-    os.system('bokeh serve --show dashboard.py --port {}'.format(port))

debug_utils.py

@@ -1,49 +0,0 @@
-#
-# 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.
-#
-import matplotlib.pyplot as plt
-import numpy as np
-
-
-def show_observation_stack(stack, channels_last=False):
-    if isinstance(stack, list):  # is list
-        stack_size = len(stack)
-    elif len(stack.shape) == 3:
-        stack_size = stack.shape[0]  # is numpy array
-    elif len(stack.shape) == 4:
-        stack_size = stack.shape[1]  # ignore batch dimension
-        stack = stack[0]
-    else:
-        assert False, ""
-
-    if channels_last:
-        stack = np.transpose(stack, (2, 0, 1))
-        stack_size = stack.shape[0]
-
-    for i in range(stack_size):
-        plt.subplot(1, stack_size, i + 1)
-        plt.imshow(stack[i], cmap='gray')
-
-    plt.show()
-
-
-def show_diff_between_two_observations(observation1, observation2):
-    plt.imshow(observation1 - observation2, cmap='gray')
-    plt.show()
-
-
-def plot_grayscale_observation(observation):
-    plt.imshow(observation, cmap='gray')
-    plt.show()

install.sh

@@ -1,205 +0,0 @@
-#!/bin/bash -e
-
-prompt () {
-    # prints a yes / no question to the user and returns the answer
-    # first argument is the prompt question
-    # second argument is the default answer - Y / N
-    local default_answer
-
-    # set the default value
-    case "${2}" in
-        y|Y ) default_answer=1; options="[Y/n]";;
-        n|N ) default_answer=0; options="[y/N]";;
-        "" ) default_answer=; options="[y/n]";;
-        * ) echo "invalid default value"; exit;;
-    esac
-
-    while true; do
-        # read the user choice
-        read -p "${1} ${options} " choice
-
-        # return the choice or the default value if an enter was pressed
-        case "${choice}" in
-            y|Y ) retval=1; return;;
-            n|N ) retval=0; return;;
-            "" ) if [ ! -z "${default_answer}" ]; then retval=${default_answer}; return; fi;;
-        esac
-    done
-}
-
-add_to_bashrc () {
-    # adds an env variable to the bashrc
-    # first argument is the variable name
-    # second argument is the variable value
-
-    EXISTS_IN_BASHRC=`awk  '/${2}/{print $1}' ~/.bashrc`
-    if [ "${EXISTS_IN_BASHRC}" == "" ]; then
-        echo "export ${1}=${2}" >> ~/.bashrc
-    fi
-}
-
-GET_PREFERENCES_MANUALLY=1
-
-INSTALL_COACH=0
-INSTALL_DASHBOARD=0
-INSTALL_GYM=0
-INSTALL_NEON=0
-INSTALL_VIRTUAL_ENVIRONMENT=1
-
-# Get user preferences
-TEMP=`getopt -o cpgvrmeNndh \
-             --long coach,dashboard,gym,no_virtual_environment,neon,debug,help \
-             -- "$@"`
-eval set -- "$TEMP"
-while true; do
-#for i in "$@"
-    case ${1} in
-        -c|--coach)
-            INSTALL_COACH=1
-            GET_PREFERENCES_MANUALLY=0
-            shift;;
-        -p|--dashboard)
-            INSTALL_DASHBOARD=1
-            GET_PREFERENCES_MANUALLY=0;
-            shift;;
-        -g|--gym)
-            INSTALL_GYM=1
-            GET_PREFERENCES_MANUALLY=0;
-            shift;;
-        -N|--no_virtual_environment)
-            INSTALL_VIRTUAL_ENVIRONMENT=0
-            GET_PREFERENCES_MANUALLY=0;
-            shift;;
-        -ne|--neon)
-            INSTALL_NEON=1
-            GET_PREFERENCES_MANUALLY=0;
-            shift;;
-        -d|--debug) set -x; shift;;
-        -h|--help)
-            echo "Available command line arguments:"
-            echo ""
-            echo "   -c | --coach                  - Install Coach requirements"
-            echo "   -p | --dashboard              - Install Dashboard requirements"
-            echo "   -g | --gym                    - Install Gym support"
-            echo "   -N | --no_virtual_environment - Do not install inside of a virtual environment"
-            echo "   -d | --debug                  - Run in debug mode"
-            echo "   -h | --help                   - Display this help message"
-            echo ""
-            exit;;
-        --) shift; break;;
-        *) break;; # unknown option;;
-    esac
-done
-
-if [ ${GET_PREFERENCES_MANUALLY} -eq 1 ]; then
-    prompt "Install Coach requirements?" Y
-    INSTALL_COACH=${retval}
-
-    prompt "Install Dashboard requirements?" Y
-    INSTALL_DASHBOARD=${retval}
-
-    prompt "Install Gym support?" Y
-    INSTALL_GYM=${retval}
-
-    prompt "Install neon support?" Y
-    INSTALL_NEON=${retval}
-fi
-
-IN_VIRTUAL_ENV=`python3 -c 'import sys; print("%i" % hasattr(sys, "real_prefix"))'`
-
-# basic installations
-sudo -E apt-get install python3-pip cmake zlib1g-dev python3-tk python-opencv -y
-pip3 install --upgrade pip
-
-# if we are not in a virtual environment, we will create one with the appropriate python version and then activate it
-# if we are already in a virtual environment,
-
-if [ ${INSTALL_VIRTUAL_ENVIRONMENT} -eq 1 ]; then
-    if [ ${IN_VIRTUAL_ENV} -eq 0 ]; then
-        sudo -E pip3 install virtualenv
-        virtualenv -p python3 coach_env
-        . coach_env/bin/activate
-    fi
-fi
-
-#------------------------------------------------
-# From now on we are in a virtual environment
-#------------------------------------------------
-
-# get python local and global paths
-python_version=python$(python -c "import sys; print (str(sys.version_info[0])+'.'+str(sys.version_info[1]))")
-var=( $(which -a $python_version) )
-get_python_lib_cmd="from distutils.sysconfig import get_python_lib; print (get_python_lib())"
-lib_virtualenv_path=$(python -c "$get_python_lib_cmd")
-lib_system_path=$(${var[-1]} -c "$get_python_lib_cmd")
-
-# Boost libraries
-sudo -E apt-get install libboost-all-dev -y
-
-# Coach
-if [ ${INSTALL_COACH} -eq 1 ]; then
-    echo "Installing Coach requirements"
-    pip3 install -r ./requirements_coach.txt
-fi
-
-# Dashboard
-if [ ${INSTALL_DASHBOARD} -eq 1 ]; then
-    echo "Installing Dashboard requirements"
-    pip3 install -r ./requirements_dashboard.txt
-    sudo -E apt-get install dpkg-dev build-essential python3.5-dev libjpeg-dev  libtiff-dev libsdl1.2-dev libnotify-dev \
-    freeglut3 freeglut3-dev libsm-dev libgtk2.0-dev libgtk-3-dev libwebkitgtk-dev libgtk-3-dev libwebkitgtk-3.0-dev libgstreamer-plugins-base1.0-dev -y
-
-    sudo -E -H pip3 install -U --pre -f \
-    https://wxpython.org/Phoenix/snapshot-builds/linux/gtk3/ubuntu-16.04/wxPython-4.0.0a3.dev3059+4a5c5d9-cp35-cp35m-linux_x86_64.whl  wxPython
-
-    # link wxPython Phoenix library into the virtualenv since it is installed with apt-get and not accessible
-    libs=( wx )
-    for lib in ${libs[@]}
-    do
-        ln -sf $lib_system_path/$lib $lib_virtualenv_path/$lib
-    done
-fi
-
-# Gym
-if [ ${INSTALL_GYM} -eq 1 ]; then
-    echo "Installing Gym support"
-    sudo -E apt-get install libav-tools libsdl2-dev swig cmake -y
-    pip3 install box2d # for bipedal walker etc.
-    pip3 install gym
-fi
-
-# NGraph and Neon
-if [ ${INSTALL_NEON} -eq 1 ]; then
-    echo "Installing neon requirements"
-
-    # MKL
-    git clone https://github.com/01org/mkl-dnn.git
-    cd mkl-dnn
-    cd scripts && ./prepare_mkl.sh && cd ..
-    mkdir -p build && cd build && cmake .. && make -j
-    sudo make install -j
-    cd ../..
-    export MKLDNN_ROOT=/usr/local/
-    add_to_bashrc MKLDNN_ROOT ${MKLDNN_ROOT}
-    export LD_LIBRARY_PATH=$MKLDNN_ROOT/lib:$LD_LIBRARY_PATH
-    add_to_bashrc LD_LIBRARY_PATH ${MKLDNN_ROOT}/lib:$LD_LIBRARY_PATH
-
-    # NGraph
-    git clone https://github.com/NervanaSystems/ngraph.git
-    cd ngraph
-    make install -j
-    cd ..
-
-    # Neon
-    sudo -E apt-get install libhdf5-dev libyaml-dev pkg-config clang virtualenv libcurl4-openssl-dev libopencv-dev libsox-dev -y
-    pip3 install nervananeon
-fi
-
-if ! [ -x "$(command -v nvidia-smi)" ]; then
-    # Intel Optimized TensorFlow
-    #pip3 install https://anaconda.org/intel/tensorflow/1.3.0/download/tensorflow-1.3.0-cp35-cp35m-linux_x86_64.whl
-    pip3 install https://anaconda.org/intel/tensorflow/1.4.0/download/tensorflow-1.4.0-cp35-cp35m-linux_x86_64.whl
-else
-    # GPU supported TensorFlow
-    pip3 install tensorflow-gpu==1.4.1
-fi

requirements.txt

@@ -7,3 +7,5 @@ pygame==1.9.3
 PyOpenGL==3.1.0
 scipy==0.19.0
 scikit-image==0.13.0
+gym
+tensorflow

scripts/coach

@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+import argparse
 import atexit
 import json
 import os
@@ -22,13 +23,12 @@ import subprocess
 import sys
 import time
 
-import agents
+from coach import agents  # noqa
-import argparse
+from coach import configurations as conf
-import configurations as conf
+from coach import environments
-import environments
+from coach import logger
-import logger
+from coach import presets
-import presets
+from coach import utils
-import utils
 
 
 if len(set(logger.failed_imports)) > 0:

scripts/parallel_actor.py

@@ -20,10 +20,10 @@ import time
 
 import tensorflow as tf
 
-import agents
+from coach import agents
-import environments
+from coach import environments
-import logger
+from coach import logger
-import presets
+from coach import presets
 
 start_time = time.time()
 

setup.py

@@ -0,0 +1,28 @@
+#!/usr/bin/env python3
+"""
+Setup for the coach project
+"""
+from setuptools import setup, find_packages
+
+
+setup(name="coach",
+      version="0.7",
+      description="Reinforcement Learning Coach",
+      author="Caspi, Itai and Leibovich, Gal and Novik, Gal",
+      author_email="gal.novik@intel.com",
+      url="https://github.com/NervanaSystems/coach",
+      packages=find_packages(),
+      download_url="https://github.com/NervanaSystems/coach",
+      keywords=["reinforcement", "machine", "learning"],
+      install_requires=["annoy", "Pillow", "matplotlib", "numpy", "pandas",
+                        "pygame", "PyOpenGL", "scipy", "scikit-image",
+                        "tensorflow", "gym"],
+      scripts=["scripts/coach"],
+      classifiers=["Programming Language :: Python :: 3",
+                   "Development Status :: 4 - Beta",
+                   "Environment :: Console",
+                   "Intended Audience :: End Users/Desktop",
+                   "License :: OSI Approved :: Apache Software License",
+                   "Operating System :: OS Independent",
+                   "Topic :: Scientific/Engineering :: Artificial "
+                   "Intelligence"])