removed bug where agent would see its own prediction as conflict

examples/training_example.py

@@ -16,7 +16,7 @@ env = RailEnv(width=20,
               height=20,
               rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=1, min_dist=8, max_dist=99999, seed=0),
               obs_builder_object=TreeObservation,
-              number_of_agents=1)
+              number_of_agents=2)
 
 
 # Import your own Agent or use RLlib to train agents on Flatland

flatland/envs/observations.py

@@ -334,18 +334,24 @@ class TreeObsForRailEnv(ObservationBuilder):
                 int_position = coordinate_to_position(self.env.width, [position])
                 pre_step = max(0, num_steps - 1)
                 post_step = min(self.max_prediction_depth - 1, num_steps + 1)
+                # Look for opposing paths at distance num_step
                 if int_position in np.delete(self.predicted_pos[num_steps], handle):
-                    conflicting_agent = np.where(np.delete(self.predicted_pos[num_steps], handle) == int_position)[0][0]
-                    if direction != self.predicted_dir[num_steps][conflicting_agent]:
-                        potential_conflict = 1
+                    conflicting_agent = np.where(np.delete(self.predicted_pos[num_steps], handle) == int_position)
+                    for ca in conflicting_agent:
+                        if direction != self.predicted_dir[num_steps][ca[0]]:
+                            potential_conflict = 1
+                # Look for opposing paths at distance num_step-1
                 elif int_position in np.delete(self.predicted_pos[pre_step], handle):
-                    conflicting_agent = np.where(np.delete(self.predicted_pos[pre_step], handle) == int_position)[0][0]
-                    if direction != self.predicted_dir[pre_step][conflicting_agent]:
-                        potential_conflict = 1
+                    conflicting_agent = np.where(self.predicted_pos[pre_step] == int_position)
+                    for ca in conflicting_agent:
+                        if direction != self.predicted_dir[pre_step][ca[0]]:
+                            potential_conflict = 1
+                # Look for opposing paths at distance num_step+1
                 elif int_position in np.delete(self.predicted_pos[post_step], handle):
-                    conflicting_agent = np.where(np.delete(self.predicted_pos[post_step], handle) == int_position)[0][0]
-                    if direction != self.predicted_dir[post_step][conflicting_agent]:
-                        potential_conflict = 1
+                    conflicting_agent = np.where(np.delete(self.predicted_pos[post_step], handle) == int_position)
+                    for ca in conflicting_agent:
+                        if direction != self.predicted_dir[post_step][ca[0]]:
+                            potential_conflict = 1
 
             if position in self.location_has_target and position != agent.target:
                 if num_steps < other_target_encountered: