Merge branch 'master' into retraining_wait_maneuver

env/simple_intersection/simple_intersection_env.py

@@ -53,9 +53,8 @@ class SimpleIntersectionEnv(RoadEnv, EpisodicEnvBase):
     # can be set to a specific value which may be different than the default.
     # TODO: check _cost_weights in both here and ManeuverBase. The _cost_weights has to be substituted to here, but it doesn't sometimes.
     # TODO: set a functionality of setting _cost_weights for low and high level training separately.
-    _cost_weights = (0, 0, 0, 0, 0, 0, 0, 0)
-                    #(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)
+    _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

high_level_policy_main.py

@@ -47,7 +47,8 @@ def high_level_policy_training(nb_steps=25000,
         nb_actions=options.get_number_of_nodes(),
         target_model_update=1e-3,
         delta_clip=100,
-        low_level_policies=options.maneuvers)
+        low_level_policies=options.maneuvers,
+        gamma=1)
 
     if load_weights:
         agent.load_model(save_path)
@@ -77,7 +78,8 @@ def high_level_policy_testing(nb_episodes_for_test=100,
     agent = DQNLearner(
         input_shape=(50, ),
         nb_actions=options.get_number_of_nodes(),
-        low_level_policies=options.maneuvers)
+        low_level_policies=options.maneuvers,
+        gamma=1)
 
     if pretrained:
         trained_agent_file = "backends/trained_policies/highlevel/" + trained_agent_file
@@ -99,7 +101,8 @@ def evaluate_high_level_policy(nb_episodes_for_test=100,
     agent = DQNLearner(
         input_shape=(50, ),
         nb_actions=options.get_number_of_nodes(),
-        low_level_policies=options.maneuvers)
+        low_level_policies=options.maneuvers,
+        gamma=1)
 
     if pretrained:
         trained_agent_file = "backends/trained_policies/highlevel/" + trained_agent_file

options/simple_intersection/maneuver_base.py

@@ -30,9 +30,8 @@ class ManeuverBase(EpisodicEnvBase):
     # as a negative reward, so a cost will be summed up to the reward
     # 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 = (0, 0, 0, 0, 0, 0, 0, 0)
-            #(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)
+    _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)
 
     _extra_r_terminal = None
     _extra_r_on_timeout = None

options/simple_intersection/maneuvers.py

@@ -243,34 +243,81 @@ 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 _low_level_manual_policy(self):
-        return (0, 0)  # Do nothing during "Wait" but just wait until the highest priority is given.
+    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
 
-#    @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')
+    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 Left(ManeuverBase):