diff --git a/changelog.md b/changelog.md
index 7232dba6c1ded713e7dcc09c4c191846a332aa9d..f9093f79ee03eea1116dc493853defc02b632643 100644
--- a/changelog.md
+++ b/changelog.md
@@ -1,6 +1,11 @@
Changelog
==========
+Changes since Flatland 2.1.0
+--------------------------
+### Changes in 'schedule_generators'
+- Schedule generators now provide the max number of steps allowed per episode
+- Pickle files generated with older versions of Flatland need to be regenerated in order to include `_max_episode_steps`
Changes since Flatland 2.0.0
--------------------------
### Changes in `EnvAgent`
diff --git a/flatland/envs/rail_env.py b/flatland/envs/rail_env.py
index dabb2609b173306342f86bc945de231d75c7d9de..6778fe8d4f081efa5baa518ecfb73a37a9878837 100644
--- a/flatland/envs/rail_env.py
+++ b/flatland/envs/rail_env.py
@@ -239,32 +239,6 @@ class RailEnv(Environment):
agent.reset()
self.active_agents = [i for i in range(len(self.agents))]
- @staticmethod
- def compute_max_episode_steps(width: int, height: int, ratio_nr_agents_to_nr_cities: float = 20.0) -> int:
- """
- compute_max_episode_steps(width, height, ratio_nr_agents_to_nr_cities, timedelay_factor, alpha)
-
- The method computes the max number of episode steps allowed
-
- Parameters
- ----------
- width : int
- width of environment
- height : int
- height of environment
- ratio_nr_agents_to_nr_cities : float, optional
- number_of_agents/number_of_cities
-
- Returns
- -------
- max_episode_steps: int
- maximum number of episode steps
-
- """
- timedelay_factor = 4
- alpha = 2
- return int(timedelay_factor * alpha * (width + height + ratio_nr_agents_to_nr_cities))
-
def action_required(self, agent):
"""
Check if an agent needs to provide an action
@@ -328,8 +302,6 @@ class RailEnv(Environment):
if optionals and 'distance_map' in optionals:
self.distance_map.set(optionals['distance_map'])
-
-
if regenerate_schedule or regenerate_rail or self.get_num_agents() == 0:
agents_hints = None
if optionals and 'agents_hints' in optionals:
@@ -339,13 +311,9 @@ class RailEnv(Environment):
self.np_random)
self.agents = EnvAgent.from_schedule(schedule)
- if agents_hints and 'city_orientations' in agents_hints:
- ratio_nr_agents_to_nr_cities = self.get_num_agents() / len(agents_hints['city_orientations'])
- self._max_episode_steps = self.compute_max_episode_steps(
- width=self.width, height=self.height,
- ratio_nr_agents_to_nr_cities=ratio_nr_agents_to_nr_cities)
- else:
- self._max_episode_steps = self.compute_max_episode_steps(width=self.width, height=self.height)
+ # Get max number of allowed time steps from schedule generator
+ # Look at the specific schedule generator used to see where this number comes from
+ self._max_episode_steps = schedule.max_episode_steps
self.agent_positions = np.zeros((self.height, self.width), dtype=int) - 1
@@ -836,7 +804,8 @@ class RailEnv(Environment):
msg_data = {
"grid": grid_data,
"agents": agent_data,
- "malfunction": malfunction_data}
+ "malfunction": malfunction_data,
+ "max_episode_steps": self._max_episode_steps}
return msgpack.packb(msg_data, use_bin_type=True)
def get_agent_state_msg(self) -> Packer:
@@ -863,7 +832,8 @@ class RailEnv(Environment):
"grid": grid_data,
"agents": agent_data,
"distance_map": distance_map_data,
- "malfunction": malfunction_data}
+ "malfunction": malfunction_data,
+ "max_episode_steps": self._max_episode_steps}
return msgpack.packb(msg_data, use_bin_type=True)
def set_full_state_msg(self, msg_data):
diff --git a/flatland/envs/schedule_generators.py b/flatland/envs/schedule_generators.py
index 7736d53c667904e23718a78a2c4601fa2574c08b..a84264e04fa2039ea09f9a53df52ab692800ff30 100644
--- a/flatland/envs/schedule_generators.py
+++ b/flatland/envs/schedule_generators.py
@@ -76,9 +76,13 @@ def complex_schedule_generator(speed_ratio_map: Mapping[float, float] = None, se
speeds = speed_initialization_helper(num_agents, speed_ratio_map, seed=_runtime_seed, np_random=np_random)
else:
speeds = [1.0] * len(agents_position)
+ # Compute max number of steps with given schedule
+ extra_time_factor = 1.5 # Factor to allow for more then minimal time
+ max_episode_steps = int(extra_time_factor * rail.height * rail.width)
return Schedule(agent_positions=agents_position, agent_directions=agents_direction,
- agent_targets=agents_target, agent_speeds=speeds, agent_malfunction_rates=None)
+ agent_targets=agents_target, agent_speeds=speeds, agent_malfunction_rates=None,
+ max_episode_steps=max_episode_steps)
return generator
@@ -163,8 +167,16 @@ def sparse_schedule_generator(speed_ratio_map: Mapping[float, float] = None, see
else:
speeds = [1.0] * len(agents_position)
+ # We add multiply factors to the max number of time steps to simplify task in Flatland challenge.
+ # These factors might change in the future.
+ timedelay_factor = 4
+ alpha = 2
+ max_episode_steps = int(
+ timedelay_factor * alpha * (rail.width + rail.height + num_agents / len(city_positions)))
+
return Schedule(agent_positions=agents_position, agent_directions=agents_direction,
- agent_targets=agents_target, agent_speeds=speeds, agent_malfunction_rates=None)
+ agent_targets=agents_target, agent_speeds=speeds, agent_malfunction_rates=None,
+ max_episode_steps=max_episode_steps)
return generator
@@ -196,12 +208,12 @@ def random_schedule_generator(speed_ratio_map: Optional[Mapping[float, float]] =
valid_positions.append((r, c))
if len(valid_positions) == 0:
return Schedule(agent_positions=[], agent_directions=[],
- agent_targets=[], agent_speeds=[], agent_malfunction_rates=None)
+ agent_targets=[], agent_speeds=[], agent_malfunction_rates=None, max_episode_steps=0)
if len(valid_positions) < num_agents:
warnings.warn("schedule_generators: len(valid_positions) < num_agents")
return Schedule(agent_positions=[], agent_directions=[],
- agent_targets=[], agent_speeds=[], agent_malfunction_rates=None)
+ agent_targets=[], agent_speeds=[], agent_malfunction_rates=None, max_episode_steps=0)
agents_position_idx = [i for i in np_random.choice(len(valid_positions), num_agents, replace=False)]
agents_position = [valid_positions[agents_position_idx[i]] for i in range(num_agents)]
@@ -259,8 +271,14 @@ def random_schedule_generator(speed_ratio_map: Optional[Mapping[float, float]] =
np_random.choice(len(valid_starting_directions), 1)[0]]
agents_speed = speed_initialization_helper(num_agents, speed_ratio_map, seed=_runtime_seed, np_random=np_random)
+
+ # Compute max number of steps with given schedule
+ extra_time_factor = 1.5 # Factor to allow for more then minimal time
+ max_episode_steps = int(extra_time_factor * rail.height * rail.width)
+
return Schedule(agent_positions=agents_position, agent_directions=agents_direction,
- agent_targets=agents_target, agent_speeds=agents_speed, agent_malfunction_rates=None)
+ agent_targets=agents_target, agent_speeds=agents_speed, agent_malfunction_rates=None,
+ max_episode_steps=max_episode_steps)
return generator
@@ -292,7 +310,11 @@ def schedule_from_file(filename, load_from_package=None) -> ScheduleGenerator:
agents = EnvAgent.load_legacy_static_agent(data["agents_static"])
else:
agents = [EnvAgent(*d[0:12]) for d in data["agents"]]
-
+ if "max_episode_steps" in data:
+ max_episode_steps = data["max_episode_steps"]
+ else:
+ # If no max time was found return 0.
+ max_episode_steps = 0
# setup with loaded data
agents_position = [a.initial_position for a in agents]
agents_direction = [a.direction for a in agents]
@@ -301,6 +323,7 @@ def schedule_from_file(filename, load_from_package=None) -> ScheduleGenerator:
agents_malfunction = [a.malfunction_data['malfunction_rate'] for a in agents]
return Schedule(agent_positions=agents_position, agent_directions=agents_direction,
- agent_targets=agents_target, agent_speeds=agents_speed, agent_malfunction_rates=None)
+ agent_targets=agents_target, agent_speeds=agents_speed, agent_malfunction_rates=None,
+ max_episode_steps=max_episode_steps)
return generator
diff --git a/flatland/envs/schedule_utils.py b/flatland/envs/schedule_utils.py
index e89f170dbb87388bcecbc6b2e176ba277162a4db..a811ea4af7d7f7faccfe16e94adf117cba05d6b8 100644
--- a/flatland/envs/schedule_utils.py
+++ b/flatland/envs/schedule_utils.py
@@ -7,4 +7,5 @@ Schedule = NamedTuple('Schedule', [('agent_positions', IntVector2DArray),
('agent_directions', List[Grid4TransitionsEnum]),
('agent_targets', IntVector2DArray),
('agent_speeds', List[float]),
- ('agent_malfunction_rates', List[int])])
+ ('agent_malfunction_rates', List[int]),
+ ('max_episode_steps', int)])
diff --git a/tests/test_flatland_envs_rail_env.py b/tests/test_flatland_envs_rail_env.py
index 5c518a27160f28a8a3518a4fe013f97324e4d41e..351a326a19c078e7f525c4b96ad8662777f7dd2d 100644
--- a/tests/test_flatland_envs_rail_env.py
+++ b/tests/test_flatland_envs_rail_env.py
@@ -80,7 +80,7 @@ def test_rail_environment_single_agent():
schedule_generator=random_schedule_generator(), number_of_agents=1,
obs_builder_object=GlobalObsForRailEnv())
- for _ in range(200):
+ for _ in range(50):
_ = rail_env.reset(False, False, True)
# We do not care about target for the moment
diff --git a/tests/test_flatland_schedule_from_file.py b/tests/test_flatland_schedule_from_file.py
new file mode 100644
index 0000000000000000000000000000000000000000..52a64a19343ad320d8c26922ed5e42e400821c73
--- /dev/null
+++ b/tests/test_flatland_schedule_from_file.py
@@ -0,0 +1,125 @@
+from test_utils import create_and_save_env
+
+from flatland.envs.rail_env import RailEnv
+from flatland.envs.rail_generators import sparse_rail_generator, random_rail_generator, complex_rail_generator, \
+ rail_from_file
+from flatland.envs.schedule_generators import sparse_schedule_generator, random_schedule_generator, \
+ complex_schedule_generator, schedule_from_file
+
+
+def test_schedule_from_file_sparse():
+ """
+ Test to see that all parameters are loaded as expected
+ Returns
+ -------
+
+ """
+ # Different agent types (trains) with different speeds.
+ speed_ration_map = {1.: 0.25, # Fast passenger train
+ 1. / 2.: 0.25, # Fast freight train
+ 1. / 3.: 0.25, # Slow commuter train
+ 1. / 4.: 0.25} # Slow freight train
+
+ # Generate Sparse test env
+ rail_generator = sparse_rail_generator(max_num_cities=5,
+ seed=1,
+ grid_mode=False,
+ max_rails_between_cities=3,
+ max_rails_in_city=6,
+ )
+ schedule_generator = sparse_schedule_generator(speed_ration_map)
+
+ create_and_save_env(file_name="./sparse_env_test.pkl", rail_generator=rail_generator,
+ schedule_generator=schedule_generator)
+
+
+ # Sparse generator
+ rail_generator = rail_from_file("./sparse_env_test.pkl")
+ schedule_generator = schedule_from_file("./sparse_env_test.pkl")
+ sparse_env_from_file = RailEnv(width=1, height=1, rail_generator=rail_generator,
+ schedule_generator=schedule_generator)
+ sparse_env_from_file.reset(True, True)
+
+ # Assert loaded agent number is correct
+ assert sparse_env_from_file.get_num_agents() == 10
+
+ # Assert max steps is correct
+ assert sparse_env_from_file._max_episode_steps == 500
+
+
+
+def test_schedule_from_file_random():
+ """
+ Test to see that all parameters are loaded as expected
+ Returns
+ -------
+
+ """
+ # Different agent types (trains) with different speeds.
+ speed_ration_map = {1.: 0.25, # Fast passenger train
+ 1. / 2.: 0.25, # Fast freight train
+ 1. / 3.: 0.25, # Slow commuter train
+ 1. / 4.: 0.25} # Slow freight train
+
+ # Generate random test env
+ rail_generator = random_rail_generator()
+ schedule_generator = random_schedule_generator(speed_ration_map)
+
+ create_and_save_env(file_name="./random_env_test.pkl", rail_generator=rail_generator,
+ schedule_generator=schedule_generator)
+
+
+ # Random generator
+ rail_generator = rail_from_file("./random_env_test.pkl")
+ schedule_generator = schedule_from_file("./random_env_test.pkl")
+ random_env_from_file = RailEnv(width=1, height=1, rail_generator=rail_generator,
+ schedule_generator=schedule_generator)
+ random_env_from_file.reset(True, True)
+
+ # Assert loaded agent number is correct
+ assert random_env_from_file.get_num_agents() == 10
+
+ # Assert max steps is correct
+ assert random_env_from_file._max_episode_steps == 1350
+
+
+
+
+def test_schedule_from_file_complex():
+ """
+ Test to see that all parameters are loaded as expected
+ Returns
+ -------
+
+ """
+ # Different agent types (trains) with different speeds.
+ speed_ration_map = {1.: 0.25, # Fast passenger train
+ 1. / 2.: 0.25, # Fast freight train
+ 1. / 3.: 0.25, # Slow commuter train
+ 1. / 4.: 0.25} # Slow freight train
+
+ # Generate complex test env
+ rail_generator = complex_rail_generator(nr_start_goal=10,
+ nr_extra=1,
+ min_dist=8,
+ max_dist=99999)
+ schedule_generator = complex_schedule_generator(speed_ration_map)
+
+ create_and_save_env(file_name="./complex_env_test.pkl", rail_generator=rail_generator,
+ schedule_generator=schedule_generator)
+
+ # Load the different envs and check the parameters
+
+
+ # Complex generator
+ rail_generator = rail_from_file("./complex_env_test.pkl")
+ schedule_generator = schedule_from_file("./complex_env_test.pkl")
+ complex_env_from_file = RailEnv(width=1, height=1, rail_generator=rail_generator,
+ schedule_generator=schedule_generator)
+ complex_env_from_file.reset(True, True)
+
+ # Assert loaded agent number is correct
+ assert complex_env_from_file.get_num_agents() == 10
+
+ # Assert max steps is correct
+ assert complex_env_from_file._max_episode_steps == 1350
diff --git a/tests/test_utils.py b/tests/test_utils.py
index e4fba2aebd795462971e8d1e8f16992c2affbac8..cae9b7fb814d63dcbd8c40678a6fb02e46d56a3b 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -6,7 +6,10 @@ from attr import attrs, attrib
from flatland.core.grid.grid4 import Grid4TransitionsEnum
from flatland.envs.agent_utils import EnvAgent, RailAgentStatus
+from flatland.envs.malfunction_generators import MalfunctionParameters, malfunction_from_params
from flatland.envs.rail_env import RailEnvActions, RailEnv
+from flatland.envs.rail_generators import RailGenerator
+from flatland.envs.schedule_generators import ScheduleGenerator
from flatland.utils.rendertools import RenderTool
@@ -131,3 +134,20 @@ def run_replay_config(env: RailEnv, test_configs: List[ReplayConfig], rendering:
replay = test_config.replay[step]
_assert(a, rewards_dict[a], replay.reward, 'reward')
+
+
+def create_and_save_env(file_name: str, schedule_generator: ScheduleGenerator, rail_generator: RailGenerator):
+ stochastic_data = MalfunctionParameters(malfunction_rate=1000, # Rate of malfunction occurence
+ min_duration=15, # Minimal duration of malfunction
+ max_duration=50 # Max duration of malfunction
+ )
+
+ env = RailEnv(width=30,
+ height=30,
+ rail_generator=rail_generator,
+ schedule_generator=schedule_generator,
+ number_of_agents=10,
+ malfunction_generator_and_process_data=malfunction_from_params(stochastic_data),
+ remove_agents_at_target=True)
+ env.reset(True, True)
+ env.save(file_name)