def _launch(self):
rail_generator = sparse_rail_generator(
seed=self._config['seed'],
max_num_cities=self._config['max_num_cities'],
grid_mode=self._config['grid_mode'],
max_rails_between_cities=self._config['max_rails_between_cities'],
max_rails_in_city=self._config['max_rails_in_city'])
malfunction_generator = no_malfunction_generator()
if {
'malfunction_rate', 'malfunction_min_duration',
'malfunction_max_duration'
} <= self._config.keys():
stochastic_data = {
'malfunction_rate': self._config['malfunction_rate'],
'min_duration': self._config['malfunction_min_duration'],
'max_duration': self._config['malfunction_max_duration']
}
malfunction_generator = malfunction_from_params(stochastic_data)
speed_ratio_map = None
if 'speed_ratio_map' in self._config:
speed_ratio_map = {
float(k): float(v)
for k, v in self._config['speed_ratio_map'].items()
}
schedule_generator = sparse_schedule_generator(speed_ratio_map)
env = None
try:
env = RailEnv(
width=self._config['width'],
height=self._config['height'],
rail_generator=rail_generator,
schedule_generator=schedule_generator,
number_of_agents=self._config['number_of_agents'],
malfunction_generator_and_process_data=malfunction_generator,
obs_builder_object=self._observation.builder(),
remove_agents_at_target=False,
random_seed=self._config['seed'])
env.reset()
except ValueError as e:
logging.error("=" * 50)
logging.error(f"Error while creating env: {e}")
logging.error("=" * 50)
return env
def __init__(
self,
width,
height,
rail_generator: RailGenerator = random_rail_generator(),
schedule_generator: ScheduleGenerator = random_schedule_generator(
),
number_of_agents=1,
obs_builder_object: ObservationBuilder = GlobalObsForRailEnv(),
malfunction_generator_and_process_data=no_malfunction_generator(),
remove_agents_at_target=True,
random_seed=1,
record_steps=False):
"""
Environment init.
Parameters
----------
rail_generator : function
The rail_generator function is a function that takes the width,
height and agents handles of a rail environment, along with the number of times
the env has been reset, and returns a GridTransitionMap object and a list of
starting positions, targets, and initial orientations for agent handle.
The rail_generator can pass a distance map in the hints or information for specific schedule_generators.
Implementations can be found in flatland/envs/rail_generators.py
schedule_generator : function
The schedule_generator function is a function that takes the grid, the number of agents and optional hints
and returns a list of starting positions, targets, initial orientations and speed for all agent handles.
Implementations can be found in flatland/envs/schedule_generators.py
width : int
The width of the rail map. Potentially in the future,
a range of widths to sample from.
height : int
The height of the rail map. Potentially in the future,
a range of heights to sample from.
number_of_agents : int
Number of agents to spawn on the map. Potentially in the future,
a range of number of agents to sample from.
obs_builder_object: ObservationBuilder object
ObservationBuilder-derived object that takes builds observation
vectors for each agent.
remove_agents_at_target : bool
If remove_agents_at_target is set to true then the agents will be removed by placing to
RailEnv.DEPOT_POSITION when the agent has reach it's target position.
random_seed : int or None
if None, then its ignored, else the random generators are seeded with this number to ensure
that stochastic operations are replicable across multiple operations
"""
super().__init__()
self.malfunction_generator, self.malfunction_process_data = malfunction_generator_and_process_data
self.rail_generator: RailGenerator = rail_generator
self.schedule_generator: ScheduleGenerator = schedule_generator
self.rail: Optional[GridTransitionMap] = None
self.width = width
self.height = height
self.remove_agents_at_target = remove_agents_at_target
self.rewards = [0] * number_of_agents
self.done = False
self.obs_builder = obs_builder_object
self.obs_builder.set_env(self)
self._max_episode_steps: Optional[int] = None
self._elapsed_steps = 0
self.dones = dict.fromkeys(
list(range(number_of_agents)) + ["__all__"], False)
self.obs_dict = {}
self.rewards_dict = {}
self.dev_obs_dict = {}
self.dev_pred_dict = {}
self.agents: List[EnvAgent] = []
self.number_of_agents = number_of_agents
self.num_resets = 0
self.distance_map = DistanceMap(self.agents, self.height, self.width)
self.action_space = [5]
self._seed()
self._seed()
self.random_seed = random_seed
if self.random_seed:
self._seed(seed=random_seed)
self.valid_positions = None
# global numpy array of agents position, True means that there is an agent at that cell
self.agent_positions: np.ndarray = np.full((height, width), False)
# save episode timesteps ie agent positions, orientations. (not yet actions / observations)
self.record_steps = record_steps # whether to save timesteps
self.cur_episode = [] # save timesteps in here