def test_ma_intersection_close_spawn():
def _no_close_spawn(vehicles):
vehicles = list(vehicles.values())
for c1, v1 in enumerate(vehicles):
for c2 in range(c1 + 1, len(vehicles)):
v2 = vehicles[c2]
dis = norm(v1.position[0] - v2.position[0],
v1.position[1] - v2.position[1])
assert distance_greater(v1.position, v2.position, length=2.2)
MultiAgentIntersectionEnv._DEBUG_RANDOM_SEED = 1
env = MultiAgentIntersectionEnv({
# "use_render": True, "fast": True,
"horizon": 50,
"num_agents": 16,
"map_config": {
"exit_length": 30
}
})
env.seed(100)
try:
_check_spaces_before_reset(env)
for num_r in range(10):
obs = env.reset()
_check_spaces_after_reset(env)
for _ in range(10):
o, r, d, i = env.step({k: [0, 0] for k in env.vehicles.keys()})
assert not any(d.values())
_no_close_spawn(env.vehicles)
print('Finish {} resets.'.format(num_r))
finally:
env.close()
MultiAgentIntersectionEnv._DEBUG_RANDOM_SEED = None
def test_ma_intersection_init_space():
try:
for start_seed in [5000, 6000, 7000]:
for num_agents in [16, 32]:
for num_others in [0, 2, 4, 8]:
for crash_vehicle_penalty in [0, 5]:
env_config = dict(
start_seed=start_seed,
num_agents=num_agents,
vehicle_config=dict(lidar=dict(
num_others=num_others)),
crash_vehicle_penalty=crash_vehicle_penalty)
env = MultiAgentIntersectionEnv(env_config)
single_space = env.observation_space["agent0"]
assert single_space.shape is not None, single_space
assert np.prod(
single_space.shape) is not None, single_space
single_space = env.action_space["agent0"]
assert single_space.shape is not None, single_space
assert np.prod(
single_space.shape) is not None, single_space
_check_spaces_before_reset(env)
env.reset()
_check_spaces_after_reset(env)
env.close()
print('Finish: ', env_config)
finally:
if "env" in locals():
env.close()
def test_ma_no_reset_error():
# It is possible that many agents are populated in the same spawn place!
def check_pos(vehicles):
while vehicles:
v_1 = vehicles[0]
for v_2 in vehicles[1:]:
v_1_pos = v_1.position
v_2_pos = v_2.position
assert norm(
v_1_pos[0] - v_2_pos[0], v_1_pos[1] - v_2_pos[1]
) > v_1.WIDTH / 2 + v_2.WIDTH / 2, "Vehicles overlap after reset()"
if v_1.crash_vehicle:
x = 1
raise ValueError("Vehicles overlap after reset()")
vehicles.remove(v_1)
env = MultiAgentIntersectionEnv({"horizon": 300, "num_agents": 40, "delay_done": 0, "use_render": False})
try:
_check_spaces_before_reset(env)
obs = env.reset()
_check_spaces_after_reset(env, obs)
assert env.observation_space.contains(obs)
for step in range(50):
check_pos(list(env.vehicles.values()))
o, r, d, i = env.step({k: [0, 1] for k in env.vehicles.keys()})
env.reset()
if d["__all__"]:
break
finally:
env.close()
def test_ma_intersection_env():
for env in [MultiAgentIntersectionEnv({"delay_done": 0, "num_agents": 1,
"vehicle_config": {"lidar": {"num_others": 8}}}),
MultiAgentIntersectionEnv({"num_agents": 1, "delay_done": 0,
"vehicle_config": {"lidar": {"num_others": 0}}}),
MultiAgentIntersectionEnv({"num_agents": 4, "delay_done": 0,
"vehicle_config": {"lidar": {"num_others": 8}}}),
MultiAgentIntersectionEnv({"num_agents": 4, "delay_done": 0,
"vehicle_config": {"lidar": {"num_others": 0}}}),
MultiAgentIntersectionEnv({"num_agents": 8, "delay_done": 0,
"vehicle_config": {"lidar": {"num_others": 0}}})]:
try:
_check_spaces_before_reset(env)
obs = env.reset()
_check_spaces_after_reset(env, obs)
assert env.observation_space.contains(obs)
for step in range(100):
act = {k: [1, 1] for k in env.vehicles.keys()}
o, r, d, i = _act(env, act)
if step == 0:
assert not any(d.values())
finally:
env.close()
def test_ma_intersection_horizon_termination():
# test horizon
env = MultiAgentIntersectionEnv({
"horizon": 100,
"num_agents": 8,
"crash_done": False
})
try:
for _ in range(
3
): # This function is really easy to break, repeat multiple times!
_check_spaces_before_reset(env)
obs = env.reset()
_check_spaces_after_reset(env, obs)
assert env.observation_space.contains(obs)
should_respawn = set()
special_agents = set(["agent0", "agent7"])
for step in range(1, 10000):
act = {k: [0, 0] for k in env.vehicles.keys()}
for v_id in act.keys():
if v_id in special_agents:
act[v_id] = [1, 1] # Add some randomness
else:
if v_id in env.vehicles:
env.vehicles[v_id].set_static(True)
obs, r, d, i = _act(env, act)
if step == 0 or step == 1:
assert not any(d.values())
if should_respawn:
for kkk in should_respawn:
assert kkk not in obs, "It seems the max_step agents is not respawn!"
assert kkk not in r
assert kkk not in d
assert kkk not in i
should_respawn.clear()
for kkk, ddd in d.items():
if ddd and kkk == "__all__":
print("Current: ", step)
continue
if ddd and kkk not in special_agents:
assert i[kkk]["max_step"]
assert not i[kkk]["out_of_road"]
assert not i[kkk]["crash"]
assert not i[kkk]["crash_vehicle"]
should_respawn.add(kkk)
if d["__all__"]:
obs = env.reset()
should_respawn.clear()
break
finally:
env.close()
def test_randomize_spawn_place():
last_pos = {}
env = MultiAgentIntersectionEnv({"num_agents": 4, "use_render": False, "fast": True})
try:
obs = env.reset()
for step in range(1000):
act = {k: [1, 1] for k in env.vehicles.keys()}
last_pos = {kkk: v.position for kkk, v in env.vehicles.items()}
o, r, d, i = env.step(act)
obs = env.reset()
new_pos = {kkk: v.position for kkk, v in env.vehicles.items()}
for kkk, new_p in new_pos.items():
assert not np.all(new_p == last_pos[kkk]), (new_p, last_pos[kkk], kkk)
finally:
env.close()
def test_ma_intersection_no_short_episode():
env = MultiAgentIntersectionEnv({
"horizon": 300,
"num_agents": 40,
})
try:
_check_spaces_before_reset(env)
o = env.reset()
_check_spaces_after_reset(env, o)
actions = [[0, 1], [1, 1], [-1, 1]]
start = time.time()
d_count = 0
d = {"__all__": False}
for step in range(2000):
# act = {k: actions[np.random.choice(len(actions))] for k in o.keys()}
act = {
k: actions[np.random.choice(len(actions))]
for k in env.vehicles.keys()
}
o_keys = set(o.keys()).union({"__all__"})
a_keys = set(env.action_space.spaces.keys()).union(set(d.keys()))
assert o_keys == a_keys
o, r, d, i = _act(env, act)
for kkk, iii in i.items():
if d[kkk]:
assert iii["episode_length"] >= 1
d_count += 1
if d["__all__"]:
o = env.reset()
d = {"__all__": False}
if (step + 1) % 100 == 0:
print(
"Finish {}/2000 simulation steps. Time elapse: {:.4f}. Average FPS: {:.4f}"
.format(step + 1,
time.time() - start,
(step + 1) / (time.time() - start)))
if d_count > 200:
break
finally:
env.close()
def test_ma_intersection_40_agent_reset_after_respawn():
def check_pos(vehicles):
while vehicles:
v_1 = vehicles[0]
for v_2 in vehicles[1:]:
v_1_pos = v_1.position
v_2_pos = v_2.position
assert norm(
v_1_pos[0] - v_2_pos[0], v_1_pos[1] - v_2_pos[1]
) > v_1.WIDTH / 2 + v_2.WIDTH / 2, "Vehicles overlap after reset()"
assert not v_1.crash_vehicle, "Vehicles overlap after reset()"
vehicles.remove(v_1)
env = MultiAgentIntersectionEnv({
"horizon": 50,
"num_agents": 40,
"use_render": False
})
try:
_check_spaces_before_reset(env)
obs = env.reset()
_check_spaces_after_reset(env, obs)
assert env.observation_space.contains(obs)
for step in range(50):
env.reset()
check_pos(list(env.vehicles.values()))
for v_id in list(env.vehicles.keys())[:20]:
env.agent_manager.finish(v_id)
env.step({k: [1, 1] for k in env.vehicles.keys()})
env.step({k: [1, 1] for k in env.vehicles.keys()})
env.step({k: [1, 1] for k in env.vehicles.keys()})
finally:
env.close()
def test_ma_intersection_reward_done_alignment():
# out of road
env = MultiAgentIntersectionEnv({
"horizon": 200,
"num_agents": 4,
"out_of_road_penalty": 777,
"crash_done": False
})
try:
_check_spaces_before_reset(env)
obs = env.reset()
_check_spaces_after_reset(env, obs)
assert env.observation_space.contains(obs)
for action in [-1, 1]:
for step in range(5000):
act = {k: [action, 1] for k in env.vehicles.keys()}
o, r, d, i = _act(env, act)
for kkk, ddd in d.items():
if ddd and kkk != "__all__":
# #assert r[kkk] == -777
assert i[kkk]["out_of_road"]
# print('{} done passed!'.format(kkk))
for kkk, rrr in r.items():
if rrr == -777:
assert d[kkk]
assert i[kkk]["out_of_road"]
# print('{} reward passed!'.format(kkk))
if d["__all__"]:
env.reset()
break
finally:
env.close()
# crash
env = MultiAgentIntersectionEnv({
"horizon": 100,
"num_agents": 2,
"crash_vehicle_penalty": 1.7777,
"crash_done": True,
"delay_done": 0,
# "use_render": True,
# "fast": True,
"top_down_camera_initial_z": 160
})
# Force the seed here so that the agent1 and agent2 are in same heading! Otherwise they might be in vertical
# heading and cause one of the vehicle raise "out of road" error!
env._DEBUG_RANDOM_SEED = 1
try:
_check_spaces_before_reset(env)
obs = env.reset()
_check_spaces_after_reset(env, obs)
for step in range(5):
act = {k: [0, 0] for k in env.vehicles.keys()}
o, r, d, i = _act(env, act)
env.vehicles["agent0"].set_position(env.vehicles["agent1"].position,
height=1.2)
for step in range(5000):
act = {k: [0, 0] for k in env.vehicles.keys()}
o, r, d, i = _act(env, act)
if not any(d.values()):
continue
assert sum(d.values()) == 2
for kkk in ['agent0', 'agent1']:
iii = i[kkk]
assert iii["crash_vehicle"]
assert iii["crash"]
# #assert r[kkk] == -1.7777
# for kkk, ddd in d.items():
ddd = d[kkk]
if ddd and kkk != "__all__":
#assert r[kkk] == -1.7777
assert i[kkk]["crash_vehicle"]
assert i[kkk]["crash"]
# print('{} done passed!'.format(kkk))
# for kkk, rrr in r.items():
rrr = r[kkk]
if rrr == -1.7777:
assert d[kkk]
assert i[kkk]["crash_vehicle"]
assert i[kkk]["crash"]
# print('{} reward passed!'.format(kkk))
# assert d["__all__"]
# if d["__all__"]:
break
finally:
env._DEBUG_RANDOM_SEED = None
env.close()
# crash with real fixed vehicle
# crash 2
env = MultiAgentIntersectionEnv({
"map_config": {
"exit_length": 110,
"lane_num": 1
},
# "use_render": True,
# "fast": True,
"horizon": 200,
"num_agents": 40,
"crash_vehicle_penalty": 1.7777,
"crash_done": False
})
try:
_check_spaces_before_reset(env)
obs = env.reset()
_check_spaces_after_reset(env, obs)
for step in range(1):
act = {k: [0, 0] for k in env.vehicles.keys()}
o, r, d, i = _act(env, act)
for v_id, v in env.vehicles.items():
if v_id != "agent0":
v.set_static(True)
for step in range(5000):
act = {k: [0, 1] for k in env.vehicles.keys()}
o, r, d, i = _act(env, act)
for kkk, iii in i.items():
if iii["crash"]:
assert iii["crash_vehicle"]
if iii["crash_vehicle"]:
assert iii["crash"]
# #assert r[kkk] == -1.7777
for kkk, ddd in d.items():
if ddd and kkk != "__all__":
assert i[kkk]["out_of_road"]
# print('{} done passed!'.format(kkk))
for kkk, rrr in r.items():
if rrr == -1.7777:
# assert d[kkk]
assert i[kkk]["crash_vehicle"]
assert i[kkk]["crash"]
# print('{} reward passed!'.format(kkk))
if d["agent0"]:
break
if d["__all__"]:
break
finally:
env.close()
# success
env = MultiAgentIntersectionEnv({
"horizon": 100,
"num_agents": 2,
"success_reward": 999,
"out_of_road_penalty": 555,
"crash_done": True
})
try:
_check_spaces_before_reset(env)
obs = env.reset()
_check_spaces_after_reset(env)
env.vehicles["agent0"].set_position(
env.vehicles["agent0"].navigation.final_lane.end)
assert env.observation_space.contains(obs)
for step in range(5000):
act = {k: [0, 0] for k in env.vehicles.keys()}
o, r, d, i = _act(env, act)
if d["__all__"]:
break
kkk = "agent0"
#assert r[kkk] == 999
assert i[kkk]["arrive_dest"]
assert d[kkk]
kkk = "agent1"
#assert r[kkk] != 999
assert not i[kkk]["arrive_dest"]
assert not d[kkk]
break
finally:
env.close()
def test_ma_intersection_reset():
env = MultiAgentIntersectionEnv({"horizon": 50, "num_agents": 4})
try:
_check_spaces_before_reset(env)
obs = env.reset()
_check_spaces_after_reset(env, obs)
assert env.observation_space.contains(obs)
for step in range(1000):
act = {k: [1, 1] for k in env.vehicles.keys()}
o, r, d, i = _act(env, act)
if step == 0:
assert not any(d.values())
if d["__all__"]:
obs = env.reset()
assert env.observation_space.contains(obs)
_check_spaces_after_reset(env, obs)
assert set(env.observation_space.spaces.keys()) == set(env.action_space.spaces.keys()) == \
set(env.observations.keys()) == set(obs.keys()) == \
set(env.config["target_vehicle_configs"].keys())
break
finally:
env.close()
# Put vehicles to destination and then reset. This might cause error if agent is assigned destination BEFORE reset.
env = MultiAgentIntersectionEnv({
"horizon": 100,
"num_agents": 32,
"success_reward": 777,
"use_render": False
})
try:
_check_spaces_before_reset(env)
success_count = 0
agent_count = 0
obs = env.reset()
_check_spaces_after_reset(env, obs)
assert env.observation_space.contains(obs)
for num_reset in range(5):
for step in range(1000):
for _ in range(2):
act = {k: [1, 1] for k in env.vehicles.keys()}
o, r, d, i = _act(env, act)
# Force vehicle to success!
for v_id, v in env.vehicles.items():
loc = v.navigation.final_lane.end
v.set_position(loc)
pos = v.position
np.testing.assert_almost_equal(pos, loc, decimal=3)
new_loc = v.navigation.final_lane.end
long, lat = v.navigation.final_lane.local_coordinates(
v.position)
flag1 = (v.navigation.final_lane.length - 5 < long <
v.navigation.final_lane.length + 5)
flag2 = (v.navigation.get_current_lane_width() / 2 >= lat
>= (0.5 - v.navigation.get_current_lane_num()) *
v.navigation.get_current_lane_width())
if not v.arrive_destination:
print('sss')
assert v.arrive_destination
act = {k: [0, 0] for k in env.vehicles.keys()}
o, r, d, i = _act(env, act)
for v in env.vehicles.values():
assert len(v.navigation.checkpoints) > 2
for kkk, iii in i.items():
if iii and iii["arrive_dest"]:
# print("{} success!".format(kkk))
success_count += 1
for kkk, ddd in d.items():
if ddd and kkk != "__all__":
assert i[kkk]["arrive_dest"]
agent_count += 1
for kkk, rrr in r.items():
if d[kkk]:
assert rrr == 777
if d["__all__"]:
print("Finish {} agents. Success {} agents.".format(
agent_count, success_count))
o = env.reset()
assert env.observation_space.contains(o)
_check_spaces_after_reset(env, o)
break
finally:
env.close()