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Cleanup imports.

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Till now, most of the modules were importing all of the module objects
(variables, classes, functions, other imports) into module namespace,
which potentially could (and was) cause of unintentional use of class or
methods, which was indirect imported.

With this patch, all the star imports were substituted with top-level
module, which provides desired class or function.

Besides, all imports where sorted (where possible) in a way pep8[1]
suggests - first are imports from standard library, than goes third
party imports (like numpy, tensorflow etc) and finally coach modules.
All of those sections are separated by one empty line.

[1] https://www.python.org/dev/peps/pep-0008/#imports
This commit is contained in:
Roman Dobosz
2018-04-12 19:46:32 +02:00
parent cafa152382
commit 1b095aeeca
75 changed files with 1169 additions and 1139 deletions
Download Patch File Download Diff File Expand all files Collapse all files

37
agents/policy_gradients_agent.py
View File

@@ -13,25 +13,18 @@
# See the License for the specific language governing permissions and
# limitations under the License.
#
from agents.policy_optimization_agent import *
import numpy as np
from logger import *
import tensorflow as tf
try:
import matplotlib.pyplot as plt
except:
from logger import failed_imports
failed_imports.append("matplotlib")
from utils import *
from agents import policy_optimization_agent as poa
import logger
import utils
class PolicyGradientsAgent(PolicyOptimizationAgent):
class PolicyGradientsAgent(poa.PolicyOptimizationAgent):
def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0):
PolicyOptimizationAgent.__init__(self, env, tuning_parameters, replicated_device, thread_id)
self.returns_mean = Signal('Returns Mean')
self.returns_variance = Signal('Returns Variance')
poa.PolicyOptimizationAgent.__init__(self, env, tuning_parameters, replicated_device, thread_id)
self.returns_mean = utils.Signal('Returns Mean')
self.returns_variance = utils.Signal('Returns Variance')
self.signals.append(self.returns_mean)
self.signals.append(self.returns_variance)
self.last_gradient_update_step_idx = 0
@@ -41,21 +34,21 @@ class PolicyGradientsAgent(PolicyOptimizationAgent):
current_states, next_states, actions, rewards, game_overs, total_returns = self.extract_batch(batch)
for i in reversed(range(len(total_returns))):
if self.policy_gradient_rescaler == PolicyGradientRescaler.TOTAL_RETURN:
if self.policy_gradient_rescaler == poa.PolicyGradientRescaler.TOTAL_RETURN:
total_returns[i] = total_returns[0]
elif self.policy_gradient_rescaler == PolicyGradientRescaler.FUTURE_RETURN:
elif self.policy_gradient_rescaler == poa.PolicyGradientRescaler.FUTURE_RETURN:
# just take the total return as it is
pass
elif self.policy_gradient_rescaler == PolicyGradientRescaler.FUTURE_RETURN_NORMALIZED_BY_EPISODE:
elif self.policy_gradient_rescaler == poa.PolicyGradientRescaler.FUTURE_RETURN_NORMALIZED_BY_EPISODE:
# we can get a single transition episode while playing Doom Basic, causing the std to be 0
if self.std_discounted_return != 0:
total_returns[i] = (total_returns[i] - self.mean_discounted_return) / self.std_discounted_return
else:
total_returns[i] = 0
elif self.policy_gradient_rescaler == PolicyGradientRescaler.FUTURE_RETURN_NORMALIZED_BY_TIMESTEP:
elif self.policy_gradient_rescaler == poa.PolicyGradientRescaler.FUTURE_RETURN_NORMALIZED_BY_TIMESTEP:
total_returns[i] -= self.mean_return_over_multiple_episodes[i]
else:
screen.warning("WARNING: The requested policy gradient rescaler is not available")
logger.screen.warning("WARNING: The requested policy gradient rescaler is not available")
targets = total_returns
if not self.env.discrete_controls and len(actions.shape) < 2:
@@ -69,12 +62,12 @@ class PolicyGradientsAgent(PolicyOptimizationAgent):
return total_loss
def choose_action(self, curr_state, phase=RunPhase.TRAIN):
def choose_action(self, curr_state, phase=utils.RunPhase.TRAIN):
# convert to batch so we can run it through the network
if self.env.discrete_controls:
# DISCRETE
action_values = self.main_network.online_network.predict(self.tf_input_state(curr_state)).squeeze()
if phase == RunPhase.TRAIN:
if phase == utils.RunPhase.TRAIN:
action = self.exploration_policy.get_action(action_values)
else:
action = np.argmax(action_values)
@@ -84,7 +77,7 @@ class PolicyGradientsAgent(PolicyOptimizationAgent):
# CONTINUOUS
result = self.main_network.online_network.predict(self.tf_input_state(curr_state))
action_values = result[0].squeeze()
if phase == RunPhase.TRAIN:
if phase == utils.RunPhase.TRAIN:
action = self.exploration_policy.get_action(action_values)
else:
action = action_values