From 7dc47bce50616f94dad25d86a9e1828c034ca2ba Mon Sep 17 00:00:00 2001
From: Jaeyoung Lee <jaeyoung.lee@uwaterloo.ca>
Date: Fri, 25 Jan 2019 00:02:00 -0500
Subject: [PATCH] Fix the bug of having -100000 or 10000 rewards sometime.
---
backends/controller_base.py | 8 +-
backends/kerasrl_learner.py | 23 ++--
config.json | 2 +-
env/env_base.py | 2 +-
env/simple_intersection/road_geokinemetry.py | 2 +-
.../simple_intersection_env.py | 4 +-
options/options_loader.py | 5 -
options/simple_intersection/maneuver_base.py | 4 +-
options/simple_intersection/maneuvers.py | 114 +++++++++---------
9 files changed, 75 insertions(+), 89 deletions(-)
diff --git a/backends/controller_base.py b/backends/controller_base.py
index 1617036..ad31856 100644
--- a/backends/controller_base.py
+++ b/backends/controller_base.py
@@ -65,7 +65,7 @@ class ControllerBase(PolicyBase):
total_reward += self.current_node.high_level_extra_reward
# TODO for info
- return observation, total_reward, self.env.termination_condition, info
+ return observation, total_reward, terminal, info
# TODO: Looks generic. Move to an intermediate class/highlevel manager so that base class can be clean
''' Executes one step of current node. Sets node_terminal_state_reached flag if node termination condition
@@ -76,9 +76,7 @@ class ControllerBase(PolicyBase):
def low_level_step_current_node(self):
- u_ego = self.current_node.low_level_policy(
- self.current_node.get_reduced_features_tuple())
+ u_ego = self.current_node.low_level_policy(self.current_node.get_reduced_features_tuple())
feature, R, terminal, info = self.current_node.step(u_ego)
self.node_terminal_state_reached = terminal
- return self.env.get_features_tuple(
- ), R, self.env.termination_condition, info
+ return self.env.get_features_tuple(), R, self.env.termination_condition, info
diff --git a/backends/kerasrl_learner.py b/backends/kerasrl_learner.py
index 3d79734..405a9ba 100644
--- a/backends/kerasrl_learner.py
+++ b/backends/kerasrl_learner.py
@@ -317,6 +317,7 @@ class DQNLearner(LearnerBase):
nb_steps_warmup=self.nb_steps_warmup,
target_model_update=self.target_model_update,
policy=policy,
+ test_policy=test_policy,
enable_dueling_network=True)
agent.compile(Adam(lr=self.lr), metrics=['mae'])
@@ -327,6 +328,8 @@ class DQNLearner(LearnerBase):
env,
nb_steps=1000000,
visualize=False,
+ verbose=1,
+ log_interval=10000,
nb_max_episode_steps=200,
tensorboard=False,
model_checkpoints=False,
@@ -346,7 +349,8 @@ class DQNLearner(LearnerBase):
env,
nb_steps=nb_steps,
visualize=visualize,
- verbose=1,
+ verbose=verbose,
+ log_interval=log_interval,
nb_max_episode_steps=nb_max_episode_steps,
callbacks=callbacks)
@@ -479,21 +483,13 @@ class RestrictedGreedyQPolicy(GreedyQPolicy):
Selection action
"""
assert q_values.ndim == 1
- nb_actions = q_values.shape[0]
- restricted_q_values = list()
-
- for i in range(0, nb_actions):
- if q_values[i] != -np.inf:
- restricted_q_values.append(q_values[i])
- # every q_value is -np.inf (this sometimes inevitably happens within the fit and test functions
- # of kerasrl at the terminal stage as they force to call forward in Kerasrl-learner which calls this function.
- # In this case, we choose a policy randomly.
- if len(restricted_q_values) < 1:
- action = np.random.random_integers(0, nb_actions - 1)
+ if np.max(q_values) == - np.inf:
+ # every q_value is -np.inf, we choose action = 0
+ action = 0
else:
- action = np.argmax(restricted_q_values)
+ action = np.argmax(q_values)
return action
@@ -521,6 +517,7 @@ class DQNAgentOverOptions(DQNAgent):
def __get_invalid_node_indices(self):
"""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):
# TODO: Locate reset_maneuver to another place as this is a "get" function.
diff --git a/config.json b/config.json
index 5e40c77..b8d9b1c 100644
--- a/config.json
+++ b/config.json
@@ -3,7 +3,7 @@
"wait": "Wait",
"follow": "Follow",
"stop": "Stop",
- "immediatestop": "ImmediateStop",
+ "changelane": "ChangeLane",
"keeplane": "KeepLane"
},
diff --git a/env/env_base.py b/env/env_base.py
index 4d28722..038d59a 100644
--- a/env/env_base.py
+++ b/env/env_base.py
@@ -157,7 +157,7 @@ class EpisodicEnvBase(GymCompliantEnvBase):
if self._terminate_in_goal and self.goal_achieved:
return True
- return self.violation_happened and self._LTL_preconditions_enable
+ return self.violation_happened
@property
def goal_achieved(self):
diff --git a/env/simple_intersection/road_geokinemetry.py b/env/simple_intersection/road_geokinemetry.py
index 63190be..602b3e5 100644
--- a/env/simple_intersection/road_geokinemetry.py
+++ b/env/simple_intersection/road_geokinemetry.py
@@ -93,7 +93,7 @@ vlanes = Route(
[-vwidth - 5.0 - intersection_voffset, -vwidth - intersection_voffset], 35,
[-4.0, 4.0])
-intersection_width = vlanes.n_lanes * vlanes.width
+intersection_width = vlanes.n_lanes * vlanes.width
intersection_height = hlanes.n_lanes * hlanes.width
intersection_width_w_offset = intersection_width + 2 * intersection_hoffset
diff --git a/env/simple_intersection/simple_intersection_env.py b/env/simple_intersection/simple_intersection_env.py
index 32f50ef..a2faa92 100644
--- a/env/simple_intersection/simple_intersection_env.py
+++ b/env/simple_intersection/simple_intersection_env.py
@@ -51,7 +51,9 @@ class SimpleIntersectionEnv(RoadEnv, EpisodicEnvBase):
#: The weight vector to calculate the cost. In the maneuver, cost_weights
# can be set to a specific value which may be different than the default.
- cost_weights = (1.0, 0.25, 0.1, 1.0, 100.0, 0.1, 0.25, 0.1)
+ # TODO: check _cost_weights in both here and ManeuverBase. The _cost_weights has to be substituted to here, but it doesn't sometimes.
+ _cost_weights = (10.0 * 1e-3, 10.0 * 1e-3, 0.25 * 1e-3, 1.0 * 1e-3,
+ 100.0 * 1e-3, 0.1 * 1e-3, 0.05 * 1e-3, 0.1 * 1e-3)
#TODO: Move this to constants
# The empirical min and max of each term in the cost vector, which is used to normalize the values
diff --git a/options/options_loader.py b/options/options_loader.py
index bce1a6c..f4f7386 100644
--- a/options/options_loader.py
+++ b/options/options_loader.py
@@ -180,11 +180,6 @@ class OptionsGraph:
print("\n Warning: the trained low-level policy of \"" + key +
"\" does not exists; the manual policy will be used.\n")
- # setting the cost weights for high-level policy training.
- # TODO: this shouldn't be initialized here, but within ManeuverBase class (e.g. make some flag indicating high-level training and set the weights)...
- maneuver._cost_weights = (100.0 * 1e-3, 10.0 * 1e-3, 0.25 * 1e-3, 1.0 * 1e-3,
- 100.0 * 1e-3, 0.1 * 1e-3, 0.25 * 1e-3, 0.1 * 1e-3)
-
if self.config["method"] == "mcts":
maneuver.timeout = np.inf
diff --git a/options/simple_intersection/maneuver_base.py b/options/simple_intersection/maneuver_base.py
index fbbc3ed..82bae5e 100644
--- a/options/simple_intersection/maneuver_base.py
+++ b/options/simple_intersection/maneuver_base.py
@@ -30,7 +30,7 @@ class ManeuverBase(EpisodicEnvBase):
# as a negative reward, so a cost will be summed up to the reward
# with subtraction.
_cost_weights = (10.0 * 1e-3, 10.0 * 1e-3, 0.25 * 1e-3, 1.0 * 1e-3,
- 100.0 * 1e-3, 0.1 * 1e-3, 0.25 * 1e-3, 0.1 * 1e-3)
+ 100.0 * 1e-3, 0.1 * 1e-3, 0.05 * 1e-3, 0.1 * 1e-3)
_extra_r_terminal = None
_extra_r_on_timeout = None
@@ -38,7 +38,7 @@ class ManeuverBase(EpisodicEnvBase):
#: the flag being False when _cost_weights is used without
# modification; If True, then the action parts of _cost_weights
# are increased for some edge cases (see the step method).
- _extra_action_weights_flag = True
+ _extra_action_weights_flag = False
#: the extra weights on the actions added to _cost_weights
# for some edge cases when _extra_action_weights_flag = True.
diff --git a/options/simple_intersection/maneuvers.py b/options/simple_intersection/maneuvers.py
index 570b4ac..67c8318 100644
--- a/options/simple_intersection/maneuvers.py
+++ b/options/simple_intersection/maneuvers.py
@@ -15,11 +15,10 @@ class KeepLane(ManeuverBase):
self._target_lane = self.env.ego.APs['lane']
def _init_LTL_preconditions(self):
- self._LTL_preconditions.append(LTLProperty("G ( not veh_ahead )", 0))
- #self._LTL_preconditions.append(
- # LTLProperty("G ( not stopped_now )", 100,
- # self._enable_low_level_training_properties))
+ self._LTL_preconditions.append(
+ LTLProperty("G ( not stopped_now )", 100,
+ self._enable_low_level_training_properties))
self._LTL_preconditions.append(
LTLProperty(
@@ -37,6 +36,7 @@ class KeepLane(ManeuverBase):
self.env._terminate_in_goal = False
self.env._reward_in_goal = None
self._enable_low_level_training_properties = True
+ self._extra_action_weights_flag = True
def generate_validation_scenario(self):
self.generate_scenario(
@@ -65,7 +65,7 @@ class KeepLane(ManeuverBase):
return False
-class ImmediateStop(ManeuverBase):
+class Halt(ManeuverBase):
_terminate_in_goal = True
_reward_in_goal = None
@@ -102,6 +102,7 @@ class ImmediateStop(ManeuverBase):
self.env._reward_in_goal = None
self._reward_in_goal = 200
self._enable_low_level_training_properties = True
+ self._extra_action_weights_flag = True
def generate_validation_scenario(self):
self._ego_pos_range = (rd.hlanes.start_pos, rd.hlanes.end_pos)
@@ -147,14 +148,11 @@ class Stop(ManeuverBase):
self._target_lane = self.env.ego.APs['lane']
def _init_LTL_preconditions(self):
- #self._LTL_preconditions.append(
- # LTLProperty("G ( not has_stopped_in_stop_region )",
- # self._penalty(self._reward_in_goal)))
-
self._LTL_preconditions.append(
- LTLProperty("G ( not has_stopped_in_stop_region )", -150,
- not self._enable_low_level_training_properties))
+ LTLProperty("G ( not has_stopped_in_stop_region )",
+ self._penalty(self._reward_in_goal), not self._enable_low_level_training_properties))
+ # before_intersection rather than "before_but_close_to_stop_region or in_stop_region"?
self._LTL_preconditions.append(
LTLProperty(
"G ( (before_but_close_to_stop_region or in_stop_region) U has_stopped_in_stop_region )",
@@ -191,6 +189,7 @@ class Stop(ManeuverBase):
self._reward_in_goal = 200
self._penalty_in_violation = 150
self._enable_low_level_training_properties = True
+ self._extra_action_weights_flag = True
def _low_level_manual_policy(self):
return self.env.aggressive_driving_policy(EGO_INDEX)
@@ -234,27 +233,21 @@ class Wait(ManeuverBase):
def _init_LTL_preconditions(self):
self._LTL_preconditions.append(
+ LTLProperty("G ( (in_stop_region and stopped_now) and not (highest_priority and intersection_is_clear))",
+ None, not self._enable_low_level_training_properties)) # not available in low-level training...
- LTLProperty(
- "G ( (in_stop_region and stopped_now) and not (highest_priority and intersection_is_clear))", 0,
- not self._enable_low_level_training_properties)) # not available in low-level training...
-
- #LTLProperty(
- # "G ( (in_stop_region and stopped_now) U (highest_priority and intersection_is_clear))", 0,
- # not self._enable_low_level_training_properties)) # not available in low-level training...
-
- #self._LTL_preconditions.append(
- # LTLProperty("G ( not (in_intersection and highest_priority) )",
- # self._penalty(self._reward_in_goal)))
+ self._LTL_preconditions.append(
+ LTLProperty("G ( not (in_intersection and highest_priority) )",
+ self._penalty(self._reward_in_goal)))
- #self._LTL_preconditions.append(
- # LTLProperty(
- # "G ( in_stop_region U (highest_priority and intersection_is_clear) )", 150, self._enable_low_level_training_properties))
+ self._LTL_preconditions.append(
+ LTLProperty(
+ "G ( in_stop_region U (highest_priority and intersection_is_clear) )", 150, self._enable_low_level_training_properties))
- #self._LTL_preconditions.append(
- # LTLProperty(
- # "G ( (lane and target_lane) or (not lane and not target_lane) )",
- # 100, self._enable_low_level_training_properties))
+ self._LTL_preconditions.append(
+ LTLProperty(
+ "G ( (lane and target_lane) or (not lane and not target_lane) )",
+ 100, self._enable_low_level_training_properties))
def _init_param(self):
self._v_ref = 0 #if self._enable_low_level_training_properties else rd.speed_limit
@@ -324,7 +317,7 @@ class ChangeLane(ManeuverBase):
_violation_penalty_in_low_level_training = None
- # high_level_extra_reward = -1000000
+ high_level_extra_reward = -50
def _init_param(self):
self._v_ref = rd.speed_limit
@@ -332,13 +325,17 @@ class ChangeLane(ManeuverBase):
def _init_LTL_preconditions(self):
self._LTL_preconditions.append(
- LTLProperty("G ( on_route and not over_speed_limit )",
- self._violation_penalty_in_low_level_training,
- self._enable_low_level_training_properties))
+ LTLProperty("G ( on_route and not over_speed_limit )",
+ self._violation_penalty_in_low_level_training,
+ self._enable_low_level_training_properties))
self._LTL_preconditions.append(
- LTLProperty("G ( not stopped_now )",
- 100, self._enable_low_level_training_properties))
+ LTLProperty("G ( not stopped_now )",
+ 100, self._enable_low_level_training_properties))
+
+ self._LTL_preconditions.append(
+ LTLProperty("G ( not in_intersection and not in_stop_region )",
+ None, not self._enable_low_level_training_properties)) # activated only for the high-level case.
@property
def goal_achieved(self):
@@ -346,9 +343,9 @@ class ChangeLane(ManeuverBase):
APs = self.env.ego.APs
on_other_lane = APs['lane'] == self._target_lane
achieved_y_displacement = np.sign(ego.y) * \
- (ego.y - rd.hlanes.centres[APs['target_lane']]) >= - self.min_y_distance
+ (ego.y - rd.hlanes.centres[APs['target_lane']]) >= - self.min_y_distance
return on_other_lane and APs['on_route'] and \
- achieved_y_displacement and APs['parallel_to_lane']
+ achieved_y_displacement and APs['parallel_to_lane']
def _low_level_manual_policy(self):
return self.env.aggressive_driving_policy(EGO_INDEX)
@@ -366,6 +363,7 @@ class ChangeLane(ManeuverBase):
self._reward_in_goal = 200
self._violation_penalty_in_low_level_training = 150
self._enable_low_level_training_properties = True
+ self._extra_action_weights_flag = True
def generate_validation_scenario(self):
self.generate_scenario(
@@ -396,20 +394,17 @@ class Follow(ManeuverBase):
LTLProperty("G ( veh_ahead )", self._penalty_for_out_of_range))
self._LTL_preconditions.append(
- LTLProperty(
- "G ( (lane and target_lane) or (not lane and not target_lane) )",
- self._penalty_for_change_lane))
+ LTLProperty(
+ "G ( (lane and target_lane) or (not lane and not target_lane) )",
+ self._penalty_for_change_lane))
- #self._LTL_preconditions.append(
+ # self._LTL_preconditions.append(
# LTLProperty("G ( not stopped_now U veh_ahead_stopped_now)", 200,
# self._enable_low_level_training_properties))
self._LTL_preconditions.append(
- LTLProperty("G ( not veh_ahead_too_close )", self._penalty_for_out_of_range,
- self._enable_low_level_training_properties))
-
- self._LTL_preconditions.append(
- LTLProperty("G ( not in_stop_region)", 0, not self._enable_low_level_training_properties))
+ LTLProperty("G ( not veh_ahead_too_close )", self._penalty_for_out_of_range,
+ self._enable_low_level_training_properties))
def generate_learning_scenario(self):
self.generate_scenario(
@@ -422,6 +417,7 @@ class Follow(ManeuverBase):
self._penalty_for_out_of_range = 200
self._penalty_for_change_lane = 170
self._enable_low_level_training_properties = True
+ self._extra_action_weights_flag = True
def generate_validation_scenario(self):
self.generate_learning_scenario()
@@ -434,12 +430,12 @@ class Follow(ManeuverBase):
def _set_v_ref(self):
#if self._enable_low_level_training_properties:
- self._target_veh_i, _ = self.env.get_V2V_distance()
+ self._target_veh_i, _ = self.env.get_V2V_distance()
- if self._target_veh_i is not None:
- self._v_ref = self.env.vehs[self._target_veh_i].v
- else:
- self._v_ref = 0
+ if self._target_veh_i is not None:
+ self._v_ref = self.env.vehs[self._target_veh_i].v
+ else:
+ self._v_ref = 0
#else:
# self._v_ref = rd.speed_limit
@@ -448,19 +444,17 @@ class Follow(ManeuverBase):
@property
def extra_termination_condition(self):
- APs = self.env.ego.APs
+ # APs = self.env.ego.APs
if self._target_veh_i is None:
return False
- elif not self._enable_low_level_training_properties: # activated only for the high-level training.
- if (APs['in_stop_region'] or APs['before_but_close_to_stop_region']) \
- and (self.env.vehs[self._target_veh_i].APs['in_intersection'] or
- self.env.vehs[self._target_veh_i].x > 0):
- return True
- else:
- return False
-
+ #elif not self._enable_low_level_training_properties: # activated only for the high-level training.
+ # if (APs['in_stop_region'] or APs['before_but_close_to_stop_region']) \
+ # and (self.env.vehs[self._target_veh_i].APs['in_intersection'] or
+ # self.env.vehs[self._target_veh_i].x > 0):
+ # return True
+ # else:
return False
def _features_dim_reduction(self, features_tuple):
--
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