"merge-v0": {
"meta_ac_space":
lambda relative_goals, multiagent: Box(low=-.5
if relative_goals else 0,
high=.5
if relative_goals else 1,
shape=(1
if multiagent else 5, ),
dtype=np.float32),
"state_indices":
lambda multiagent: [5 * i for i in range(1 if multiagent else 5)],
"env":
lambda evaluate, render, n_levels, multiagent, shared, maddpg: FlowEnv(
flow_params=merge(
exp_num=0,
horizon=6000,
simulator="traci",
multiagent=multiagent,
),
render=render,
multiagent=multiagent,
shared=shared,
maddpg=maddpg,
),
},
"merge-v1": {
"meta_ac_space":
lambda relative_goals, multiagent: Box(low=-.5
if relative_goals else 0,
high=.5
if relative_goals else 1,
shape=(1
def test_single_agent_merge(self):
# create version 0 of the environment
env = FlowEnv(
flow_params=merge(
exp_num=0,
horizon=6000,
simulator="traci",
multiagent=False
),
version=0
)
env.reset()
# test observation space
test_space(
env.observation_space,
expected_min=np.array([0 for _ in range(25)]),
expected_max=np.array([1 for _ in range(25)]),
expected_size=25,
)
# test action space
test_space(
env.action_space,
expected_min=np.array([-1.5 for _ in range(5)]),
expected_max=np.array([1.5 for _ in range(5)]),
expected_size=5,
)
# kill the environment
env.wrapped_env.terminate()
# create version 1 of the environment
env = FlowEnv(
flow_params=merge(
exp_num=1,
horizon=6000,
simulator="traci",
multiagent=False
),
version=1
)
env.reset()
# test observation space
test_space(
env.observation_space,
expected_min=np.array([0 for _ in range(65)]),
expected_max=np.array([1 for _ in range(65)]),
expected_size=65,
)
# test action space
test_space(
env.action_space,
expected_min=np.array([-1.5 for _ in range(13)]),
expected_max=np.array([1.5 for _ in range(13)]),
expected_size=13,
)
# kill the environment
env.wrapped_env.terminate()
# create version 2 of the environment
env = FlowEnv(
flow_params=merge(
exp_num=2,
horizon=6000,
simulator="traci",
multiagent=False
),
version=2
)
env.reset()
# test observation space
test_space(
env.observation_space,
expected_min=np.array([0 for _ in range(85)]),
expected_max=np.array([1 for _ in range(85)]),
expected_size=85,
)
# test action space
test_space(
env.action_space,
expected_min=np.array([-1.5 for _ in range(17)]),
expected_max=np.array([1.5 for _ in range(17)]),
expected_size=17,
)
# kill the environment
env.wrapped_env.terminate()