updated files to new malfunction behavior

torch_training/multi_agent_inference.py

@@ -3,6 +3,7 @@ from collections import deque
 
 import numpy as np
 import torch
+from flatland.envs.malfunction_generators import malfunction_from_params
 from flatland.envs.observations import TreeObsForRailEnv
 from flatland.envs.predictions import ShortestPathPredictorForRailEnv
 from flatland.envs.rail_env import RailEnv
@@ -36,12 +37,12 @@ n_agents = 10
 observation_builder = TreeObsForRailEnv(max_depth=2)
 
 # Use a the malfunction generator to break agents from time to time
-stochastic_data = {'prop_malfunction': 0.0,  # Percentage of defective agents
-                   'malfunction_rate': 30,  # Rate of malfunction occurence
-                   'min_duration': 3,  # Minimal duration of malfunction
-                   'max_duration': 20  # Max duration of malfunction
+stochastic_data = {'malfunction_rate': 8000,  # Rate of malfunction occurence of single agent
+                   'min_duration': 15,  # Minimal duration of malfunction
+                   'max_duration': 50  # Max duration of malfunction
                    }
 
+
 # Custom observation builder
 TreeObservation = TreeObsForRailEnv(max_depth=2, predictor=ShortestPathPredictorForRailEnv())
 
@@ -61,7 +62,7 @@ env = RailEnv(width=x_dim,
                                                    max_rails_in_city=2),
               schedule_generator=sparse_schedule_generator(speed_ration_map),
               number_of_agents=n_agents,
-              stochastic_data=stochastic_data,  # Malfunction data generator
+              malfunction_generator_and_process_data=malfunction_from_params(stochastic_data),
               obs_builder_object=TreeObservation)
 env.reset(True, True)
 

torch_training/multi_agent_training.py

@@ -2,9 +2,11 @@ import getopt
 import random
 import sys
 from collections import deque
-
 # make sure the root path is in system path
 from pathlib import Path
+
+from flatland.envs.malfunction_generators import malfunction_from_params
+
 base_dir = Path(__file__).resolve().parent.parent
 sys.path.append(str(base_dir))
 
@@ -41,10 +43,9 @@ def main(argv):
 
 
     # Use a the malfunction generator to break agents from time to time
-    stochastic_data = {'prop_malfunction': 0.0,  # Percentage of defective agents
-                       'malfunction_rate': 30,  # Rate of malfunction occurence
-                       'min_duration': 3,  # Minimal duration of malfunction
-                       'max_duration': 20  # Max duration of malfunction
+    stochastic_data = {'malfunction_rate': 8000,  # Rate of malfunction occurence of single agent
+                       'min_duration': 15,  # Minimal duration of malfunction
+                       'max_duration': 50  # Max duration of malfunction
                        }
 
     # Custom observation builder
@@ -66,7 +67,7 @@ def main(argv):
                                                        max_rails_in_city=3),
                   schedule_generator=sparse_schedule_generator(speed_ration_map),
                   number_of_agents=n_agents,
-                  stochastic_data=stochastic_data,  # Malfunction data generator
+                  malfunction_generator_and_process_data=malfunction_from_params(stochastic_data),
                   obs_builder_object=TreeObservation)
 
     # After training we want to render the results so we also load a renderer

torch_training/render_agent_behavior.py

@@ -3,6 +3,7 @@ from collections import deque
 
 import numpy as np
 import torch
+from flatland.envs.malfunction_generators import malfunction_from_params
 from flatland.envs.observations import TreeObsForRailEnv
 from flatland.envs.rail_env import RailEnv
 from flatland.envs.rail_generators import sparse_rail_generator
@@ -37,10 +38,9 @@ min_dist = 5
 observation_builder = TreeObsForRailEnv(max_depth=2)
 
 # Use a the malfunction generator to break agents from time to time
-stochastic_data = {'prop_malfunction': 0.0,  # Percentage of defective agents
-                   'malfunction_rate': 30,  # Rate of malfunction occurence
-                   'min_duration': 3,  # Minimal duration of malfunction
-                   'max_duration': 20  # Max duration of malfunction
+stochastic_data = {'malfunction_rate': 80,  # Rate of malfunction occurence of single agent
+                   'min_duration': 15,  # Minimal duration of malfunction
+                   'max_duration': 50  # Max duration of malfunction
                    }
 
 # Custom observation builder
@@ -59,10 +59,10 @@ env = RailEnv(width=x_dim,
                                                    seed=1,  # Random seed
                                                    grid_mode=False,
                                                    max_rails_between_cities=2,
-                                                   max_rails_in_city=2),
+                                                   max_rails_in_city=4),
               schedule_generator=sparse_schedule_generator(speed_ration_map),
               number_of_agents=n_agents,
-              stochastic_data=stochastic_data,  # Malfunction data generator
+              malfunction_generator_and_process_data=malfunction_from_params(stochastic_data),
               obs_builder_object=TreeObservation)
 env.reset()
 

torch_training/training_navigation.py

@@ -2,9 +2,11 @@ import getopt
 import random
 import sys
 from collections import deque
-
 # make sure the root path is in system path
 from pathlib import Path
+
+from flatland.envs.malfunction_generators import malfunction_from_params
+
 base_dir = Path(__file__).resolve().parent.parent
 sys.path.append(str(base_dir))
 
@@ -40,10 +42,9 @@ def main(argv):
 
 
     # Use a the malfunction generator to break agents from time to time
-    stochastic_data = {'prop_malfunction': 0.0,  # Percentage of defective agents
-                       'malfunction_rate': 30,  # Rate of malfunction occurence
-                       'min_duration': 3,  # Minimal duration of malfunction
-                       'max_duration': 20  # Max duration of malfunction
+    stochastic_data = {'malfunction_rate': 8000,  # Rate of malfunction occurence of single agent
+                       'min_duration': 15,  # Minimal duration of malfunction
+                       'max_duration': 50  # Max duration of malfunction
                        }
 
     # Custom observation builder
@@ -65,7 +66,8 @@ def main(argv):
                                                        max_rails_in_city=3),
                   schedule_generator=sparse_schedule_generator(speed_ration_map),
                   number_of_agents=n_agents,
-                  stochastic_data=stochastic_data,  # Malfunction data generator
+                  malfunction_generator_and_process_data=malfunction_from_params(stochastic_data),
+                  # Malfunction data generator
                   obs_builder_object=TreeObservation)
 
     # After training we want to render the results so we also load a renderer