def test_view(self):
g = nx.Graph()
g.add_edges_from([(0, 1), (1, 2), (2, 3), (3, 4), (1, 5)])
nx.set_node_attributes(g, {
0: (0, 1),
1: (0, 2),
2: (0, 3),
3: (0, 4),
4: (0, 5),
5: (1, 1)
},
name="coords")
orders = [(3, 2, 2, 3, 3)
] # <source, destination, time, length, price>
drivers = np.array([1, 1, 1, 1, 1, 1])
action = np.array([1, 0, 0], dtype=float)
env = TaxiEnv(g, orders, 1, drivers, 10)
env.seed(123)
env.set_view([2, 3, 4])
obs, _, _ = env.get_observation()
# check observation space and content
assert env.observation_space_shape == obs.shape
view_size = 3
assert env.observation_space_shape == (
view_size * 4 + 10,
) # default income is not included, so its <driver, order, idle, time_id, node_id>
assert env.action_space_shape == (
3,
) # degree of 1 is 3, but of the rest is 2. So it should be 2 + 1 (staying action)
assert env.current_node_id in [2, 3, 4]
# an action [1, 0, 0] for the node 2 means to go to node 3, because its the only neighbor in the view
env.step(action)
assert env.current_node_id in [2, 3, 4]
env.step(action)
assert env.current_node_id in [2, 3, 4]
obs, rew, done, info = env.step(action)
assert (obs[:view_size] == np.array([0.5, 1, 0])).all(
) # there are 2 drivers in the node 3 at the end, one from node 2, one from node 4.
assert (obs[view_size:2 * view_size] == np.array([0, 0, 0])).all()
assert (obs[2 * view_size:3 * view_size] == np.array([0.5, 1,
0])).all()
# next time iteration should happen
assert env.time == 1
assert env.current_node_id in [2, 3]
assert (obs[2 * view_size:3 * view_size] == np.array([0.5, 1,
0])).all()
assert (obs[3 * view_size:3 * view_size + 10] == np.array(
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0])).all()
assert obs[3 * view_size + 10:].shape == (3, )
assert (obs[3 * view_size + 10:] == np.array([
1, 0, 0
])).all() or (obs[3 * view_size + 10:] == np.array([0, 1, 0])).all()
assert [d.position for d in env.all_driver_list] == [0, 1, 3, 2, 3, 5]
def test_sync(self):
g = nx.Graph()
g.add_edges_from([(0, 1), (1, 2), (2, 3)])
nx.set_node_attributes(g, {
0: (0, 1),
1: (0, 2),
2: (1, 1),
3: (1, 2)
},
name="coords")
orders = [(0, 1, 0, 1, 1), (1, 1, 1, 2, 2), (2, 2, 1, 3, 3),
(3, 2, 2, 3, 3)]
drivers = np.array([1, 0, 0, 5])
action = np.array([0.3, 0.4, 0.3], dtype=float)
env = TaxiEnv(g, orders, 1, drivers, 10)
env.step(action)
env2 = TaxiEnv(g, orders, 1, drivers, 10)
env2.sync(env)
o1, _, _ = env.get_observation()
o2, _, _ = env2.get_observation()
assert (o1 == o2).all()
env.seed(1)
env2.seed(1)
while not env.done:
obs, rew, done, info = env.step(action)
obs2, rew2, done2, info2 = env2.step(action)
assert (obs == obs2).all()
assert rew == rew2
assert done == done2
assert info == info2
class TaxiEnvBatch(gym.Env):
'''
This class is a wrapper over taxi_env, providing an interface for cA2C,
that requires processing drivers in batches + some additional context information
'''
metadata = {'render.modes': ['rgb_array']}
def __init__(self,
world: nx.Graph,
orders: Tuple[int, int, int, int, float],
order_sampling_rate: float,
drivers_per_node: Array[int],
n_intervals: List,
wc: float,
count_neighbors: bool = False,
weight_poorest: bool = False,
normalize_rewards: bool = True,
minimum_reward: bool = False,
reward_bound: float = None,
include_income_to_observation: bool = False,
poorest_first: bool = False,
idle_reward: bool = False) -> None:
self.itEnv = TaxiEnv(world, orders, order_sampling_rate,
drivers_per_node, n_intervals, wc,
count_neighbors, weight_poorest,
normalize_rewards, minimum_reward, reward_bound,
include_income_to_observation, poorest_first,
idle_reward)
self.world = self.itEnv.world
self.n_intervals = n_intervals
self.n_drivers = self.itEnv.n_drivers
self.time = 0
self.include_income_to_observation = include_income_to_observation
self.one_cell_action_space = self.itEnv.max_degree + 1
self.action_space = spaces.Box(low=0,
high=1,
shape=(self.one_cell_action_space *
self.itEnv.world_size, ))
if include_income_to_observation:
assert self.itEnv.observation_space_shape[0] == 3 * len(
self.world) + self.itEnv.n_intervals + 3
self.observation_space_shape = (
self.itEnv.observation_space_shape[0] + 2 * len(self.world) -
3, )
else:
assert self.itEnv.observation_space_shape[0] == 3 * len(
self.world) + self.itEnv.n_intervals
self.observation_space_shape = (
self.itEnv.observation_space_shape[0] - len(self.world), )
self.observation_space = spaces.Box(low=0,
high=1,
shape=self.observation_space_shape)
def reset(self) -> Array[int]:
self.time = 0
if self.itEnv.include_income_to_observation:
t = self.itEnv.world_size + 3
observation = self.itEnv.reset()[:-t]
# assuming all incomes are zero
return np.concatenate(
(observation, np.zeros(3 * self.itEnv.world_size)))
else:
t = self.itEnv.world_size
return self.itEnv.reset()[:-t]
def get_reset_info(self):
'''
Currently used only to get max_orders and max_drivers, that should current_cell independent
'''
return self.itEnv.get_reset_info()
def step(self,
action: Array[float]) -> Tuple[Array[int], float, bool, Dict]:
cells_with_nonzero_drivers = np.sum([
1 for n in self.itEnv.world.nodes(data=True)
if n[1]['info'].get_driver_num() > 0
])
nodes_with_orders = np.sum([
1 for n in self.itEnv.world.nodes(data=True)
if n[1]['info'].get_order_num() > 0
])
total_orders = np.sum([
n[1]['info'].get_order_num()
for n in self.itEnv.world.nodes(data=True)
])
global_observation = np.zeros(5 * self.itEnv.world_size +
self.itEnv.n_intervals)
global_done = False
global_reward = 0
reward_per_node = np.zeros(self.itEnv.world_size)
init_t = self.itEnv.time
self.last_action_for_drawing = action
total_served_orders = 0
max_driver = None
max_order = None
for i in range(cells_with_nonzero_drivers):
current_cell = self.itEnv.current_node_id
a = current_cell * self.one_cell_action_space
action_per_cell = action[a:a + self.one_cell_action_space]
observation, reward, done, info = self.itEnv.step(action_per_cell)
reward_per_node[current_cell] = reward
global_done = done
global_reward += reward
total_served_orders += info['served_orders']
# updated at each step, but the final should be corrent
max_driver = info["driver normalization constant"]
max_order = info["order normalization constant"]
if self.itEnv.include_income_to_observation:
assert observation.shape[
0] == 3 * self.itEnv.world_size + self.itEnv.n_intervals + 3
size_without_income = 2 * self.itEnv.world_size + self.itEnv.n_intervals
ws = self.itEnv.world_size
offset = current_cell
global_observation[:
size_without_income] = observation[:
size_without_income]
global_observation[size_without_income +
3 * offset:size_without_income +
3 * offset + 3] = observation[-3:]
else:
global_observation = observation[:-self.itEnv.world_size]
# if cells_with_nonzero_drivers == 0:
# observation, reward, done, info = self.itEnv.step(action_per_cell)
#
# reward_per_node[current_cell] = reward
# global_done = done
# global_reward += reward
# total_served_orders += info['served_orders']
#
# # updated at each step, but the final should be corrent
# max_driver = info["driver normalization constant"]
# max_order = info["order normalization constant"]
#
# if self.itEnv.include_income_to_observation:
# assert observation.shape[0] == 3*self.itEnv.world_size+self.itEnv.n_intervals+3
# size_without_income = 2*self.itEnv.world_size+self.itEnv.n_intervals
# ws = self.itEnv.world_size
# offset = current_cell
# global_observation[:size_without_income] = observation[:size_without_income]
# global_observation[size_without_income+3*offset:size_without_income+3*offset+3] = observation[-3:]
# else:
# global_observation = observation[:-self.itEnv.world_size]
assert not global_done or init_t + 1 == self.itEnv.n_intervals
assert self.itEnv.time == init_t + 1
self.time += 1
global_info = {
"reward_per_node":
reward_per_node,
"served_orders":
total_served_orders,
"nodes_with_drivers":
cells_with_nonzero_drivers,
"nodes_with_orders":
nodes_with_orders,
"driver normalization constant":
max_driver,
"order normalization constant":
max_order,
"total_orders":
total_orders,
"idle_reward":
float(
np.mean([
d.get_idle_period() for d in self.itEnv.all_driver_list
])),
"min_idle":
float(
np.min(
[d.get_idle_period() for d in self.itEnv.all_driver_list]))
}
return global_observation, global_reward, global_done, global_info
def seed(self, seed):
self.itEnv.seed(seed)
def get_min_revenue(self):
return self.itEnv.get_min_revenue()
def get_total_revenue(self):
return self.itEnv.get_total_revenue()
def compute_remaining_drivers_and_orders(self, state):
return self.itEnv.compute_remaining_drivers_and_orders(state)
def set_income_bound(self, bound):
self.itEnv.set_income_bound(bound)
def render(self, mode='rgb_array'):
fig = plt.figure(figsize=(20, 20))
ax = fig.gca()
ax.axis('off')
pos = nx.get_node_attributes(self.world, 'coords')
G = nx.DiGraph(self.world)
nodelist = []
edgelist = []
action = self.last_action_for_drawing
act = self.itEnv.action_space_shape[0]
node_colors = []
edge_colors = []
for n in self.world.nodes():
node_action = action[act * n:act * (n + 1)]
nodelist.append(n)
node_colors.append(node_action[-1])
j = 0
added = 0
for nn in self.world.neighbors(n):
if node_action[j] > 0:
edgelist.append((n, nn))
edge_colors.append(node_action[j])
added += 1
j += 1
assert abs(np.sum(node_action) - 1) < 0.00001, node_action
assert node_action[-1] != 0 or added > 0, (node_action, n)
nx.draw_networkx(G,
edgelist=edgelist,
edge_color=edge_colors,
vmin=-1,
vmax=1,
node_shape='.',
edge_vmax=1.1,
cmap=matplotlib.cm.get_cmap("Blues"),
edge_cmap=matplotlib.cm.get_cmap("Blues"),
node_color=node_colors,
nodelist=nodelist,
pos=pos,
arrows=True,
with_labels=False,
ax=ax)
canvas = FigureCanvasAgg(fig)
canvas.draw()
s, (width, height) = canvas.print_to_buffer()
# Option 2a: Convert to a NumPy array
X = np.frombuffer(s, np.uint8).reshape((height, width, 4))
plt.close(fig)
return X