diff --git a/changelog.md b/changelog.md
index 109f748dbd7360e86409f782ef2ede56ecfaa089..1099488b3c03d3045c0e5cfdc8f661a97a9ff365 100644
--- a/changelog.md
+++ b/changelog.md
@@ -12,7 +12,7 @@ Changes since Flatland 2.0.0
- by default the reset method of RailEnv is not called in the constructor of RailEnv anymore. Therefore the reset method needs to be called after the creation of a RailEnv object
### Changes in schedule generation
-- return value of schedule generator has changed to the named tuple Schedule
+- return value of schedule generator has changed to the named tuple `Schedule`
Changes since Flatland 1.0.0
--------------------------
diff --git a/docs/specifications/railway.md b/docs/specifications/railway.md
index 0299badb5ea8743a92a8aecf7f5c6cedb060a98d..3623ea450f74c5075bf0b1b9500cbddb6e406cb3 100644
--- a/docs/specifications/railway.md
+++ b/docs/specifications/railway.md
@@ -697,3 +697,17 @@ RailEnv.step()
self.get()
...
```
+
+
+### Maximum number of allowed time steps in an episode
+
+Whenever the schedule within RailEnv is generated, the maximum number of allowed time steps in an episode is calculated
+according to the following formula:
+
+```python
+
+RailEnv._max_episode_steps = timedelay_factor * alpha * (env.width + env.height + ratio_nr_agents_to_nr_cities)
+
+```
+
+where the following default values are used `timedelay_factor=4`, `alpha=2` and `ratio_nr_agents_to_nr_cities=20`
diff --git a/flatland/envs/rail_env.py b/flatland/envs/rail_env.py
index 3be7940ef97542664cbc6d2e8850f604a64f38bf..58f807ea43309af37fe669d88624c9fd2235efa4 100644
--- a/flatland/envs/rail_env.py
+++ b/flatland/envs/rail_env.py
@@ -169,7 +169,7 @@ class RailEnv(Environment):
self.obs_builder = obs_builder_object
self.obs_builder.set_env(self)
- self._max_episode_steps = None
+ self._max_episode_steps: Optional[int] = None
self._elapsed_steps = 0
self.dones = dict.fromkeys(list(range(number_of_agents)) + ["__all__"], False)
@@ -249,6 +249,35 @@ class RailEnv(Environment):
"""
self.agents = EnvAgent.list_from_static(self.agents_static)
+ @staticmethod
+ def compute_max_episode_steps(width: int, height: int, timedelay_factor: int = 4, alpha: int = 2,
+ 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
+ timedelay_factor : int, optional
+ timedelay_factor
+ alpha : int, optional
+ alpha
+
+ Returns
+ -------
+ max_episode_steps: int
+ maximum number of episode steps
+
+ """
+ return int(timedelay_factor * alpha * (width + height + ratio_nr_agents_to_nr_cities))
+
def reset(self, regen_rail=True, replace_agents=True, activate_agents=False, random_seed=None):
""" if regen_rail then regenerate the rails.
if replace_agents then regenerate the agents static.
@@ -282,7 +311,14 @@ class RailEnv(Environment):
# why do we need static agents? could we it more elegantly?
schedule = self.schedule_generator(self.rail, self.get_num_agents(), agents_hints, self.num_resets)
self.agents_static = EnvAgentStatic.from_lists(schedule)
- self._max_episode_steps = schedule.max_episode_steps
+
+ 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)
self.restart_agents()
diff --git a/flatland/envs/schedule_generators.py b/flatland/envs/schedule_generators.py
index d4f8ed7ed8219dbb248b32d8ee266be0ccdb5e79..85beea071aebd1c39cff22a3895b6f036047718e 100644
--- a/flatland/envs/schedule_generators.py
+++ b/flatland/envs/schedule_generators.py
@@ -42,29 +42,6 @@ def speed_initialization_helper(nb_agents: int, speed_ratio_map: Mapping[float,
return list(map(lambda index: speeds[index], np.random.choice(nb_classes, nb_agents, p=speed_ratios)))
-def compute_max_episode_steps(width: int,
- height: int,
- ratio_nr_agents_to_nr_cities: float = 20.0,
- timedelay_factor: int = 4,
- alpha: int = 2) -> int:
- """
-
- The method computes the max number of episode steps allowed
- Parameters
- ----------
- width: width of environment
- height: height of environment
- ratio_nr_agents_to_nr_cities: number_of_agents/number_of_cities (default is 20)
- timedelay_factor
- alpha
-
- Returns max number of episode steps
- -------
-
- """
- return int(timedelay_factor * alpha * (width + height + ratio_nr_agents_to_nr_cities))
-
-
def complex_schedule_generator(speed_ratio_map: Mapping[float, float] = None, seed: int = 1) -> ScheduleGenerator:
def generator(rail: GridTransitionMap, num_agents: int, hints: Any = None, num_resets: int = 0) -> Schedule:
@@ -82,10 +59,8 @@ def complex_schedule_generator(speed_ratio_map: Mapping[float, float] = None, se
else:
speeds = [1.0] * len(agents_position)
- max_episode_steps = compute_max_episode_steps(width=rail.width, height=rail.height)
return Schedule(agent_positions=agents_position, agent_directions=agents_direction,
- agent_targets=agents_target, agent_speeds=speeds, agent_malfunction_rates=None,
- max_episode_steps=max_episode_steps)
+ agent_targets=agents_target, agent_speeds=speeds, agent_malfunction_rates=None)
return generator
@@ -151,11 +126,8 @@ def sparse_schedule_generator(speed_ratio_map: Mapping[float, float] = None, see
else:
speeds = [1.0] * len(agents_position)
- max_episode_steps = compute_max_episode_steps(width=rail.width, height=rail.height,
- ratio_nr_agents_to_nr_cities=num_agents/len(city_orientations))
return Schedule(agent_positions=agents_position, agent_directions=agents_direction,
- agent_targets=agents_target, agent_speeds=speeds, agent_malfunction_rates=None,
- max_episode_steps=max_episode_steps)
+ agent_targets=agents_target, agent_speeds=speeds, agent_malfunction_rates=None)
return generator
@@ -182,8 +154,6 @@ def random_schedule_generator(speed_ratio_map: Optional[Mapping[float, float]] =
np.random.seed(_runtime_seed)
- max_episode_steps = compute_max_episode_steps(width=rail.width, height=rail.height)
-
valid_positions = []
for r in range(rail.height):
for c in range(rail.width):
@@ -191,14 +161,12 @@ 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,
- max_episode_steps=max_episode_steps)
+ agent_targets=[], agent_speeds=[], agent_malfunction_rates=None)
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,
- max_episode_steps=max_episode_steps)
+ agent_targets=[], agent_speeds=[], agent_malfunction_rates=None)
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)]
@@ -257,8 +225,7 @@ def random_schedule_generator(speed_ratio_map: Optional[Mapping[float, float]] =
agents_speed = speed_initialization_helper(num_agents, speed_ratio_map, seed=_runtime_seed)
return Schedule(agent_positions=agents_position, agent_directions=agents_direction,
- agent_targets=agents_target, agent_speeds=agents_speed, agent_malfunction_rates=None,
- max_episode_steps=max_episode_steps)
+ agent_targets=agents_target, agent_speeds=agents_speed, agent_malfunction_rates=None)
return generator
@@ -303,10 +270,9 @@ def schedule_from_file(filename, load_from_package=None) -> ScheduleGenerator:
else:
agents_speed = None
agents_malfunction = None
- max_episode_steps = compute_max_episode_steps(width=rail.width, height=rail.height)
return Schedule(agent_positions=agents_position, agent_directions=agents_direction,
agent_targets=agents_target, agent_speeds=agents_speed,
- agent_malfunction_rates=agents_malfunction, max_episode_steps=max_episode_steps)
+ agent_malfunction_rates=agents_malfunction)
return generator
diff --git a/flatland/envs/schedule_utils.py b/flatland/envs/schedule_utils.py
index b8f28a47785f9bdf30b06ce96d3012bb383835cb..e89f170dbb87388bcecbc6b2e176ba277162a4db 100644
--- a/flatland/envs/schedule_utils.py
+++ b/flatland/envs/schedule_utils.py
@@ -1,8 +1,10 @@
-import collections
+from typing import List, NamedTuple
-Schedule = collections.namedtuple('Schedule', 'agent_positions '
- 'agent_directions '
- 'agent_targets '
- 'agent_speeds '
- 'agent_malfunction_rates '
- 'max_episode_steps')
+from flatland.core.grid.grid4 import Grid4TransitionsEnum
+from flatland.core.grid.grid_utils import IntVector2DArray
+
+Schedule = NamedTuple('Schedule', [('agent_positions', IntVector2DArray),
+ ('agent_directions', List[Grid4TransitionsEnum]),
+ ('agent_targets', IntVector2DArray),
+ ('agent_speeds', List[float]),
+ ('agent_malfunction_rates', List[int])])
diff --git a/tests/test_flatland_malfunction.py b/tests/test_flatland_malfunction.py
index e694275bc353e620f0357101bc5fd11a5c273212..22db31572fd6d0013fa799037ec6f021e90334aa 100644
--- a/tests/test_flatland_malfunction.py
+++ b/tests/test_flatland_malfunction.py
@@ -151,21 +151,22 @@ def test_malfunction_process_statistically():
stochastic_data=stochastic_data, # Malfunction data generator
obs_builder_object=SingleAgentNavigationObs()
)
- env.reset()
+
# reset to initialize agents_static
env.reset(True, True, False, random_seed=10)
env.agents[0].target = (0, 0)
- agent_malfunction_list = [[0, 0, 0, 0, 0, 0, 0, 6, 5, 4, 3, 2, 1, 0, 0, 6, 5, 4, 3, 2],
- [0, 0, 0, 0, 0, 0, 0, 6, 5, 4, 3, 2, 1, 0, 6, 5, 4, 3, 2, 1],
+
+ agent_malfunction_list = [[0, 0, 0, 0, 0, 0, 6, 5, 4, 3, 2, 1, 0, 6, 5, 4, 3, 2, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 6, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 6, 5],
- [0, 0, 0, 0, 0, 6, 5, 4, 3, 2, 1, 0, 6, 5, 4, 3, 2, 1, 0, 0],
- [6, 6, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
- [6, 6, 5, 4, 3, 2, 1, 0, 6, 5, 4, 3, 2, 1, 0, 6, 5, 4, 3, 2],
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 5, 4, 3, 2, 1, 0, 0, 6, 5, 4],
[0, 0, 0, 0, 0, 6, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0, 6, 5, 4],
- [0, 0, 0, 0, 0, 0, 0, 0, 6, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0],
- [6, 6, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
+ [6, 6, 5, 4, 3, 2, 1, 0, 6, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0],
+ [6, 6, 5, 4, 3, 2, 1, 0, 6, 5, 4, 3, 2, 1, 0, 0, 6, 5, 4, 3],
+ [0, 0, 0, 0, 6, 5, 4, 3, 2, 1, 0, 6, 5, 4, 3, 2, 1, 0, 6, 5],
+ [0, 0, 0, 0, 0, 6, 5, 4, 3, 2, 1, 0, 0, 6, 5, 4, 3, 2, 1, 0],
+ [0, 0, 0, 0, 0, 0, 0, 6, 5, 4, 3, 2, 1, 0, 6, 5, 4, 3, 2, 1],
+ [6, 6, 6, 6, 5, 4, 3, 2, 1, 0, 0, 0, 0, 6, 5, 4, 3, 2, 1, 0]]
for step in range(20):
action_dict: Dict[int, RailEnvActions] = {}