From f428cd41e145558caad124da3d9023e8d3982d55 Mon Sep 17 00:00:00 2001
From: Ashish Gaurav <agaurav77@yahoo.com>
Date: Mon, 19 Nov 2018 18:18:47 -0500
Subject: [PATCH] run docformatter
---
backends/baselines_learner.py | 5 +-
backends/controller_base.py | 11 ++--
backends/kerasrl_learner.py | 4 +-
backends/manual_policy.py | 4 +-
backends/mcts_learner.py | 53 +++++++++----------
backends/online_mcts_controller.py | 2 +-
backends/policy_base.py | 4 +-
env/env_base.py | 31 +++++------
env/road_env.py | 6 +--
env/simple_intersection/features.py | 4 +-
env/simple_intersection/shapes.py | 2 +-
.../simple_intersection_env.py | 39 +++++++-------
env/simple_intersection/utilities.py | 7 +--
high_level_policy_main.py | 4 +-
low_level_policy_main.py | 4 +-
mcts.py | 16 +++---
model_checker/LTL_property_base.py | 22 ++++----
model_checker/atomic_propositions_base.py | 11 ++--
model_checker/parser.py | 32 ++++++-----
model_checker/simple_intersection/classes.py | 12 ++---
options/options_loader.py | 28 +++++-----
options/simple_intersection/maneuver_base.py | 40 ++++++++------
ppo2_training.py | 3 +-
23 files changed, 175 insertions(+), 169 deletions(-)
diff --git a/backends/baselines_learner.py b/backends/baselines_learner.py
index 8a62830..ab4e4da 100644
--- a/backends/baselines_learner.py
+++ b/backends/baselines_learner.py
@@ -48,10 +48,9 @@ class PPO2Agent(LearnerBase):
return MlpPolicy
def create_agent(self, policy, tensorboard):
- """Creates a PPO agent
+ """Creates a PPO agent.
- Returns:
- stable_baselines PPO2 object
+ Returns: stable_baselines PPO2 object
"""
if tensorboard:
return PPO2(
diff --git a/backends/controller_base.py b/backends/controller_base.py
index b9bbc35..885f941 100644
--- a/backends/controller_base.py
+++ b/backends/controller_base.py
@@ -18,16 +18,17 @@ class ControllerBase(PolicyBase):
setattr(self, prop, kwargs.get(prop, default))
def can_transition(self):
- """Returns boolean signifying whether we can transition. To be
- implemented in subclass.
+ """Returns boolean signifying whether we can transition.
+
+ To be implemented in subclass.
"""
raise NotImplemented(self.__class__.__name__ + \
"can_transition is not implemented.")
def do_transition(self, observation):
- """Do a transition, assuming we can transition. To be
- implemented in subclass.
+ """Do a transition, assuming we can transition. To be implemented in
+ subclass.
Args:
observation: final observation from episodic step
@@ -37,7 +38,7 @@ class ControllerBase(PolicyBase):
"do_transition is not implemented.")
def set_current_node(self, node_alias):
- """Sets the current node which is being executed
+ """Sets the current node which is being executed.
Args:
node: node alias of the node to be set
diff --git a/backends/kerasrl_learner.py b/backends/kerasrl_learner.py
index d8138bb..a98dc92 100644
--- a/backends/kerasrl_learner.py
+++ b/backends/kerasrl_learner.py
@@ -431,8 +431,8 @@ class DQNAgentOverOptions(DQNAgent):
self.low_level_policies.keys())
def __get_invalid_node_indices(self):
- """Returns a list of option indices that are invalid according to initiation conditions.
- """
+ """Returns a list of option indices that are invalid according to
+ initiation conditions."""
invalid_node_indices = list()
for index, option_alias in enumerate(self.low_level_policy_aliases):
self.low_level_policies[option_alias].reset_maneuver()
diff --git a/backends/manual_policy.py b/backends/manual_policy.py
index 4b891e7..6e59db1 100644
--- a/backends/manual_policy.py
+++ b/backends/manual_policy.py
@@ -20,8 +20,8 @@ class ManualPolicy(ControllerBase):
self.adj = transition_adj
def _transition(self):
- """Check if the current node's termination condition is met and if
- it is possible to transition to another node, i.e. its initiation
+ """Check if the current node's termination condition is met and if it
+ is possible to transition to another node, i.e. its initiation
condition is met. This is an internal function.
Returns the new node if a transition can happen, None otherwise
diff --git a/backends/mcts_learner.py b/backends/mcts_learner.py
index 3743dba..f9bd759 100644
--- a/backends/mcts_learner.py
+++ b/backends/mcts_learner.py
@@ -4,9 +4,8 @@ import pickle
class MCTSLearner(ControllerBase):
- """Monte Carlo Tree Search implementation using the UCB1 and
- progressive widening approach as explained in Paxton et al (2017).
- """
+ """Monte Carlo Tree Search implementation using the UCB1 and progressive
+ widening approach as explained in Paxton et al (2017)."""
_ucb_vals = set()
@@ -76,8 +75,8 @@ class MCTSLearner(ControllerBase):
self.new_node_num = to_restore['new_node_num']
def _create_node(self, low_level_policy):
- """Create the node associated with curr_node_num, using the
- given low level policy.
+ """Create the node associated with curr_node_num, using the given low
+ level policy.
Args:
low_level_policy: the option's alias
@@ -92,11 +91,10 @@ class MCTSLearner(ControllerBase):
return created_node_num, {"policy": low_level_policy}
def _to_discrete(self, observation):
- """Converts observation to a discrete observation tuple. Also
- append (a) whether we are following a vehicle, and (b) whether
- there is a vehicle in the opposite lane in the approximately
- the same x position. These values will be useful for Follow
- and ChangeLane maneuvers.
+ """Converts observation to a discrete observation tuple. Also append
+ (a) whether we are following a vehicle, and (b) whether there is a
+ vehicle in the opposite lane in the approximately the same x position.
+ These values will be useful for Follow and ChangeLane maneuvers.
Args:
observation: observation tuple from the environment
@@ -137,9 +135,9 @@ class MCTSLearner(ControllerBase):
def _get_visitation_count(self, observation, option=None):
"""Finds the visitation count of the discrete form of the observation.
- If discrete observation not found, then inserted into self.N with
- value 0. Auto converts the observation into discrete form. If option
- is not None, then this uses self.M instead of self.N
+ If discrete observation not found, then inserted into self.N with value
+ 0. Auto converts the observation into discrete form. If option is not
+ None, then this uses self.M instead of self.N.
Args:
observation: observation tuple from the environment
@@ -177,8 +175,9 @@ class MCTSLearner(ControllerBase):
def _get_possible_options(self):
"""Returns a set of options that can be taken from the current node.
- Goes through adjacency set of current node and finds which next nodes'
- initiation condition is met.
+
+ Goes through adjacency set of current node and finds which next
+ nodes' initiation condition is met.
"""
all_options = set(self.low_level_policies.keys())
@@ -211,9 +210,9 @@ class MCTSLearner(ControllerBase):
return visited_aliases
def _ucb_adjusted_q(self, observation, C=1):
- """Computes Q_star(observation, option_i) plus the UCB term, which
- is C*[predictor(observation, option_i)]/[1+N(observation, option_i)],
- for all option_i in the adjacency set of the current node.
+ """Computes Q_star(observation, option_i) plus the UCB term, which is
+ C*[predictor(observation, option_i)]/[1+N(observation, option_i)], for
+ all option_i in the adjacency set of the current node.
Args:
observation: observation tuple from the environment
@@ -257,14 +256,14 @@ class MCTSLearner(ControllerBase):
return sum_rewards / (1 + self._get_visitation_count(observation))
def _select(self, observation, depth=0, visualize=False):
- """MCTS selection function. For representation, we only use
- the discrete part of the observation.
+ """MCTS selection function. For representation, we only use the
+ discrete part of the observation.
Args:
observation: observation tuple from the environment
depth: current depth, starts from root node, hence 0 by default
visualize: whether or not to visualize low level steps
-
+
Returns the sum of values from the given observation.
"""
@@ -316,8 +315,8 @@ class MCTSLearner(ControllerBase):
return self._value(observation), all_ep_R + episode_R
def traverse(self, observation, visualize=False):
- """Do a complete traversal from root to leaf. Assumes the
- environment is reset and we are at the root node.
+ """Do a complete traversal from root to leaf. Assumes the environment
+ is reset and we are at the root node.
Args:
observation: observation from the environment
@@ -329,13 +328,13 @@ class MCTSLearner(ControllerBase):
return self._select(observation, visualize=visualize)
def do_transition(self, observation, visualize=False):
- """Do a transition using UCB metric, with the latest observation
- from the episodic step.
+ """Do a transition using UCB metric, with the latest observation from
+ the episodic step.
Args:
observation: final observation from episodic step
- visualize: whether or not to visualize low level steps
-
+ visualize: whether or not to visualize low level steps
+
Returns o_star using UCB metric
"""
diff --git a/backends/online_mcts_controller.py b/backends/online_mcts_controller.py
index 64343d8..a53d83b 100644
--- a/backends/online_mcts_controller.py
+++ b/backends/online_mcts_controller.py
@@ -5,7 +5,7 @@ import numpy as np
class OnlineMCTSController(ControllerBase):
- """Online MCTS"""
+ """Online MCTS."""
def __init__(self, env, low_level_policies, start_node_alias):
"""Constructor for manual policy execution.
diff --git a/backends/policy_base.py b/backends/policy_base.py
index ad1d3df..25bb5c3 100644
--- a/backends/policy_base.py
+++ b/backends/policy_base.py
@@ -1,3 +1,3 @@
class PolicyBase:
- """Abstract policy base from which every policy backend is defined
- and inherited."""
+ """Abstract policy base from which every policy backend is defined and
+ inherited."""
diff --git a/env/env_base.py b/env/env_base.py
index a68c066..4a8b9a5 100644
--- a/env/env_base.py
+++ b/env/env_base.py
@@ -3,23 +3,20 @@ from model_checker import Parser
class GymCompliantEnvBase:
def step(self, action):
- """ Gym compliant step function which
- will be implemented in the subclass.
- """
+ """Gym compliant step function which will be implemented in the
+ subclass."""
raise NotImplemented(self.__class__.__name__ +
"step is not implemented.")
def reset(self):
- """ Gym compliant reset function which
- will be implemented in the subclass.
- """
+ """Gym compliant reset function which will be implemented in the
+ subclass."""
raise NotImplemented(self.__class__.__name__ +
"reset is not implemented.")
def render(self):
- """ Gym compliant step function which
- will be implemented in the subclass.
- """
+ """Gym compliant step function which will be implemented in the
+ subclass."""
raise NotImplemented(self.__class__.__name__ +
"render is not implemented.")
@@ -49,7 +46,8 @@ class EpisodicEnvBase(GymCompliantEnvBase):
def _init_LTL_preconditions(self):
"""Initialize the LTL preconditions (self._LTL_preconditions)..
- in the subclass.
+
+ in the subclass.
"""
return
@@ -72,9 +70,8 @@ class EpisodicEnvBase(GymCompliantEnvBase):
self._r_terminal = None
def _terminal_reward_superposition(self, r_obs):
- """Calculate the next value when observing "obs," from the prior
- using min, max, or summation depending on the superposition_type.
- """
+ """Calculate the next value when observing "obs," from the prior using
+ min, max, or summation depending on the superposition_type."""
if r_obs is None:
return
@@ -133,7 +130,8 @@ class EpisodicEnvBase(GymCompliantEnvBase):
return violate, info
def current_model_checking_result(self):
- """Returns whether or not any of the conditions is currently violated."""
+ """Returns whether or not any of the conditions is currently
+ violated."""
for LTL_precondition in self._LTL_preconditions:
if LTL_precondition.result == Parser.FALSE:
@@ -155,9 +153,8 @@ class EpisodicEnvBase(GymCompliantEnvBase):
@property
def termination_condition(self):
- """In the subclass, specify the condition for termination of the episode
- (or the maneuver).
- """
+ """In the subclass, specify the condition for termination of the
+ episode (or the maneuver)."""
if self._terminate_in_goal and self.goal_achieved:
return True
diff --git a/env/road_env.py b/env/road_env.py
index 3a189ed..9743b58 100644
--- a/env/road_env.py
+++ b/env/road_env.py
@@ -1,6 +1,6 @@
class RoadEnv:
- """The generic road env
+ """The generic road env.
- TODO: Implement this generic road env for plugging-in other road envs.
- TODO: roadEnv also having a step() function can cause a problem.
+ TODO: Implement this generic road env for plugging-in other road envs.
+ TODO: roadEnv also having a step() function can cause a problem.
"""
\ No newline at end of file
diff --git a/env/simple_intersection/features.py b/env/simple_intersection/features.py
index 9973cf6..2b70e51 100644
--- a/env/simple_intersection/features.py
+++ b/env/simple_intersection/features.py
@@ -165,9 +165,7 @@ class Features(object):
return self.other_vehs[index]
def get_features_tuple(self):
- """
- continuous + discrete features
- """
+ """continuous + discrete features."""
feature = self.con_ego + self.dis_ego
for other_veh in self.other_vehs:
diff --git a/env/simple_intersection/shapes.py b/env/simple_intersection/shapes.py
index 62f1566..d814290 100644
--- a/env/simple_intersection/shapes.py
+++ b/env/simple_intersection/shapes.py
@@ -14,7 +14,7 @@ class Rectangle(Shape):
"""Rectangle using OpenGL."""
def __init__(self, xmin, xmax, ymin, ymax, color=(0, 0, 0, 255)):
- """ Constructor for Rectangle.
+ """Constructor for Rectangle.
The rectangle has the four points (xmin, ymin), (xmin, ymax),
(xmax, ymax), (xmax, ymin).
diff --git a/env/simple_intersection/simple_intersection_env.py b/env/simple_intersection/simple_intersection_env.py
index f37867f..1d61b91 100644
--- a/env/simple_intersection/simple_intersection_env.py
+++ b/env/simple_intersection/simple_intersection_env.py
@@ -150,7 +150,7 @@ class SimpleIntersectionEnv(RoadEnv, EpisodicEnvBase):
return self.vehs[EGO_INDEX]
def generate_scenario(self, **kwargs):
- """Randomly generate a road scenario with
+ """Randomly generate a road scenario with.
"the N-number of vehicles + an ego vehicle"
@@ -609,9 +609,9 @@ class SimpleIntersectionEnv(RoadEnv, EpisodicEnvBase):
@staticmethod
def __regulate_veh_speed(veh, dist, v_max_multiplier):
- """This private method set and regulate the speed of the
- vehicle in a way that it never requires to travel the given
- distance for complete stop.
+ """This private method set and regulate the speed of the vehicle in a
+ way that it never requires to travel the given distance for complete
+ stop.
Args:
veh: the vehicle reference.
@@ -920,9 +920,7 @@ class SimpleIntersectionEnv(RoadEnv, EpisodicEnvBase):
self.ego.theta) > self.max_ego_theta
def _check_collisions(self):
- """returns True when collision happens, False if not.
-
- """
+ """returns True when collision happens, False if not."""
self.__ego_collision_happened = self.check_ego_collision() or \
self.is_ego_off_road()
self.__others_collision_happened = self.check_other_veh_collisions()
@@ -1004,8 +1002,8 @@ class SimpleIntersectionEnv(RoadEnv, EpisodicEnvBase):
return True if (h_offroad and v_offroad) else False
def cost(self, u):
- """Calculate the driving cost of the ego, i.e.,
- the negative reward for the ego-driving.
+ """Calculate the driving cost of the ego, i.e., the negative reward for
+ the ego-driving.
Args:
u: the low-level input (a, dot_psi) to the ego vehicle.
@@ -1045,8 +1043,9 @@ class SimpleIntersectionEnv(RoadEnv, EpisodicEnvBase):
#TODO: Move this to utilities
def normalize_tuple(self, vec, scale_factor=10):
- """Normalizes each element in a tuple according to ranges defined in self.cost_normalization_ranges.
- Normalizes between 0 and 1. And the scales by scale_factor
+ """Normalizes each element in a tuple according to ranges defined in
+ self.cost_normalization_ranges. Normalizes between 0 and 1. And the
+ scales by scale_factor.
Args:
vec: The tuple to be normalized
@@ -1070,8 +1069,7 @@ class SimpleIntersectionEnv(RoadEnv, EpisodicEnvBase):
return tuple(normalized_vec)
def get_features_tuple(self):
- """
- Get/calculate the features wrt. the current state variables
+ """Get/calculate the features wrt. the current state variables.
Returns features tuple
"""
@@ -1173,11 +1171,12 @@ class SimpleIntersectionEnv(RoadEnv, EpisodicEnvBase):
@property
def goal_achieved(self):
- """
- A property from the base class which is True if the goal
- of the road scenario is achieved, otherwise False. This property is
- used in both step of EpisodicEnvBase and the implementation of
- the high-level reinforcement learning and execution.
+ """A property from the base class which is True if the goal of the road
+ scenario is achieved, otherwise False.
+
+ This property is used in both step of EpisodicEnvBase and the
+ implementation of the high-level reinforcement learning and
+ execution.
"""
return (self.ego.x >= rd.hlanes.end_pos) and \
@@ -1220,9 +1219,7 @@ class SimpleIntersectionEnv(RoadEnv, EpisodicEnvBase):
label.draw()
def render(self):
- """
- Gym compliant render function.
- """
+ """Gym compliant render function."""
if self.window is None:
self.window = config_Pyglet(pyglet, NUM_HPIXELS, NUM_VPIXELS)
diff --git a/env/simple_intersection/utilities.py b/env/simple_intersection/utilities.py
index cffc663..9081f92 100644
--- a/env/simple_intersection/utilities.py
+++ b/env/simple_intersection/utilities.py
@@ -247,7 +247,8 @@ def draw_all_shapes(shapes):
def calculate_v_max(dist):
- """Calculate the maximum velocity you can reach at the given position ahead.
+ """Calculate the maximum velocity you can reach at the given position
+ ahead.
Args:
dist: the distance you travel from the current position.
@@ -259,8 +260,8 @@ def calculate_v_max(dist):
def calculate_s(v):
- """Calculate the distance traveling when the vehicle is maximally
- de-accelerating for a complete stop.
+ """Calculate the distance traveling when the vehicle is maximally de-
+ accelerating for a complete stop.
Args:
v: the current speed of the vehicle.
diff --git a/high_level_policy_main.py b/high_level_policy_main.py
index 6554c55..3c5a0f3 100644
--- a/high_level_policy_main.py
+++ b/high_level_policy_main.py
@@ -16,8 +16,8 @@ def high_level_policy_training(nb_steps=25000,
visualize=False,
tensorboard=False,
save_path="highlevel_weights.h5f"):
- """
- Do RL of the high-level policy and test it.
+ """Do RL of the high-level policy and test it.
+
Args:
nb_steps: the number of steps to perform RL
load_weights: True if the pre-learned NN weights are loaded (for initializations of NNs)
diff --git a/low_level_policy_main.py b/low_level_policy_main.py
index 209f645..c6dd335 100644
--- a/low_level_policy_main.py
+++ b/low_level_policy_main.py
@@ -16,8 +16,8 @@ def low_level_policy_training(maneuver,
visualize=False,
nb_episodes_for_test=10,
tensorboard=False):
- """
- Do RL of the low-level policy of the given maneuver and test it.
+ """Do RL of the low-level policy of the given maneuver and test it.
+
Args:
maneuver: the name of the maneuver defined in config.json (e.g., 'default').
nb_steps: the number of steps to perform RL.
diff --git a/mcts.py b/mcts.py
index 7c4fa22..e68356b 100644
--- a/mcts.py
+++ b/mcts.py
@@ -34,8 +34,8 @@ def mcts_training(nb_traversals,
visualize=False,
load_saved=False,
save_file="mcts.pickle"):
- """
- Do RL of the low-level policy of the given maneuver and test it.
+ """Do RL of the low-level policy of the given maneuver and test it.
+
Args:
nb_traversals: number of MCTS traversals
save_every: save at every these many traversals
@@ -91,8 +91,8 @@ def mcts_evaluation(nb_traversals,
visualize=False,
save_file="mcts.pickle",
pretrained=False):
- """
- Do RL of the low-level policy of the given maneuver and test it.
+ """Do RL of the low-level policy of the given maneuver and test it.
+
Args:
nb_traversals: number of MCTS traversals
save_every: save at every these many traversals
@@ -170,8 +170,8 @@ def online_mcts(nb_episodes=10):
first_time = False
else:
print('Stepping through ...')
- features, R, terminal, info = options.controller.step_current_node(
- visualize_low_level_steps=True)
+ features, R, terminal, info = options.controller.\
+ step_current_node(visualize_low_level_steps=True)
episode_reward += R
print('Intermediate Reward: %f (ego x = %f)' %
(R, options.env.vehs[0].x))
@@ -224,8 +224,8 @@ def evaluate_online_mcts(nb_episodes=20, nb_trials=5):
first_time = False
else:
print('Stepping through ...')
- features, R, terminal, info = options.controller.step_current_node(
- visualize_low_level_steps=True)
+ features, R, terminal, info = options.controller.\
+ step_current_node(visualize_low_level_steps=True)
episode_reward += R
print('Intermediate Reward: %f (ego x = %f)' %
(R, options.env.vehs[0].x))
diff --git a/model_checker/LTL_property_base.py b/model_checker/LTL_property_base.py
index 304a306..4d5a9d8 100644
--- a/model_checker/LTL_property_base.py
+++ b/model_checker/LTL_property_base.py
@@ -5,17 +5,17 @@ from model_checker.parser import Parser, Errors
class LTLPropertyBase(object):
"""This is a base class that contains information of an LTL property.
- It encapsulates the model-checking part (see check / check_incremental),
- and contains additional information. The subclass needs to describe
- specific APdict to be used.
+ It encapsulates the model-checking part (see check /
+ check_incremental), and contains additional information. The
+ subclass needs to describe specific APdict to be used.
"""
#: The atomic propositions dict you must set in the subclass.
APdict = None
def __init__(self, LTL_str, penalty, enabled=True):
- """Constructor for LTLPropertyBase.
- Assumes property does not change, but property may be applied to multiple traces.
+ """Constructor for LTLPropertyBase. Assumes property does not change,
+ but property may be applied to multiple traces.
Args:
LTL_str: the human readable string representation of the LTL property
@@ -42,8 +42,10 @@ class LTLPropertyBase(object):
self.result = Parser.UNDECIDED
def reset_property(self):
- """Resets existing property so that it can be applied to a new sequence of states.
- Assumes init_property or check were previously called.
+ """Resets existing property so that it can be applied to a new sequence
+ of states.
+
+ Assumes init_property or check were previously called.
"""
self.parser.ResetProperty()
self.result = Parser.UNDECIDED
@@ -64,11 +66,11 @@ class LTLPropertyBase(object):
def check_incremental(self, state):
"""Checks an initialised property w.r.t. the next state in a trace.
- Assumes init_property or check were previously called.
-
+ Assumes init_property or check were previously called.
+
Args:
state: next state (an integer)
-
+
Returns:
incremental result, in {TRUE, FALSE, UNDECIDED}
"""
diff --git a/model_checker/atomic_propositions_base.py b/model_checker/atomic_propositions_base.py
index 7e50d70..4012cdf 100644
--- a/model_checker/atomic_propositions_base.py
+++ b/model_checker/atomic_propositions_base.py
@@ -2,9 +2,9 @@ class Bits(object):
"""A bit-control class that allows us bit-wise manipulation as shown in the
example::
- bits = Bits()
- bits[0] = False
- bits[2] = bits[0]
+ bits = Bits()
+ bits[0] = False
+ bits[2] = bits[0]
"""
def __init__(self, value=0):
@@ -35,8 +35,9 @@ class Bits(object):
class AtomicPropositionsBase(Bits):
"""An AP-control base class for AP-wise manipulation.
- the dictionary APdict and its length APdict_len has
- to be given in the subclass
+
+ the dictionary APdict and its length APdict_len has to be given in
+ the subclass
"""
APdict = None
diff --git a/model_checker/parser.py b/model_checker/parser.py
index 8a415fe..8a6dc99 100644
--- a/model_checker/parser.py
+++ b/model_checker/parser.py
@@ -191,9 +191,11 @@ class Parser(object):
TRUE = 2
def Check_old(self, propscanner, trace):
- """Deprecated method to check an entire trace with a new property Scanner.
- Includes SetProperty, which includes ResetProperty.
- """
+ """Deprecated method to check an entire trace with a new property
+ Scanner.
+
+ Includes SetProperty, which includes ResetProperty.
+ """
self.SetProperty(propscanner)
for state in trace:
result = self.CheckIncremental(state)
@@ -201,9 +203,11 @@ class Parser(object):
return result
def Check(self, trace):
- """Checks an entire trace w.r.t. an existing property Scanner.
- Includes ResetProperty, but not SetProperty.
- """
+ """Checks an entire trace w.r.t.
+
+ an existing property Scanner. Includes ResetProperty, but not
+ SetProperty.
+ """
self.ResetProperty()
for state in trace:
result = self.CheckIncremental(state)
@@ -211,23 +215,23 @@ class Parser(object):
return result
def SetProperty(self, propscanner):
- """Sets the property Scanner that tokenizes the property.
- """
+ """Sets the property Scanner that tokenizes the property."""
self.scanner = propscanner
self.ResetProperty()
def ResetProperty(self):
- """Re-iniitializes an existing property Scanner.
- """
+ """Re-iniitializes an existing property Scanner."""
self.step = 0
self.maxfactor = -1
self.start = self.scanner.t = self.scanner.tokens
def CheckIncremental(self, state):
- """Checks a new state w.r.t. an existing property Scanner.
- Constructs a trace from new states using a new or previous trace list.
- If a previous trace list is used, states after index self.step are not valid.
- """
+ """Checks a new state w.r.t.
+
+ an existing property Scanner. Constructs a trace from new states
+ using a new or previous trace list. If a previous trace list is
+ used, states after index self.step are not valid.
+ """
if len(self.trace) <= self.step:
self.trace.append(state)
else:
diff --git a/model_checker/simple_intersection/classes.py b/model_checker/simple_intersection/classes.py
index 471d7da..4a88c83 100644
--- a/model_checker/simple_intersection/classes.py
+++ b/model_checker/simple_intersection/classes.py
@@ -4,8 +4,7 @@ from .AP_dict import AP_dict_simple_intersection
# TODO: classes.py is not a good name. Spliti the two classes into separate files and rename them.
class AtomicPropositions(AtomicPropositionsBase):
- """ An AP-control class for AP-wise manipulation as shown in the
- example:
+ """An AP-control class for AP-wise manipulation as shown in the example:
APs = AtomicPropositions()
APs[0] = False # this is same as
@@ -15,16 +14,15 @@ class AtomicPropositions(AtomicPropositionsBase):
Requires:
Index in [...] is an integer should be in the range
{0,1,2, ..., AP_dict_len}.
-
"""
APdict = AP_dict_simple_intersection
class LTLProperty(LTLPropertyBase):
- """ is a class that contains information of an LTL property in
- simple_intersection road scenario.
+ """is a class that contains information of an LTL property in
+ simple_intersection road scenario.
- It encapsulates the model-checking part and contains additional
- information.
+ It encapsulates the model-checking part and contains additional
+ information.
"""
APdict = AP_dict_simple_intersection
diff --git a/options/options_loader.py b/options/options_loader.py
index 0befa6b..f58554f 100644
--- a/options/options_loader.py
+++ b/options/options_loader.py
@@ -5,8 +5,7 @@ from backends import RLController, DDPGLearner, MCTSLearner, OnlineMCTSControlle
class OptionsGraph:
- """
- Represent the options graph as a graph like structure. The configuration
+ """Represent the options graph as a graph like structure. The configuration
is specified in a json file and consists of the following specific values:
* nodes: dictionary of policy node aliases -> maneuver classes
@@ -18,8 +17,7 @@ class OptionsGraph:
visualize_low_level_steps = False
def __init__(self, json_path, env_class, *env_args, **env_kwargs):
- """
- Constructor for the options graph.
+ """Constructor for the options graph.
Args:
json_path: path to the json file which stores the configuration
@@ -78,9 +76,9 @@ class OptionsGraph:
"Controller to be used not specified")
def step(self, option):
- """Complete an episode using specified option. This assumes that
- the manager's env is at a place where the option's initiation
- condition is met.
+ """Complete an episode using specified option. This assumes that the
+ manager's env is at a place where the option's initiation condition is
+ met.
Args:
option: index of high level option to be executed
@@ -111,8 +109,9 @@ class OptionsGraph:
return self.env.reset()
def set_controller_policy(self, policy):
- """Sets the trained controller policy as a function which takes in feature vector and returns
- an option index(int). By default, trained_policy is None
+ """Sets the trained controller policy as a function which takes in
+ feature vector and returns an option index(int). By default,
+ trained_policy is None.
Args:
policy: a function which takes in feature vector and returns an option index(int)
@@ -120,7 +119,7 @@ class OptionsGraph:
self.controller.set_trained_policy(policy)
def set_controller_args(self, **kwargs):
- """Sets custom arguments depending on the chosen controller
+ """Sets custom arguments depending on the chosen controller.
Args:
**kwargs: dictionary of args and values
@@ -128,9 +127,11 @@ class OptionsGraph:
self.controller.set_controller_args(**kwargs)
def execute_controller_policy(self):
- """Performs low level steps and transitions to other nodes using controller transition policy.
+ """Performs low level steps and transitions to other nodes using
+ controller transition policy.
- Returns state after one step, step reward, episode_termination_flag, info
+ Returns state after one step, step reward,
+ episode_termination_flag, info
"""
if self.controller.can_transition():
self.controller.do_transition()
@@ -138,7 +139,8 @@ class OptionsGraph:
return self.controller.low_level_step_current_node()
def set_current_node(self, node_alias):
- """Sets the current node for controller. Used for training/testing a particular node
+ """Sets the current node for controller. Used for training/testing a
+ particular node.
Args:
node_alias: alias of the node as per config file
diff --git a/options/simple_intersection/maneuver_base.py b/options/simple_intersection/maneuver_base.py
index 46de914..78f7a63 100644
--- a/options/simple_intersection/maneuver_base.py
+++ b/options/simple_intersection/maneuver_base.py
@@ -172,8 +172,9 @@ class ManeuverBase(EpisodicEnvBase):
return self._features_dim_reduction(features), reward, terminal, info
def get_reduced_feature_length(self):
- """
- get the length of the feature tuple after applying _features_dim_reduction.
+ """get the length of the feature tuple after applying
+ _features_dim_reduction.
+
:return:
"""
return len(self.get_reduced_features_tuple())
@@ -235,8 +236,10 @@ class ManeuverBase(EpisodicEnvBase):
self.env.render() # Do nothing but calling self.env.render()
def set_low_level_trained_policy(self, trained_policy):
- """Sets the trained policy as a function which takes in feature vector and returns
- an action (a, dot_psi). By default, trained_policy is None
+ """Sets the trained policy as a function which takes in feature vector
+ and returns an action (a, dot_psi).
+
+ By default, trained_policy is None
"""
self.trained_policy = trained_policy
@@ -254,8 +257,7 @@ class ManeuverBase(EpisodicEnvBase):
@staticmethod
def _features_dim_reduction(features_tuple):
- """
- Reduce the dimension of the features in step and reset.
+ """Reduce the dimension of the features in step and reset.
Param: features_tuple: a tuple obtained by e.g., self.env.get_features_tuple()
Return: the reduced features tuple (by default, it returns features_tuple itself.
@@ -264,9 +266,11 @@ class ManeuverBase(EpisodicEnvBase):
# TODO: Determine whether this method depends on the external features_tuple or for simplicity, define and use a features_tuple within the class.
def low_level_policy(self, reduced_features_tuple):
- """the low level policy as a map from a feature vector to an action
- (a, dot_psi). By default, it'll call low_level_manual_policy below
- if it's implemented in the subclass.
+ """the low level policy as a map from a feature vector to an action (a,
+ dot_psi).
+
+ By default, it'll call low_level_manual_policy below if it's
+ implemented in the subclass.
"""
if self.trained_policy is None:
return self._low_level_manual_policy()
@@ -274,9 +278,12 @@ class ManeuverBase(EpisodicEnvBase):
return self.trained_policy(reduced_features_tuple)
def _low_level_manual_policy(self):
- """the manually-defined low level policy as a map from a feature vector to an action
- (a, dot_psi). _low_level_policy will call this manual policy unless modified in the subclass.
- Implement this in the subclass whenever necessary.
+ """the manually-defined low level policy as a map from a feature vector
+ to an action (a, dot_psi).
+
+ _low_level_policy will call this manual policy unless modified
+ in the subclass. Implement this in the subclass whenever
+ necessary.
"""
raise NotImplemented(self.__class__.__name__ +
"._low_Level_manual_policy is not implemented.")
@@ -295,9 +302,9 @@ class ManeuverBase(EpisodicEnvBase):
enable_LTL_preconditions=True,
timeout=np.infty,
**kwargs):
- """generates the scenario for low-level policy learning and validation. This method
- will be used in generate_learning_scenario and generate_validation_scenario in
- the subclasses.
+ """generates the scenario for low-level policy learning and validation.
+ This method will be used in generate_learning_scenario and
+ generate_validation_scenario in the subclasses.
Param:
enable_LTL_preconditions: whether to enable LTL preconditions in the maneuver or not
@@ -356,7 +363,6 @@ class ManeuverBase(EpisodicEnvBase):
return # do nothing unless specified in the subclass
def _update_param(self):
- """Update the parameters in the gym-compliant 'step' method above
- """
+ """Update the parameters in the gym-compliant 'step' method above."""
return # do nothing unless specified in the subclass
diff --git a/ppo2_training.py b/ppo2_training.py
index 5c5609c..269b7fd 100644
--- a/ppo2_training.py
+++ b/ppo2_training.py
@@ -29,7 +29,8 @@ if __name__ == "__main__":
"right_weights.h5f"
) # Save the NN weights for reloading them in the future.
- # Uncomment this after training to use trained model. Comment this to use manually defined policy
+ # Uncomment this after training to use trained model.
+ # Comment this to use manually defined policy
#agent.load_weights("right_weights.h5f")
print("Testing model...")
--
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