# Stochasticity¶

Warning

Another area where we improved Flatland 2.0 are stochastic events added during the episodes. This is very common for railway networks where the initial plan usually needs to be rescheduled during operations as minor events such as delayed departure from trainstations, malfunctions on trains or infrastructure or just the weather lead to delayed trains.

We implemted a poisson process to simulate delays by stopping agents at random times for random durations. The parameters necessary for the stochastic events can be provided when creating the environment.

# Use a the malfunction generator to break agents from time to time

stochastic_data = {
'prop_malfunction': 0.5,  # Percentage of defective agents
'malfunction_rate': 30,  # Rate of malfunction occurence
'min_duration': 3,  # Minimal duration of malfunction
'max_duration': 10  # Max duration of malfunction
}


The parameters are as follows:

• prop_malfunction is the proportion of agents that can malfunction. 1.0 means that each agent can break.

• malfunction_rate is the mean rate of the poisson process in number of environment steps.

• min_duration and max_duration set the range of malfunction durations. They are sampled uniformly

You can introduce stochasticity by simply creating the env as follows:

env = RailEnv(
...
stochastic_data=stochastic_data,  # Malfunction data generator
...
)


In your controller, you can check whether an agent is malfunctioning:

obs, rew, done, info = env.step(actions)
...
action_dict = dict()
for a in range(env.get_num_agents()):
if info['malfunction'][a] == 0:
action_dict.update({a: ...})

# Custom observation builder
tree_observation = TreeObsForRailEnv(max_depth=2, predictor=ShortestPathPredictorForRailEnv())

# 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

env = RailEnv(width=50,
height=50,
rail_generator=sparse_rail_generator(num_cities=20,  # Number of cities in map (where train stations are)
num_intersections=5,  # Number of intersections (no start / target)
num_trainstations=15,  # Number of possible start/targets on map
min_node_dist=3,  # Minimal distance of nodes
node_radius=2,  # Proximity of stations to city center
num_neighb=4,  # Number of connections to other cities/intersections
seed=15,  # Random seed
grid_mode=True,
enhance_intersection=True
),
schedule_generator=sparse_schedule_generator(speed_ration_map),
number_of_agents=10,
stochastic_data=stochastic_data,  # Malfunction data generator
obs_builder_object=tree_observation)
env.reset()


You will quickly realize that this will lead to unforeseen difficulties which means that your controller needs to observe the environment at all times to be able to react to the stochastic events.

To see all the changes in action you can just run the flatland_example_2_0.py file in the examples folder. The file can be found here.