def test_uniform_vehicle_distribution_default_params(self):
param_server = ParameterServer(
filename=
"modules/runtime/tests/data/deterministic_scenario_test.json")
scenario_generation = DeterministicScenarioGeneration(
num_scenarios=2, random_seed=0, params=param_server)
scenario_generation.dump_scenario_list("test.scenario")
self.assertEqual(len(scenario_generation._scenario_list), 2)
self.assertEqual(
len(scenario_generation._scenario_list[0]._agent_list), 2)
# assert scenario generation
agent0 = scenario_generation._scenario_list[0]._agent_list[0]
np.testing.assert_array_equal(agent0.state,
np.array([0., 0., 0., 0., 5.0]))
self.assertEqual(agent0.id, 0)
self.assertEqual(str(agent0.behavior_model),
"bark.behavior.BehaviorConstantVelocity")
self.assertEqual(str(agent0.dynamic_model),
"bark.dynamic.SingleTrackModel")
self.assertEqual(str(agent0.execution_model),
"bark.dynamic.ExecutionModelInterpolate")
agent1 = scenario_generation._scenario_list[0]._agent_list[1]
#np.testing.assert_array_equal(agent1.state, np.array([0., 10., 0., 0., 5.0]))
self.assertEqual(agent1.id, 1)
self.assertEqual(str(agent1.behavior_model),
"bark.behavior.BehaviorIDMClassic")
# TODO(@hart): make sure the map is not reloaded for rl-performance
for _ in range(0, 10):
_ = scenario_generation.get_next_scenario()
# loading serialized scenarios
scenario_loader = ScenarioGeneration()
scenario_loader.load_scenario_list("test.scenario")
self.assertEqual(len(scenario_loader._scenario_list), 2)
self.assertEqual(
len(scenario_loader._scenario_list[0]._agent_list),
len(scenario_generation._scenario_list[0]._agent_list))
self.assertEqual(
str(scenario_loader._scenario_list[0]._agent_list[0].behavior_model
), "bark.behavior.BehaviorConstantVelocity")
#np.testing.assert_array_equal(scenario_loader._scenario_list[0]._agent_list[0].state,
# np.array([0., 0., 0., 0., 5.0]))
self.assertEqual(
str(scenario_loader._scenario_list[0]._agent_list[1].behavior_model
), "bark.behavior.BehaviorIDMClassic")
def test_motion_primitives_concat_state(self):
params = ParameterServer(
filename="tests/data/deterministic_scenario_test.json")
scenario_generation = DeterministicScenarioGeneration(num_scenarios=3,
random_seed=0,
params=params)
state_observer = SimpleObserver(params=params)
action_wrapper = MotionPrimitives(params=params)
evaluator = GoalReached(params=params)
viewer = MPViewer(params=params,
x_range=[-30, 30],
y_range=[-40, 40],
use_world_bounds=True)
runtimerl = RuntimeRL(action_wrapper=action_wrapper,
observer=state_observer,
evaluator=evaluator,
step_time=0.2,
viewer=viewer,
scenario_generator=scenario_generation,
render=False)
for _ in range(0, 3):
runtimerl.reset()
for _ in range(0, 50): # run each scenario for 10 steps
action = action_wrapper.action_space.sample()
next_observed_state, reward, done, info = \
runtimerl.step(action)
if done:
print("State: {} \n Reward: {} \n Done {}, Info: {} \n \
=================================================" . \
format(next_observed_state, reward, done, info))
break
def test_python_model(self):
param_server = ParameterServer(
filename="modules/runtime/tests/data/deterministic_scenario.json")
scenario_generation = DeterministicScenarioGeneration(num_scenarios=3,
random_seed=0,
params=param_server)
viewer = MPViewer(params=param_server,
follow_agent_id=False,
use_world_bounds=True)
env = Runtime(0.2,
viewer,
scenario_generation,
render=True)
single_track_model = SingleTrackModel(param_server)
behavior_model = DynamicBehaviorModel(single_track_model, param_server)
env.reset()
env._world.get_agent(0).behavior_model = behavior_model
env._world.get_agent(0).behavior_model.clone()
env.reset()
env._world.get_agent(0).behavior_model = behavior_model
env._world.get_agent(0).behavior_model.set_last_action(np.array([1., 2.]))
print(env._world.get_agent(0).behavior_model.get_last_action())
env._world.step(0.2)
print(env._world.get_agent(0).behavior_model.get_last_action())
def _build_configuration(self):
"""Builds a configuration using an SAC agent
"""
self._scenario_generator = \
DeterministicScenarioGeneration(num_scenarios=3,
random_seed=0,
params=self._params)
self._observer = CustomObserver(params=self._params)
self._behavior_model = DynamicModel(params=self._params)
self._evaluator = CustomEvaluator(params=self._params)
viewer = MPViewer(params=self._params,
x_range=[-30, 30],
y_range=[-20, 40],
follow_agent_id=True)
self._viewer = viewer
# self._viewer = VideoRenderer(renderer=viewer, world_step_time=0.2)
self._runtime = RuntimeRL(action_wrapper=self._behavior_model,
observer=self._observer,
evaluator=self._evaluator,
step_time=0.2,
viewer=self._viewer,
scenario_generator=self._scenario_generator)
tfa_env = tf_py_environment.TFPyEnvironment(TFAWrapper(self._runtime))
self._agent = SACAgent(tfa_env, params=self._params)
self._runner = SACRunner(tfa_env,
self._agent,
params=self._params,
unwrapped_runtime=self._runtime)
def test_python_model_inheritance(self):
param_server = ParameterServer(
filename="modules/runtime/tests/data/deterministic_scenario.json")
scenario_generation = DeterministicScenarioGeneration(
num_scenarios=3, random_seed=0, params=param_server)
viewer = MPViewer(params=param_server,
follow_agent_id=False,
use_world_bounds=True)
scenario, idx = scenario_generation.get_next_scenario()
world = scenario.get_world_state()
behavior_model = PythonBehaviorModelWrapperInheritance(param_server)
world.GetAgent(0).behavior_model = behavior_model
world.GetAgent(0).behavior_model.SetLastAction(
np.array([1., 1.], dtype=np.float32))
world.Step(0.2)
def test_tfa_runtime():
params = ParameterServer(
filename="tests/data/deterministic_scenario_test.json")
scenario_generation = DeterministicScenarioGeneration(num_scenarios=3,
random_seed=0,
params=params)
state_observer = ClosestAgentsObserver(params=params)
action_wrapper = DynamicModel(params=params)
evaluator = GoalReached(params=params)
viewer = MPViewer(params=params,
x_range=[-30, 30],
y_range=[-20, 40],
follow_agent_id=True) # use_world_bounds=True
runtimerl = RuntimeRL(action_wrapper=action_wrapper,
observer=state_observer,
evaluator=evaluator,
step_time=0.05,
viewer=viewer,
scenario_generator=scenario_generation)
tfa_env = TFAWrapper(runtimerl)
_ = tfa_env.reset()
utils.validate_py_environment(tfa_env, episodes=5)
_ = tf_py_environment.TFPyEnvironment(tfa_env)
def test_runner():
params = ParameterServer(
filename="tests/data/deterministic_scenario_test.json")
base_dir = os.path.dirname(os.path.dirname(__file__))
params["BaseDir"] = base_dir
scenario_generation = DeterministicScenarioGeneration(num_scenarios=3,
random_seed=0,
params=params)
state_observer = ClosestAgentsObserver(params=params)
action_wrapper = DynamicModel(params=params)
evaluator = GoalReached(params=params)
viewer = MPViewer(params=params,
x_range=[-30, 30],
y_range=[-20, 40],
follow_agent_id=True)
runtimerl = RuntimeRL(action_wrapper=action_wrapper,
observer=state_observer,
evaluator=evaluator,
step_time=0.2,
viewer=viewer,
scenario_generator=scenario_generation,
render=False)
tfa_env = tf_py_environment.TFPyEnvironment(TFAWrapper(runtimerl))
sac_agent = SACAgent(tfa_env, params=params)
tfa_runner = TFARunner(tfa_env,
sac_agent,
params=params,
unwrapped_runtime=runtimerl)
tfa_runner.collect_initial_episodes()
# main functionalities
tfa_runner.train()
tfa_runner.visualize()
tfa_runner.evaluate()
def test_agent(self):
params = ParameterServer(
filename="tests/data/deterministic_scenario_test.json")
base_dir = os.path.dirname(os.path.dirname(__file__))
params["BaseDir"] = base_dir
scenario_generation = DeterministicScenarioGeneration(num_scenarios=2,
random_seed=0,
params=params)
state_observer = ClosestAgentsObserver(params=params)
action_wrapper = DynamicModel(params=params)
evaluator = GoalReached(params=params)
viewer = MPViewer(params=params,
x_range=[-30, 30],
y_range=[-20, 40],
follow_agent_id=True) # use_world_bounds=True
runtimerl = RuntimeRL(action_wrapper=action_wrapper,
observer=state_observer,
evaluator=evaluator,
step_time=0.05,
viewer=viewer,
scenario_generator=scenario_generation)
tfa_env = tf_py_environment.TFPyEnvironment(TFAWrapper(runtimerl))
sac_agent = SACAgent(tfa_env, params=params)
self.assertEqual(sac_agent._agent.name, "sac_agent")
sac_agent.reset()
# TODO(@hart): does not work because of read-only file-system
# sac_agent.save()
sac_agent.load()
def test_runtime_rl(self):
params = ParameterServer(
filename="tests/data/deterministic_scenario_test.json")
scenario_generation = DeterministicScenarioGeneration(num_scenarios=2,
random_seed=0,
params=params)
state_observer = SimpleObserver(params=params)
action_wrapper = DynamicModel(params=params)
evaluator = GoalReached(params=params)
viewer = MPViewer(params=params,
x_range=[-30, 30],
y_range=[-40, 40],
use_world_bounds=True) #use_world_bounds=True) #
runtimerl = RuntimeRL(action_wrapper=action_wrapper,
observer=state_observer,
evaluator=evaluator,
step_time=0.2,
viewer=viewer,
scenario_generator=scenario_generation,
render=False)
start_time = time.time()
for _ in range(0, 100):
runtimerl.reset()
done = False
reward = 0.
for _ in range(0, 50): # run each scenario for 10 steps
action = action_wrapper.action_space.sample(
) / 100 # to go straight
next_observed_state, reward, done, info = \
runtimerl.step(action)
# observer
self.assertEqual(len(next_observed_state), 16)
np.testing.assert_array_equal(
next_observed_state[0:4],
state_observer._normalize(
runtimerl._world.agents[100].state)[1:5])
np.testing.assert_array_equal(
next_observed_state[4:8],
state_observer._normalize(
runtimerl._world.agents[101].state)[1:5])
if done:
print("State: {} \n Reward: {} \n Done {}, Info: {} \n \
=================================================" . \
format(next_observed_state, reward, done, info))
break
# must assert to equal as the agent reaches the goal in the
# specified number of steps
self.assertEqual(done, True)
# goal must have been reached which returns a reward of 1.
self.assertEqual(reward, 1.)
self.assertEqual(runtimerl._world.agents[100].id, 100)
self.assertEqual(runtimerl._world.agents[101].id, 101)
end_time = time.time()
print("100 runs took {}s.".format(str(end_time - start_time)))
def test_runtime(self):
"""Asserts the runtime to make sure the basic
functionality is given by the current state of BARK.
"""
param_server = ParameterServer(
filename="tests/data/deterministic_scenario_test.json")
scenario_generation = DeterministicScenarioGeneration(num_scenarios=3,
random_seed=0,
params=param_server)
param_server["Visualization"]["Agents"]["DrawRoute",
"Draw the local map of each agent",
True]
viewer = MPViewer(params=param_server,
use_world_bounds=True)
env = Runtime(0.2,
viewer,
scenario_generation,
render=True)
env.reset()
agent_ids = []
agent_states = []
centers_of_driving_corridor= []
for key, agent in env._world.agents.items():
agent_ids.append(agent.id)
agent_states.append(agent.state)
for i in range(0, 5):
print("Scenario {}:".format(
str(env._scenario_generator._current_scenario_idx)))
# assert scenario ids
self.assertEqual(env._scenario_generator._current_scenario_idx, (i % 3) + 1)
for _ in range(0, 35):
# assert ids
states_before = []
for i, (key, agent) in enumerate(env._world.agents.items()):
self.assertEqual(key, agent.id)
self.assertEqual(agent_ids[i], agent.id)
states_before.append(agent.state)
# TODO(@hart): why does this not work
print(key, agent.goal_definition.goal_shape)
env.step()
# assert state has been changed by the step() function
for i, (key, agent) in enumerate(env._world.agents.items()):
np.testing.assert_equal(np.any(np.not_equal(states_before[i],
agent.state)), True)
# check whether the reset works
env.reset()
for i, (key, agent) in enumerate(env._world.agents.items()):
self.assertEqual(key, agent.id)
self.assertEqual(agent_ids[i], agent.id)
np.testing.assert_array_equal(agent_states[i], agent.state)
def test_visualization(self):
param_server = ParameterServer(
filename="modules/runtime/tests/data/deterministic_scenario.json")
scenario_generation = DeterministicScenarioGeneration(
num_scenarios=3, random_seed=0, params=param_server)
viewer = MPViewer(params=param_server,
follow_agent_id=False,
use_world_bounds=True)
env = Runtime(0.2, viewer, scenario_generation, render=True)
env.reset()
for _ in range(0, 5):
print("Scenario {}:".format(
str(env._scenario_generator._current_scenario_idx)))
for _ in range(0, 5):
env.step()
env.reset()
def test_python_model(self):
param_server = ParameterServer(
filename="modules/runtime/tests/data/deterministic_scenario.json")
scenario_generation = DeterministicScenarioGeneration(num_scenarios=3,
random_seed=0,
params=param_server)
viewer = MPViewer(params=param_server,
follow_agent_id=False,
use_world_bounds=True)
env = Runtime(0.2,
viewer,
scenario_generation,
render=True)
behavior_model = DummyBehaviorModel(param_server)
env.reset()
env._world.get_agent(0).behavior_model = behavior_model
env._world.get_agent(0).behavior_model.clone()
np.testing.assert_array_equal(
env._world.get_agent(0).behavior_model.plan(0.2, env._world)[1],
np.array([0.2, 5111.626, 5106.8305 + 0.2, 1.5, 10]))
env.reset()
env._world.get_agent(0).behavior_model = behavior_model
env._world.step(0.2)
np.testing.assert_array_equal(
env._world.get_agent(0).state,
np.array([0.2, 5111.626, 5106.8305 + 0.2, 1.5, 10], dtype=np.float32))
env._world.step(0.2)
np.testing.assert_array_equal(
env._world.get_agent(0).state,
np.array([0.4, 5111.626, 5106.8305 + 0.4, 1.5, 10], dtype=np.float32))
env._world.step(0.2)
np.testing.assert_array_equal(
env._world.get_agent(0).state,
np.array([0.6, 5111.626, 5106.8305 + 0.6, 1.5, 10], dtype=np.float32))
print("History:", env._world.get_agent(0).history)
# environment loop
env.reset()
for i in range(0, 7):
env.step()