import random
import numpy as np
from flatland.core.grid.grid4 import Grid4TransitionsEnum
from flatland.envs.agent_utils import EnvAgent
from flatland.envs.observations import TreeObsForRailEnv
from flatland.envs.rail_env import RailEnv, RailEnvActions
from flatland.envs.rail_generators import complex_rail_generator, sparse_rail_generator
from flatland.envs.schedule_generators import complex_schedule_generator, sparse_schedule_generator
from flatland.utils.rendertools import RenderTool
from test_utils import Replay
class SingleAgentNavigationObs(TreeObsForRailEnv):
"""
We derive our bbservation builder from TreeObsForRailEnv, to exploit the existing implementation to compute
the minimum distances from each grid node to each agent's target.
We then build a representation vector with 3 binary components, indicating which of the 3 available directions
for each agent (Left, Forward, Right) lead to the shortest path to its target.
E.g., if taking the Left branch (if available) is the shortest route to the agent's target, the observation vector
will be [1, 0, 0].
"""
def __init__(self):
super().__init__(max_depth=0)
self.observation_space = [3]
def reset(self):
# Recompute the distance map, if the environment has changed.
super().reset()
def get(self, handle):
agent = self.env.agents[handle]
possible_transitions = self.env.rail.get_transitions(*agent.position, agent.direction)
num_transitions = np.count_nonzero(possible_transitions)
# Start from the current orientation, and see which transitions are available;
# organize them as [left, forward, right], relative to the current orientation
# If only one transition is possible, the forward branch is aligned with it.
if num_transitions == 1:
observation = [0, 1, 0]
else:
min_distances = []
for direction in [(agent.direction + i) % 4 for i in range(-1, 2)]:
if possible_transitions[direction]:
new_position = self._new_position(agent.position, direction)
min_distances.append(self.distance_map[handle, new_position[0], new_position[1], direction])
else:
min_distances.append(np.inf)
observation = [0, 0, 0]
observation[np.argmin(min_distances)] = 1
return observation
def test_malfunction_process():
# Set fixed malfunction duration for this test
stochastic_data = {'prop_malfunction': 1.,
'malfunction_rate': 1000,
'min_duration': 3,
'max_duration': 3}
np.random.seed(5)
env = RailEnv(width=20,
height=20,
rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=1, min_dist=5, max_dist=99999,
seed=0),
schedule_generator=complex_schedule_generator(),
number_of_agents=2,
obs_builder_object=SingleAgentNavigationObs(),
stochastic_data=stochastic_data)
obs = env.reset()
# Check that a initial duration for malfunction was assigned
assert env.agents[0].malfunction_data['next_malfunction'] > 0
agent_halts = 0
total_down_time = 0
agent_malfunctioning = False
agent_old_position = env.agents[0].position
for step in range(100):
actions = {}
for i in range(len(obs)):
actions[i] = np.argmax(obs[i]) + 1
if step % 5 == 0:
# Stop the agent and set it to be malfunctioning
env.agents[0].malfunction_data['malfunction'] = -1
env.agents[0].malfunction_data['next_malfunction'] = 0
agent_halts += 1
obs, all_rewards, done, _ = env.step(actions)
if env.agents[0].malfunction_data['malfunction'] > 0:
agent_malfunctioning = True
else:
agent_malfunctioning = False
if agent_malfunctioning:
# Check that agent is not moving while malfunctioning
assert agent_old_position == env.agents[0].position
agent_old_position = env.agents[0].position
total_down_time += env.agents[0].malfunction_data['malfunction']
# Check that the appropriate number of malfunctions is achieved
assert env.agents[0].malfunction_data['nr_malfunctions'] == 21
# Check that 20 stops where performed
assert agent_halts == 20
# Check that malfunctioning data was standing around
assert total_down_time > 0
def test_malfunction_process_statistically():
"""Tests hat malfunctions are produced by stochastic_data!"""
# Set fixed malfunction duration for this test
stochastic_data = {'prop_malfunction': 1.,
'malfunction_rate': 2,
'min_duration': 3,
'max_duration': 3}
np.random.seed(5)
random.seed(0)
env = RailEnv(width=20,
height=20,
rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=1, min_dist=5, max_dist=99999,
seed=0),
schedule_generator=complex_schedule_generator(),
number_of_agents=2,
obs_builder_object=SingleAgentNavigationObs(),
stochastic_data=stochastic_data)
env.reset()
nb_malfunction = 0
for step in range(100):
action_dict = {}
for agent in env.agents:
if agent.malfunction_data['malfunction'] > 0:
nb_malfunction += 1
# We randomly select an action
action_dict[agent.handle] = np.random.randint(4)
env.step(action_dict)
# check that generation of malfunctions works as expected
# results are different in py36 and py37, therefore no exact test on nb_malfunction
assert nb_malfunction == 149, "nb_malfunction={}".format(nb_malfunction)
def test_initial_malfunction(rendering=True):
random.seed(0)
np.random.seed(0)
stochastic_data = {'prop_malfunction': 1., # Percentage of defective agents
'malfunction_rate': 70, # Rate of malfunction occurence
'min_duration': 2, # Minimal duration of malfunction
'max_duration': 5 # Max duration of malfunction
}
speed_ration_map = {1.: 1., # Fast passenger train
1. / 2.: 0., # Fast freight train
1. / 3.: 0., # Slow commuter train
1. / 4.: 0.} # Slow freight train
env = RailEnv(width=25,
height=30,
rail_generator=sparse_rail_generator(num_cities=5,
# Number of cities in map (where train stations are)
num_intersections=4,
# Number of intersections (no start / target)
num_trainstations=25, # Number of possible start/targets on map
min_node_dist=6, # Minimal distance of nodes
node_radius=3, # Proximity of stations to city center
num_neighb=3,
# Number of connections to other cities/intersections
seed=215545, # Random seed
grid_mode=True,
enhance_intersection=False
),
schedule_generator=sparse_schedule_generator(speed_ration_map),
number_of_agents=1,
stochastic_data=stochastic_data, # Malfunction data generator
)
if rendering:
renderer = RenderTool(env)
renderer.render_env(show=True, frames=False, show_observations=False)
_action = dict()
replay_steps = [
Replay(
position=(28, 5),
direction=Grid4TransitionsEnum.EAST,
action=RailEnvActions.MOVE_FORWARD,
malfunction=3
),
Replay(
position=(28, 5),
direction=Grid4TransitionsEnum.EAST,
action=RailEnvActions.MOVE_FORWARD,
malfunction=2
),
Replay(
position=(28, 5),
direction=Grid4TransitionsEnum.EAST,
action=RailEnvActions.MOVE_FORWARD,
malfunction=1
),
Replay(
position=(28, 4),
direction=Grid4TransitionsEnum.WEST,
action=RailEnvActions.MOVE_FORWARD,
malfunction=0
),
Replay(
position=(27, 4),
direction=Grid4TransitionsEnum.NORTH,
action=RailEnvActions.MOVE_FORWARD,
malfunction=0
)
]
info_dict = {
'action_required': [True]
}
for i, replay in enumerate(replay_steps):
def _assert(actual, expected, msg):
assert actual == expected, "[{}] {}: actual={}, expected={}".format(i, msg, actual, expected)
agent: EnvAgent = env.agents[0]
_assert(agent.position, replay.position, 'position')
_assert(agent.direction, replay.direction, 'direction')
_assert(agent.malfunction_data['malfunction'], replay.malfunction, 'malfunction')
if replay.action is not None:
assert info_dict['action_required'][0] == True, "[{}] expecting action_required={}".format(i, True)
_, _, _, info_dict = env.step({0: replay.action})
else:
assert info_dict['action_required'][0] == False, "[{}] expecting action_required={}".format(i, False)
_, _, _, info_dict = env.step({})
if rendering:
renderer.render_env(show=True, show_observations=True)