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_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_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_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()