Commit 558f2efd by Jaeyoung Lee

Merge branch 'retraining_wait_maneuver' into 'master'

Retraining wait maneuver See merge request !4
parents 062ad4ff 074d2f49
......@@ -270,17 +270,17 @@ class DQNLearner(LearnerBase):
Returns: Keras Model object of actor
"""
model = Sequential()
model.add(Flatten(input_shape=(1, ) + self.input_shape))
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(64, activation='tanh'))
model.add(Dense(self.nb_actions))
model.add(Activation('linear'))
# print(model.summary())
print(model.summary())
return model
......@@ -555,4 +555,4 @@ class DQNAgentOverOptions(DQNAgent):
for node_index in invalid_node_indices:
q_values[node_index] = -np.inf
return q_values
\ No newline at end of file
return q_values
......@@ -48,6 +48,7 @@ def high_level_policy_training(nb_steps=25000,
target_model_update=1e-3,
delta_clip=100,
low_level_policies=options.maneuvers)
#gamma=1)
if load_weights:
agent.load_model(save_path)
......
......@@ -31,7 +31,7 @@ class ManeuverBase(EpisodicEnvBase):
# with subtraction.
# TODO: remove or to provide additional functionality, keep _cost_weights in ManeuverBase here (see other TODOs in simple_intersection_env regarding _cost_weights).
_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)
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
......
......@@ -50,13 +50,16 @@ class KeepLane(ManeuverBase):
@staticmethod
def _features_dim_reduction(features_tuple):
return extract_ego_features(features_tuple, 'v', 'v_ref', 'e_y', 'psi', 'v tan(psi/L)', 'theta', 'lane', 'acc', 'psi_dot')
return extract_ego_features(features_tuple, 'v', 'v_ref', 'e_y',
'psi', 'v tan(psi/L)', 'theta', 'lane',
'acc', 'psi_dot')
@property
def initiation_condition(self):
"""a virtual function (property) from ManeuverBase.
As KeepLane is a default maneuver, it has to be activated to be chosen at any time, state, and condition
(refer to initiation_condition of ManeuverBase for the usual case.
As KeepLane is a default maneuver, it has to be activated to be
chosen at any time, state, and condition (refer to
initiation_condition of ManeuverBase for the usual case).
:returns True.
"""
......@@ -243,35 +246,107 @@ 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))",
LTLProperty("G ( (in_stop_region and stopped_now) U (highest_priority and intersection_is_clear))",
None, 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) )",
LTLProperty("G ( not (in_intersection and highest_priority and intersection_is_clear) )",
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))
"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))
150, 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
self._update_param()
self._target_lane = self.env.ego.APs['lane']
def _update_param(self):
if self.env.ego.APs['highest_priority'] and self.env.ego.APs['intersection_is_clear']:
self._v_ref = rd.speed_limit
else:
self._v_ref = 0
def generate_learning_scenario(self):
n_others = 0 if np.random.rand() <= 0 else np.random.randint(1, 4)
self.generate_scenario(
n_others_range=(n_others, n_others),
ego_pos_range=rd.hlanes.stop_region,
n_others_stopped_in_stop_region=n_others,
ego_v_upper_lim=0,
ego_perturb_lim=(rd.hlanes.width / 4, np.pi / 6),
ego_heading_towards_lane_centre=True)
max_waited_count = 0
min_waited_count = 1
for veh in self.env.vehs[1:]:
max_waited_count = max(max_waited_count, veh.waited_count)
min_waited_count = min(min_waited_count, veh.waited_count)
min_waited_count = min(min_waited_count, max_waited_count)
self._extra_action_weights_flag = False
if np.random.rand() <= 0.5:
self.env.ego.waited_count = np.random.randint(0, min_waited_count+1)
else:
self.env.ego.waited_count = np.random.randint(min_waited_count, max_waited_count + 21)
self.env.init_APs(False)
self._reward_in_goal = 200
self._enable_low_level_training_properties = True
self._extra_action_weights_flag = True
@property
def extra_termination_condition(self):
if self._enable_low_level_training_properties: # activated only for the low-level training.
if self.env.ego.APs['highest_priority'] and self.env.ego.APs['intersection_is_clear'] \
and np.random.rand() <= 0.1 and self.env.ego.v <= self._v_ref / 10 \
and self.env.ego.acc < 0:
self._extra_r_terminal = - 100
return True
else:
self._extra_r_terminal = None
return False
return False
@staticmethod
def _features_dim_reduction(features_tuple):
return extract_ego_features(
features_tuple, 'v', 'v_ref', 'e_y', 'psi', 'v tan(psi/L)', 'theta', 'lane', 'acc', 'psi_dot',
'pos_stop_region', 'intersection_is_clear', 'highest_priority')
class ManualWait(ManeuverBase):
_reward_in_goal = None
_terminate_in_goal = True
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...
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))
def _init_param(self):
self._v_ref = 0
self._target_lane = self.env.ego.APs['lane']
def _low_level_manual_policy(self):
return (0, 0) # Do nothing during "Wait" but just wait until the highest priority is given.
# @staticmethod
# def _features_dim_reduction(features_tuple):
# return extract_ego_features(
# features_tuple, 'v', 'v_ref', 'e_y', 'psi', 'v tan(psi/L)', 'theta', 'lane', 'acc', 'psi_dot',
# 'pos_stop_region', 'intersection_is_clear', 'highest_priority')
class Left(ManeuverBase):
......
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