diff --git a/RLLib_training/RailEnvRLLibWrapper.py b/RLLib_training/RailEnvRLLibWrapper.py
index 989800044250d60b68c68b5d2e702b5625964024..f82cd42d9bbd836b681ff284a82f357b2760bb0c 100644
--- a/RLLib_training/RailEnvRLLibWrapper.py
+++ b/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')
diff --git a/scoring/utils/misc_utils.py b/scoring/utils/misc_utils.py
index b15476dda523a8663f712daf096803fdc14218df..dee5f47f7f8f09f253dfc3f8e3d48931df94efe7 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.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
diff --git a/sequential_agent/run_test.py b/sequential_agent/run_test.py
index d0b9ce70f50465a58885a9b1feb754791bb49f34..ecba34a67ac37e5b88b9f7fcac34ea455c690078 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.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)
diff --git a/torch_training/multi_agent_inference.py b/torch_training/multi_agent_inference.py
index b310e95f19e6284cfd6fed6f195d9807551875b8..66a37ad290ade376f682fcb2f40f1a02533537dc 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.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)
diff --git a/torch_training/multi_agent_training.py b/torch_training/multi_agent_training.py
index c3c8c2b6b49dfeee1eceafa52d2bc0b4f1ff366e..7da12e54a55b9afe802651475e43234c55fba12c 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
-# 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)
diff --git a/torch_training/multi_agent_two_time_step_training.py b/torch_training/multi_agent_two_time_step_training.py
index 1c3225c24dba4c52756b42f41e53656b1f698be3..08cd84c379fe54cd4d6b71140a96623ebe2a8cbf 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
-# 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)
diff --git a/torch_training/render_agent_behavior.py b/torch_training/render_agent_behavior.py
index 489501ae8ca43df9ca94a86a837e274181b207ee..3882cc9de4aefd89e294be18b0b2c1eee905cfcc 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 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)
diff --git a/torch_training/training_navigation.py b/torch_training/training_navigation.py
index 5cc7305563076e54e25a3fb2d890e276bcbf9a38..fb03432fea29c0b4b16a02a90cf5fe509f873f7d 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.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)
diff --git a/utils/misc_utils.py b/utils/misc_utils.py
index 5b29c6b15f61b46062bac8d4fb6c4130fe61c6ec..09b315cf2a851eadf84b1a31a0d773e46549673f 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 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))