diff --git a/scoring/utils/misc_utils.py b/scoring/utils/misc_utils.py
index b15476dda523a8663f712daf096803fdc14218df..924f216b1a7ea2a540037231b837eb9de49db6ac 100644
--- a/scoring/utils/misc_utils.py
+++ b/scoring/utils/misc_utils.py
@@ -1,19 +1,19 @@
import random
import time
-from collections import deque
import numpy as np
+
+from flatland.envs.agent_generators import complex_rail_generator_agents_placer
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.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]),
+ agent_generator=complex_rail_generator_agents_placer(),
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
diff --git a/sequential_agent/run_test.py b/sequential_agent/run_test.py
index d0b9ce70f50465a58885a9b1feb754791bb49f34..ee4e9ed076864145e64f62a8742d2e0dfc513dec 100644
--- a/sequential_agent/run_test.py
+++ b/sequential_agent/run_test.py
@@ -1,10 +1,12 @@
-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.agent_generators import complex_rail_generator_agents_placer
+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 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),
+ agent_generator=complex_rail_generator_agents_placer(),
obs_builder_object=TreeObsForRailEnv(max_depth=1, predictor=ShortestPathPredictorForRailEnv()),
number_of_agents=n_agents)
env.reset(True, True)
diff --git a/torch_training/multi_agent_inference.py b/torch_training/multi_agent_inference.py
index b310e95f19e6284cfd6fed6f195d9807551875b8..96f791d1c83751ba9c05b336f097abed3305595f 100644
--- a/torch_training/multi_agent_inference.py
+++ b/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.generators import rail_from_file
+from flatland.envs.rail_env import RailEnv
+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),
+ agent_generator=complex_rail_generator_agents_placer(),
obs_builder_object=observation_helper,
number_of_agents=n_agents)
env.reset(True, True)
diff --git a/torch_training/multi_agent_training.py b/torch_training/multi_agent_training.py
index c3c8c2b6b49dfeee1eceafa52d2bc0b4f1ff366e..ef0cae712a4deb557933d6ffb019f1124f2aceb1 100644
--- a/torch_training/multi_agent_training.py
+++ b/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
+from importlib_resources import path
+
+# Import Torch and utility functions to normalize observation
+import torch_training.Nets
# Import Flatland/ Observations and Predictors
+from flatland.envs.agent_generators import complex_rail_generator_agents_placer
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 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),
+ agent_generator=complex_rail_generator_agents_placer(),
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),
+ agent_generator=complex_rail_generator_agents_placer(),
obs_builder_object=observation_helper,
number_of_agents=n_agents)
diff --git a/torch_training/multi_agent_two_time_step_training.py b/torch_training/multi_agent_two_time_step_training.py
index 1c3225c24dba4c52756b42f41e53656b1f698be3..f41495d49681b73b103fd8cceb3b1217b05ffff2 100644
--- a/torch_training/multi_agent_two_time_step_training.py
+++ b/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
+from importlib_resources import path
+
+# Import Torch and utility functions to normalize observation
+import torch_training.Nets
# Import Flatland/ Observations and Predictors
+from flatland.envs.agent_generators import complex_rail_generator_agents_placer
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 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),
+ agent_generator=complex_rail_generator_agents_placer(),
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),
+ agent_generator=complex_rail_generator_agents_placer(),
obs_builder_object=TreeObsForRailEnv(max_depth=3,
predictor=ShortestPathPredictorForRailEnv()),
number_of_agents=n_agents)
diff --git a/torch_training/render_agent_behavior.py b/torch_training/render_agent_behavior.py
index 489501ae8ca43df9ca94a86a837e274181b207ee..38bd009111db0f13357e1fdd8c6cdb3ef0432441 100644
--- a/torch_training/render_agent_behavior.py
+++ b/torch_training/render_agent_behavior.py
@@ -3,14 +3,15 @@ from collections import deque
import numpy as np
import torch
+from importlib_resources import path
+
+import torch_training.Nets
+from flatland.envs.agent_generators import complex_rail_generator_agents_placer
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 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),
+ agent_generator=complex_rail_generator_agents_placer(),
obs_builder_object=TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv()),
number_of_agents=n_agents)
env.reset(True, True)
diff --git a/torch_training/training_navigation.py b/torch_training/training_navigation.py
index 5cc7305563076e54e25a3fb2d890e276bcbf9a38..40e5a68b1e78506c16783783537b4383fadc823a 100644
--- a/torch_training/training_navigation.py
+++ b/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.agent_generators import complex_rail_generator_agents_placer
from flatland.envs.generators import complex_rail_generator
from flatland.envs.observations import TreeObsForRailEnv
from flatland.envs.rail_env import RailEnv
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),
+ agent_generator=complex_rail_generator_agents_placer(),
obs_builder_object=observation_builder,
number_of_agents=n_agents)
env.reset(True, True)
diff --git a/utils/misc_utils.py b/utils/misc_utils.py
index 5b29c6b15f61b46062bac8d4fb6c4130fe61c6ec..6e23af202b61e924f6ecbec44089440e7bdf4e9e 100644
--- a/utils/misc_utils.py
+++ b/utils/misc_utils.py
@@ -3,15 +3,16 @@ import time
from collections import deque
import numpy as np
+from line_profiler import LineProfiler
+
+from flatland.envs.agent_generators import complex_rail_generator_agents_placer
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 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]),
+ agent_generator=complex_rail_generator_agents_placer(),
obs_builder_object=GlobalObsForRailEnv(),
number_of_agents=parameters[2])
max_steps = int(3 * (env.height + env.width))