diff --git a/backends/controller_base.py b/backends/controller_base.py
index ad3185664f1eab6d506a84117af20de75cbad57c..015a12c8768269003eb6d620b0b0c22d0930dba1 100644
--- a/backends/controller_base.py
+++ b/backends/controller_base.py
@@ -51,7 +51,7 @@ class ControllerBase(PolicyBase):
Returns state at end of node execution, total reward, epsiode_termination_flag, info
'''
- # TODO: this is never called when you test high-level policy rather than train...
+ # TODO: this is never called when you TEST high-level policy (w/o MCTS) rather than train...
def step_current_node(self, visualize_low_level_steps=False):
total_reward = 0
self.node_terminal_state_reached = False
diff --git a/backends/trained_policies/follow/follow_weights_actor.h5f b/backends/trained_policies/follow/follow_weights_actor.h5f
index 0f4bf96684f16bf8fc8db62dd56a301f5a96b62f..2ac985cb51323be7472bb64862d167335aa36e9d 100644
Binary files a/backends/trained_policies/follow/follow_weights_actor.h5f and b/backends/trained_policies/follow/follow_weights_actor.h5f differ
diff --git a/backends/trained_policies/follow/follow_weights_critic.h5f b/backends/trained_policies/follow/follow_weights_critic.h5f
index 2f51be39f37e0956887e930d969caf9ea421e0c2..4b713c8bc9cc34336c7cbb9dc8948f29a089eb1c 100644
Binary files a/backends/trained_policies/follow/follow_weights_critic.h5f and b/backends/trained_policies/follow/follow_weights_critic.h5f differ
diff --git a/backends/trained_policies/highlevel/highlevel_weights.h5f b/backends/trained_policies/highlevel/highlevel_weights.h5f
index b3c5347197f637d893b80c7258a6504d4368c59b..758edd04dfb997f8bc019986abdc55c291294497 100644
Binary files a/backends/trained_policies/highlevel/highlevel_weights.h5f and b/backends/trained_policies/highlevel/highlevel_weights.h5f differ
diff --git a/env/simple_intersection/simple_intersection_env.py b/env/simple_intersection/simple_intersection_env.py
index a2faa92c4b66e6d7d29e55c0b09b255903f3a863..62a85ce711b980f3e968ecb6fc75514c5c7cde04 100644
--- a/env/simple_intersection/simple_intersection_env.py
+++ b/env/simple_intersection/simple_intersection_env.py
@@ -273,7 +273,9 @@ class SimpleIntersectionEnv(RoadEnv, EpisodicEnvBase):
# stopped_car_scenario = bool(np.random.randint(0, 1)) TODO: this scenario may not work
n_others_stopped_in_stop_region = np.random.randint(
0, min(3, n_others - stopped_car_scenario))
- veh_ahead_scenario = bool(np.random.randint(0, 1)) or veh_ahead_scenario
+ veh_ahead_scenario = bool(np.random.randint(0, 2)) or veh_ahead_scenario
+ # Activate the comment below if you want to generate more veh_ahead_scenario
+ # veh_ahead_scenario = np.random.rand() <= 0.7 or veh_ahead_scenario
if n_others_stopped_in_stop_region > min(
n_others - stopped_car_scenario, 3):
diff --git a/options/simple_intersection/maneuvers.py b/options/simple_intersection/maneuvers.py
index 67c8318d6c57801566d7693a60ceb2212dcbe64e..b6625b21ff53d3e13456b30ca2421a98cec458f2 100644
--- a/options/simple_intersection/maneuvers.py
+++ b/options/simple_intersection/maneuvers.py
@@ -152,7 +152,6 @@ class Stop(ManeuverBase):
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 )",
@@ -385,22 +384,29 @@ class ChangeLane(ManeuverBase):
class Follow(ManeuverBase):
+ _reward_in_goal = None
+
_target_veh_i = None
_penalty_for_out_of_range = None
_penalty_for_change_lane = None
def _init_LTL_preconditions(self):
self._LTL_preconditions.append(
- LTLProperty("G ( veh_ahead )", self._penalty_for_out_of_range))
+ LTLProperty("G ( veh_ahead U (in_stop_region or before_but_close_to_stop_region ) )", 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))
- # 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 (has_stopped_in_stop_region and in_stop_region ) )",
+ None))
+
+ #self._LTL_preconditions.append(
+ # LTLProperty("G ( not stopped_now U (veh_ahead_stopped_now or (in_stop_region and 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,
@@ -408,14 +414,14 @@ class Follow(ManeuverBase):
def generate_learning_scenario(self):
self.generate_scenario(
- enable_LTL_preconditions=False,
- n_others_range=(1, 1),
+ n_others_range=(1, 6),
v_max_multiplier=0.75,
ego_perturb_lim=(0, 0),
veh_ahead_scenario=True)
self.env._terminate_in_goal = False
self._penalty_for_out_of_range = 200
- self._penalty_for_change_lane = 170
+ self._penalty_for_change_lane = 200
+ self._reward_in_goal = 200
self._enable_low_level_training_properties = True
self._extra_action_weights_flag = True
@@ -433,11 +439,20 @@ class Follow(ManeuverBase):
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
+ if self.env.ego.APs['before_intersection'] and \
+ not self.env.vehs[self._target_veh_i].APs['before_intersection']:
+ x = self.env.ego.x
+ if rd.hlanes.near_stop_region >= x:
+ self._v_ref = rd.speed_limit
+ elif rd.hlanes.near_stop_region < x <= rd.hlanes.stop_region_centre:
+ self._v_ref = -(rd.speed_limit / abs(rd.hlanes.near_stop_region)) * (
+ x - rd.hlanes.stop_region_centre)
+ elif x > rd.hlanes.stop_region_centre:
+ self._v_ref = 0
+ else:
+ self._v_ref = self.env.vehs[self._target_veh_i].v
else:
self._v_ref = 0
- #else:
- # self._v_ref = rd.speed_limit
def _low_level_manual_policy(self):
return self.env.aggressive_driving_policy(EGO_INDEX)
@@ -449,18 +464,29 @@ class Follow(ManeuverBase):
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:
+ if self._enable_low_level_training_properties: # activated only for the low-level training.
+ if self.env.ego.APs['has_stopped_in_stop_region'] and \
+ self.env.ego.APs['in_stop_region']:
+ self._extra_r_terminal = None
+ return True
+
+ elif (rd.speed_limit / 5 < self._v_ref) and \
+ (self.env.ego.v < self._v_ref / 2) and \
+ self.env.ego.acc < 0 and \
+ not self.env.ego.APs['veh_ahead_stopped_now']:
+ self._extra_r_terminal = -100
+ return True
+
+ else:
+ self._extra_r_terminal = None
+ return False
+
return False
def _features_dim_reduction(self, features_tuple):
ego_features = extract_ego_features(
features_tuple, 'pos_near_stop_region', 'v', 'v_ref', 'e_y', 'psi', 'v tan(psi/L)',
- 'theta', 'lane', 'acc', 'psi_dot')
+ 'theta', 'lane', 'acc', 'psi_dot', 'pos_stop_region', 'not_in_stop_region')
if self._target_veh_i is not None:
return ego_features + extract_other_veh_features(