Merge branch '141-different-agent-classes' into 'master'

#141 different agent classes

See merge request !4

RLLib_training/RailEnvRLLibWrapper.py

@@ -2,8 +2,9 @@ import numpy as np
 from ray.rllib.env.multi_agent_env import MultiAgentEnv
 from ray.rllib.utils.seed import seed as set_seed
 
-from flatland.envs.generators import complex_rail_generator, random_rail_generator
 from flatland.envs.rail_env import RailEnv
+from flatland.envs.rail_generators import complex_rail_generator, random_rail_generator
+from flatland.envs.schedule_generators import complex_schedule_generator, random_schedule_generator
 
 
 class RailEnvRLLibWrapper(MultiAgentEnv):
@@ -25,19 +26,25 @@ class RailEnvRLLibWrapper(MultiAgentEnv):
                                                          min_dist=config['min_dist'],
                                                          nr_extra=config['nr_extra'],
                                                          seed=config['seed'] * (1 + vector_index))
+            self.schedule_generator = complex_schedule_generator()
 
         elif config['rail_generator'] == "random_rail_generator":
             self.rail_generator = random_rail_generator()
+            self.schedule_generator = random_schedule_generator()
         elif config['rail_generator'] == "load_env":
             self.predefined_env = True
             self.rail_generator = random_rail_generator()
+            self.schedule_generator = random_schedule_generator()
         else:
             raise (ValueError, f'Unknown rail generator: {config["rail_generator"]}')
 
         set_seed(config['seed'] * (1 + vector_index))
         self.env = RailEnv(width=config["width"], height=config["height"],
                            number_of_agents=config["number_of_agents"],
-                           obs_builder_object=config['obs_builder'], rail_generator=self.rail_generator)
+                           obs_builder_object=config['obs_builder'],
+                           rail_generator=self.rail_generator,
+                           schedule_generator=self.schedule_generator
+                           )
 
         if self.predefined_env:
             self.env.load_resource('torch_training.railway', 'complex_scene.pkl')

scoring/utils/misc_utils.py

 import random
 import time
-from collections import deque
 
 import numpy as np
-from flatland.envs.generators import complex_rail_generator, rail_from_file
-from flatland.envs.observations import GlobalObsForRailEnv, TreeObsForRailEnv
+
+from flatland.envs.observations import TreeObsForRailEnv
 from flatland.envs.predictions import ShortestPathPredictorForRailEnv
-from flatland.utils.rendertools import RenderTool
 from flatland.envs.rail_env import RailEnv
-
-from utils.observation_utils import norm_obs_clip, split_tree, max_lt
+from flatland.envs.rail_generators import complex_rail_generator, rail_from_file
+from flatland.envs.schedule_generators import complex_schedule_generator
+from flatland.utils.rendertools import RenderTool
 
 # Time factor to test the max time allowed for an env.
 max_time_factor = 1
 
+
 def printProgressBar(iteration, total, prefix='', suffix='', decimals=1, length=100, fill='*'):
     """
     Call in a loop to create terminal progress bar
@@ -54,7 +54,6 @@ def run_test(parameters, agent, observation_builder=None, observation_wrapper=No
     random.seed(parameters[3])
     np.random.seed(parameters[3])
 
-
     printProgressBar(0, nr_trials_per_test, prefix='Progress:', suffix='Complete', length=20)
     for trial in range(nr_trials_per_test):
         # Reset the env
@@ -73,7 +72,6 @@ def run_test(parameters, agent, observation_builder=None, observation_wrapper=No
             for a in range(env.get_num_agents()):
                 obs[a] = observation_wrapper(obs[a])
 
-
         # Run episode
         trial_score = 0
         max_steps = int(max_time_factor * (env.height + env.width))
@@ -115,6 +113,7 @@ def create_testfiles(parameters, test_nr=0, nr_trials_per_test=100):
                   rail_generator=complex_rail_generator(nr_start_goal=nr_paths, nr_extra=5, min_dist=min_dist,
                                                         max_dist=99999,
                                                         seed=parameters[3]),
+                  schedule_generator=complex_schedule_generator(),
                   obs_builder_object=TreeObsForRailEnv(max_depth=2),
                   number_of_agents=parameters[2])
     printProgressBar(0, nr_trials_per_test, prefix='Progress:', suffix='Complete', length=20)
@@ -151,6 +150,7 @@ def render_test(parameters, test_nr=0, nr_examples=5):
         env_renderer.close_window()
     return
 
+
 def run_test_sequential(parameters, agent, test_nr=0, tree_depth=3):
     # Parameter initialization
     features_per_node = 9
@@ -168,7 +168,6 @@ def run_test_sequential(parameters, agent, test_nr=0, tree_depth=3):
     random.seed(parameters[3])
     np.random.seed(parameters[3])
 
-
     printProgressBar(0, nr_trials_per_test, prefix='Progress:', suffix='Complete', length=20)
     for trial in range(nr_trials_per_test):
         # Reset the env
@@ -177,7 +176,8 @@ def run_test_sequential(parameters, agent, test_nr=0, tree_depth=3):
         env = RailEnv(width=3,
                       height=3,
                       rail_generator=rail_from_file(file_name),
-                      obs_builder_object=TreeObsForRailEnv(max_depth=tree_depth, predictor=ShortestPathPredictorForRailEnv()),
+                      obs_builder_object=TreeObsForRailEnv(max_depth=tree_depth,
+                                                           predictor=ShortestPathPredictorForRailEnv()),
                       number_of_agents=1,
                       )
 
@@ -185,7 +185,7 @@ def run_test_sequential(parameters, agent, test_nr=0, tree_depth=3):
         done = env.dones
         # Run episode
         trial_score = 0
-        max_steps = int(max_time_factor* (env.height + env.width))
+        max_steps = int(max_time_factor * (env.height + env.width))
         for step in range(max_steps):
 
             # Action

sequential_agent/run_test.py

-from sequential_agent.simple_order_agent import OrderedAgent
-from flatland.envs.generators import rail_from_file, complex_rail_generator
+import numpy as np
+
 from flatland.envs.observations import TreeObsForRailEnv
 from flatland.envs.predictions import ShortestPathPredictorForRailEnv
 from flatland.envs.rail_env import RailEnv
+from flatland.envs.rail_generators import complex_rail_generator
+from flatland.envs.schedule_generators import complex_schedule_generator
 from flatland.utils.rendertools import RenderTool
-import numpy as np
+from sequential_agent.simple_order_agent import OrderedAgent
 
 np.random.seed(2)
 """
@@ -29,6 +31,7 @@ env = RailEnv(width=x_dim,
               rail_generator=complex_rail_generator(nr_start_goal=n_goals, nr_extra=5, min_dist=min_dist,
                                                     max_dist=99999,
                                                     seed=0),
+              schedule_generator=complex_schedule_generator(),
               obs_builder_object=TreeObsForRailEnv(max_depth=1, predictor=ShortestPathPredictorForRailEnv()),
               number_of_agents=n_agents)
 env.reset(True, True)

torch_training/multi_agent_inference.py

@@ -3,14 +3,14 @@ from collections import deque
 
 import numpy as np
 import torch
-from flatland.envs.generators import rail_from_file
-from flatland.envs.rail_env import RailEnv
-from flatland.utils.rendertools import RenderTool
 from importlib_resources import path
 from observation_builders.observations import TreeObsForRailEnv
 from predictors.predictions import ShortestPathPredictorForRailEnv
 
 import torch_training.Nets
+from flatland.envs.rail_env import RailEnv
+from flatland.envs.rail_generators import rail_from_file
+from flatland.utils.rendertools import RenderTool
 from torch_training.dueling_double_dqn import Agent
 from utils.observation_utils import normalize_observation
 
@@ -41,6 +41,7 @@ env = RailEnv(width=x_dim,
               rail_generator=complex_rail_generator(nr_start_goal=n_goals, nr_extra=2, min_dist=min_dist,
                                                     max_dist=99999,
                                                     seed=0),
+              schedule_generator=complex_schedule_generator(),
               obs_builder_object=observation_helper,
               number_of_agents=n_agents)
 env.reset(True, True)

torch_training/multi_agent_training.py

@@ -7,15 +7,16 @@ from collections import deque
 import matplotlib.pyplot as plt
 import numpy as np
 import torch
-# Import Flatland/ Observations and Predictors
-from flatland.envs.generators import complex_rail_generator
-from flatland.envs.observations import TreeObsForRailEnv
-from flatland.envs.predictions import ShortestPathPredictorForRailEnv
-from flatland.envs.rail_env import RailEnv
 from importlib_resources import path
 
 # Import Torch and utility functions to normalize observation
 import torch_training.Nets
+from flatland.envs.observations import TreeObsForRailEnv
+from flatland.envs.predictions import ShortestPathPredictorForRailEnv
+from flatland.envs.rail_env import RailEnv
+from flatland.envs.rail_generators import complex_rail_generator
+# Import Flatland/ Observations and Predictors
+from flatland.envs.schedule_generators import complex_schedule_generator
 from torch_training.dueling_double_dqn import Agent
 from utils.observation_utils import normalize_observation
 
@@ -56,6 +57,7 @@ def main(argv):
                   rail_generator=complex_rail_generator(nr_start_goal=n_goals, nr_extra=2, min_dist=min_dist,
                                                         max_dist=99999,
                                                         seed=0),
+                  schedule_generator=complex_schedule_generator(),
                   obs_builder_object=observation_helper,
                   number_of_agents=n_agents)
     env.reset(True, True)
@@ -113,6 +115,7 @@ def main(argv):
                           rail_generator=complex_rail_generator(nr_start_goal=n_goals, nr_extra=2, min_dist=min_dist,
                                                                 max_dist=99999,
                                                                 seed=0),
+                          schedule_generator=complex_schedule_generator(),
                           obs_builder_object=observation_helper,
                           number_of_agents=n_agents)
 

torch_training/multi_agent_two_time_step_training.py

@@ -7,15 +7,16 @@ from collections import deque
 import matplotlib.pyplot as plt
 import numpy as np
 import torch
-# Import Flatland/ Observations and Predictors
-from flatland.envs.generators import complex_rail_generator
-from flatland.envs.observations import TreeObsForRailEnv
-from flatland.envs.predictions import ShortestPathPredictorForRailEnv
-from flatland.envs.rail_env import RailEnv
 from importlib_resources import path
 
 # Import Torch and utility functions to normalize observation
 import torch_training.Nets
+from flatland.envs.observations import TreeObsForRailEnv
+from flatland.envs.predictions import ShortestPathPredictorForRailEnv
+from flatland.envs.rail_env import RailEnv
+from flatland.envs.rail_generators import complex_rail_generator
+# Import Flatland/ Observations and Predictors
+from flatland.envs.schedule_generators import complex_schedule_generator
 from torch_training.dueling_double_dqn import Agent
 from utils.observation_utils import norm_obs_clip, split_tree
 
@@ -52,6 +53,7 @@ def main(argv):
                   rail_generator=complex_rail_generator(nr_start_goal=n_goals, nr_extra=5, min_dist=min_dist,
                                                         max_dist=99999,
                                                         seed=0),
+                  schedule_generator=complex_schedule_generator(),
                   obs_builder_object=observation_helper,
                   number_of_agents=n_agents)
     env.reset(True, True)
@@ -109,6 +111,7 @@ def main(argv):
                           rail_generator=complex_rail_generator(nr_start_goal=n_goals, nr_extra=5, min_dist=min_dist,
                                                                 max_dist=99999,
                                                                 seed=0),
+                          schedule_generator=complex_schedule_generator(),
                           obs_builder_object=TreeObsForRailEnv(max_depth=3,
                                                                predictor=ShortestPathPredictorForRailEnv()),
                           number_of_agents=n_agents)

torch_training/render_agent_behavior.py

@@ -3,14 +3,15 @@ from collections import deque
 
 import numpy as np
 import torch
-from flatland.envs.generators import complex_rail_generator
+from importlib_resources import path
+
+import torch_training.Nets
 from flatland.envs.observations import TreeObsForRailEnv
 from flatland.envs.predictions import ShortestPathPredictorForRailEnv
 from flatland.envs.rail_env import RailEnv
+from flatland.envs.rail_generators import complex_rail_generator
+from flatland.envs.schedule_generators import complex_schedule_generator
 from flatland.utils.rendertools import RenderTool
-from importlib_resources import path
-
-import torch_training.Nets
 from torch_training.dueling_double_dqn import Agent
 from utils.observation_utils import norm_obs_clip, split_tree
 
@@ -37,6 +38,7 @@ env = RailEnv(width=x_dim,
               rail_generator=complex_rail_generator(nr_start_goal=n_goals, nr_extra=5, min_dist=min_dist,
                                                     max_dist=99999,
                                                     seed=0),
+              schedule_generator=complex_schedule_generator(),
               obs_builder_object=TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv()),
               number_of_agents=n_agents)
 env.reset(True, True)

torch_training/training_navigation.py

@@ -7,11 +7,12 @@ import matplotlib.pyplot as plt
 import numpy as np
 import torch
 from dueling_double_dqn import Agent
-from flatland.envs.generators import complex_rail_generator
+
 from flatland.envs.observations import TreeObsForRailEnv
 from flatland.envs.rail_env import RailEnv
+from flatland.envs.rail_generators import complex_rail_generator
+from flatland.envs.schedule_generators import complex_schedule_generator
 from flatland.utils.rendertools import RenderTool
-
 from utils.observation_utils import norm_obs_clip, split_tree
 
 
@@ -44,6 +45,7 @@ def main(argv):
                   rail_generator=complex_rail_generator(nr_start_goal=n_goals, nr_extra=5, min_dist=min_dist,
                                                         max_dist=99999,
                                                         seed=0),
+                  schedule_generator=complex_schedule_generator(),
                   obs_builder_object=observation_builder,
                   number_of_agents=n_agents)
     env.reset(True, True)

utils/misc_utils.py

@@ -3,15 +3,16 @@ import time
 from collections import deque
 
 import numpy as np
-from flatland.envs.generators import complex_rail_generator
-from flatland.envs.observations import GlobalObsForRailEnv
-from flatland.envs.rail_env import RailEnv
 from line_profiler import LineProfiler
 
+from flatland.envs.observations import GlobalObsForRailEnv
+from flatland.envs.rail_env import RailEnv
+from flatland.envs.rail_generators import complex_rail_generator
+from flatland.envs.schedule_generators import complex_schedule_generator
 from utils.observation_utils import norm_obs_clip, split_tree
 
 
-def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '*'):
+def printProgressBar(iteration, total, prefix='', suffix='', decimals=1, length=100, fill='*'):
     """
     Call in a loop to create terminal progress bar
     @params:
@@ -31,13 +32,14 @@ def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1,
     if iteration == total:
         print('')
 
+
 class RandomAgent:
 
     def __init__(self, state_size, action_size):
         self.state_size = state_size
         self.action_size = action_size
 
-    def act(self, state, eps = 0):
+    def act(self, state, eps=0):
         """
         :param state: input is the observation of the agent
         :return: returns an action
@@ -87,6 +89,7 @@ def run_test(parameters, agent, test_nr=0, tree_depth=3):
                   rail_generator=complex_rail_generator(nr_start_goal=nr_paths, nr_extra=5, min_dist=min_dist,
                                                         max_dist=99999,
                                                         seed=parameters[3]),
+                  schedule_generator=complex_schedule_generator(),
                   obs_builder_object=GlobalObsForRailEnv(),
                   number_of_agents=parameters[2])
     max_steps = int(3 * (env.height + env.width))