def test_count_neighbors(self):
g = nx.Graph()
g.add_edges_from([(0, 1), (1, 2), (2, 0)])
nx.set_node_attributes(g, {
0: (0, 1),
1: (0, 2),
2: (1, 1)
},
name="coords")
orders = [(0, 1, 0, 1, 1), (1, 2, 0, 2, 2), (2, 0, 0, 3, 3)]
drivers = np.array([4, 0, 1])
env = TaxiEnv(g, orders, 1, drivers, 3, 0.5, count_neighbors=True)
observation1 = env.reset()
env.current_node_id = 0
env.non_empty_nodes = [1, 2]
dispatch_list = env.make_order_dispatch_list_and_remove_orders()
assert len(dispatch_list) == 2
env.reset() # order_dispatch_list can be run only single time
env.current_node_id = 0
env.non_empty_nodes = [1, 2]
observation2, reward, done, info = env.step(np.zeros(3))
assert reward == (1 + 2 - 0.5 - 0.5) / 4
def test_reward_options(self):
'''
Test these:
weight_poorest: bool = False,
normalize_rewards: bool = True,
minimum_reward: bool = False,
reward_bound: float = None,
include_income_to_observation: int = 0
'''
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, 0, 2, 2), (2, 2, 0, 3, 3),
(3, 2, 0, 3, 3)]
drivers = np.array([1, 0, 0, 5])
action = np.array([1, 0, 0], dtype=float)
env = TaxiEnv(g,
orders,
1,
drivers,
3,
0.5,
count_neighbors=True,
normalize_rewards=False)
observation = env.reset()
env.current_node_id = 3
env.non_empty_nodes = [0, 1, 2]
observation, reward, done, info = env.step(action)
assert reward == (3 + 3 - 0.5 - 0.5 - 0.5)
env = TaxiEnv(g,
orders,
1,
drivers,
3,
0.5,
count_neighbors=True,
weight_poorest=True)
observation = env.reset()
env.current_node_id = 3
env.non_empty_nodes = [0, 1, 2]
observation, reward, done, info = env.step(action)
# reward is softmax of the reard multiplied by reward
r = np.array([
0, 3, 3, -0.5, -.5, -.5
]) # 0 is because there is a guy in the node 0 that does not move
mult = 1 - env.softmax(r)
rew = mult * r
rew /= 5
assert reward == pytest.approx(np.sum(rew))
env = TaxiEnv(g,
orders,
1,
drivers,
3,
0.5,
count_neighbors=True,
minimum_reward=True)
observation = env.reset()
env.current_node_id = 3
env.non_empty_nodes = [0, 1, 2]
observation, reward, done, info = env.step(action)
assert reward == -0.5 / 5 # returns a single value of a minimum reward, normalized
env = TaxiEnv(g,
orders,
1,
drivers,
3,
0.5,
count_neighbors=True,
normalize_rewards=False,
minimum_reward=True)
observation = env.reset()
env.current_node_id = 3
env.non_empty_nodes = [0, 1, 2]
observation, reward, done, info = env.step(action)
assert reward == -0.5 # returns a single value of a minimum reward, non-normalized
env = TaxiEnv(g,
orders,
1,
drivers,
3,
0.5,
count_neighbors=True,
reward_bound=1)
observation = env.reset()
env.current_node_id = 3
env.non_empty_nodes = [0, 1, 2]
observation, reward, done, info = env.step(action)
assert reward == (1 + 1 - 0.5 - 0.5 - 0.5) / 5
drivers = np.array([2, 0, 0, 5])
env = TaxiEnv(g,
orders,
1,
drivers,
3,
0.5,
count_neighbors=True,
reward_bound=1,
include_income_to_observation=True)
observation = env.reset()
env.world.nodes[0]['info'].drivers[0].add_income(0.9)
env.current_node_id = 3
env.non_empty_nodes = [0, 1, 2]
# all drivers from 3rd node are moved but haven't arrived, so observation should show only the driver at 0's node
observation, reward, done, info = env.step(action)
assert env.current_node_id == 0
assert observation.shape[0] == 5 * env.world_size + env.n_intervals