def test_single_agent_highway_single(self):
# create the base environment
env = FlowEnv(
flow_params=highway_single(
multiagent=False
),
multiagent=False,
shared=False,
version=1
)
env.reset()
# test observation space
test_space(
env.observation_space,
expected_min=np.array([-float("inf") for _ in range(50)]),
expected_max=np.array([float("inf") for _ in range(50)]),
expected_size=50,
)
# test action space
test_space(
env.action_space,
expected_min=np.array([-1 for _ in range(10)]),
expected_max=np.array([1 for _ in range(10)]),
expected_size=10,
)
# kill the environment
env.wrapped_env.terminate()
def test_single_agent_ring_small(self):
# create the base environment
env = FlowEnv(
flow_params=ring_small(
num_automated=1,
horizon=1500,
simulator="traci",
multiagent=False
),
version=0
)
env.reset()
# test observation space
test_space(
env.observation_space,
expected_min=np.array([-np.inf for _ in range(3)]),
expected_max=np.array([np.inf for _ in range(3)]),
expected_size=3,
)
# test action space
test_space(
env.action_space,
expected_min=np.array([-1]),
expected_max=np.array([1]),
expected_size=1,
)
# kill the environment
env.wrapped_env.terminate()
def test_single_agent_ring(self):
# create the base environment
env = FlowEnv(
flow_params=ring(
num_automated=5,
simulator="traci",
multiagent=False
),
multiagent=False,
shared=False,
version=1
)
env.reset()
# test observation space
test_space(
env.observation_space,
expected_min=np.array([-float("inf") for _ in range(25)]),
expected_max=np.array([float("inf") for _ in range(25)]),
expected_size=25,
)
# test action space
test_space(
env.action_space,
expected_min=np.array([-1 for _ in range(5)]),
expected_max=np.array([1 for _ in range(5)]),
expected_size=5,
)
# kill the environment
env.wrapped_env.terminate()
def test_single_agent_ring(self):
# create the base environment
env = FlowEnv(
env_name="ring",
env_params={
"num_automated": 1,
"horizon": 1500,
"simulator": "traci",
"multiagent": False
},
version=0
)
env.reset()
# test observation space
test_space(
env.observation_space,
expected_min=np.array([-np.inf for _ in range(3)]),
expected_max=np.array([np.inf for _ in range(3)]),
expected_size=3,
)
# test action space
test_space(
env.action_space,
expected_min=np.array([-1]),
expected_max=np.array([1]),
expected_size=1,
)
# kill the environment
env.wrapped_env.terminate()
def test_multi_agent_figure_eight(self):
# create the base environment
env = FlowEnv(
env_name="figure_eight",
env_params={
"num_automated": 1,
"horizon": 1500,
"simulator": "traci",
"multiagent": True
},
version=0
)
env.reset()
# test observation space
pass # TODO
# test action space
pass # TODO
# kill the environment
env.wrapped_env.terminate()
# create the environment with multiple automated vehicles
env = FlowEnv(
env_name="figure_eight",
env_params={
"num_automated": 14,
"horizon": 1500,
"simulator": "traci",
"multiagent": True
},
version=1
)
env.reset()
# test observation space
pass # TODO
# test action space
pass # TODO
# kill the environment
env.wrapped_env.terminate()
def test_multi_agent_figure_eight(self):
# create the base environment
env = FlowEnv(
flow_params=figure_eight(
num_automated=1,
horizon=1500,
simulator="traci",
multiagent=True
),
version=0
)
env.reset()
# test observation space
pass # TODO
# test action space
pass # TODO
# kill the environment
env.wrapped_env.terminate()
# create the environment with multiple automated vehicles
env = FlowEnv(
flow_params=figure_eight(
num_automated=14,
horizon=1500,
simulator="traci",
multiagent=True
),
version=1
)
env.reset()
# test observation space
pass # TODO
# test action space
pass # TODO
# kill the environment
env.wrapped_env.terminate()
def test_multi_agent_ring(self):
# create the base environment
env = FlowEnv(
flow_params=ring(
num_automated=5,
simulator="traci",
multiagent=True
),
multiagent=True,
shared=False,
version=1
)
env.reset()
# test the agent IDs.
self.assertListEqual(
sorted(env.agents), ['rl_0_0', 'rl_0_1', 'rl_0_2', 'rl_0_3',
'rl_0_4'])
# test observation space
test_space(
env.observation_space["rl_0_0"],
expected_min=np.array([-float("inf") for _ in range(5)]),
expected_max=np.array([float("inf") for _ in range(5)]),
expected_size=5,
)
# test action space
test_space(
env.action_space["rl_0_0"],
expected_min=np.array([-1]),
expected_max=np.array([1]),
expected_size=1,
)
# kill the environment
env.wrapped_env.terminate()
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()
def test_single_agent_figure_eight(self):
# create the base environment
env = FlowEnv(
flow_params=figure_eight(
num_automated=1,
horizon=1500,
simulator="traci",
multiagent=False
),
version=0
)
env.reset()
# test observation space
test_space(
env.observation_space,
expected_min=np.array([0 for _ in range(28)]),
expected_max=np.array([1 for _ in range(28)]),
expected_size=28,
)
# test action space
test_space(
env.action_space,
expected_min=np.array([-3]),
expected_max=np.array([3]),
expected_size=1,
)
# kill the environment
env.wrapped_env.terminate()
# create the environment with multiple automated vehicles
env = FlowEnv(
flow_params=figure_eight(
num_automated=14,
horizon=1500,
simulator="traci",
multiagent=False
),
version=1
)
env.reset()
# test observation space
test_space(
env.observation_space,
expected_min=np.array([0 for _ in range(28)]),
expected_max=np.array([1 for _ in range(28)]),
expected_size=28,
)
# test action space
test_space(
env.action_space,
expected_min=np.array([-3 for _ in range(14)]),
expected_max=np.array([3 for _ in range(14)]),
expected_size=14,
)
# kill the environment
env.wrapped_env.terminate()
def test_multi_agent_ring_small(self):
# create the base environment
env = FlowEnv(
flow_params=ring_small(
num_automated=1,
horizon=1500,
simulator="traci",
multiagent=True
),
multiagent=True,
shared=False,
version=1
)
env.reset()
# test the agent IDs.
self.assertListEqual(env.agents, ["rl_0_0"])
# test observation space
test_space(
env.observation_space["rl_0_0"],
expected_min=np.array([-5 for _ in range(3)]),
expected_max=np.array([5 for _ in range(3)]),
expected_size=3,
)
# test action space
test_space(
env.action_space["rl_0_0"],
expected_min=np.array([-1]),
expected_max=np.array([1]),
expected_size=1,
)
# kill the environment
env.wrapped_env.terminate()
# create the environment with multiple automated vehicles
env = FlowEnv(
flow_params=ring_small(
num_automated=4,
horizon=1500,
simulator="traci",
multiagent=True
),
multiagent=True,
shared=True,
)
env.reset()
# test the agent IDs.
self.assertListEqual(
env.agents, ["rl_0_0", "rl_1_0", "rl_2_0", "rl_3_0"])
# test observation space
test_space(
env.observation_space,
expected_min=np.array([-5 for _ in range(3)]),
expected_max=np.array([5 for _ in range(3)]),
expected_size=3,
)
# test action space
test_space(
env.action_space,
expected_min=np.array([-1]),
expected_max=np.array([1]),
expected_size=1,
)
# kill the environment
env.wrapped_env.terminate()
def test_single_agent_merge(self):
# create version 0 of the environment
env = FlowEnv(
env_name="merge",
env_params={
"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(
env_name="merge",
env_params={
"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(
env_name="merge",
env_params={
"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()
def test_single_agent_figure_eight(self):
# create the base environment
env = FlowEnv(
env_name="figure_eight",
env_params={
"num_automated": 1,
"horizon": 1500,
"simulator": "traci",
"multiagent": False
},
version=0
)
env.reset()
# test observation space
test_space(
env.observation_space,
expected_min=np.array([0 for _ in range(28)]),
expected_max=np.array([1 for _ in range(28)]),
expected_size=28,
)
# test action space
test_space(
env.action_space,
expected_min=np.array([-3]),
expected_max=np.array([3]),
expected_size=1,
)
# kill the environment
env.wrapped_env.terminate()
# create the environment with multiple automated vehicles
env = FlowEnv(
env_name="figure_eight",
env_params={
"num_automated": 14,
"horizon": 1500,
"simulator": "traci",
"multiagent": False
},
version=1
)
env.reset()
# test observation space
test_space(
env.observation_space,
expected_min=np.array([0 for _ in range(28)]),
expected_max=np.array([1 for _ in range(28)]),
expected_size=28,
)
# test action space
test_space(
env.action_space,
expected_min=np.array([-3 for _ in range(14)]),
expected_max=np.array([3 for _ in range(14)]),
expected_size=14,
)
# kill the environment
env.wrapped_env.terminate()
