def test_collect_demonstrations(self):
params = ParameterServer()
bp = DiscreteHighwayBlueprint(params,
number_of_senarios=10,
random_seed=0)
env = SingleAgentRuntime(blueprint=bp, render=False)
env._observer = NearestAgentsObserver(params)
env._action_wrapper = BehaviorDiscreteMacroActionsML(params)
env._evaluator = TestEvaluator()
demo_behavior = bark_ml.library_wrappers.lib_fqf_iqn_qrdqn.\
tests.test_demo_behavior.TestDemoBehavior(params)
collector = DemonstrationCollector()
collection_result = collector.CollectDemonstrations(env, demo_behavior, 4, "./test_demo_collected", \
use_mp_runner=False, runner_init_params={"deepcopy" : False})
self.assertTrue(
os.path.exists("./test_demo_collected/collection_result"))
print(collection_result.get_data_frame().to_string())
experiences = collector.ProcessCollectionResult(
eval_criteria={"goal_r1": lambda x: x})
# expected length = 2 scenarios (only every second reaches goal) x 3 steps (4 executed, but first not counted)
self.assertEqual(len(experiences), 2 * 3)
collector.dump("./final_collections")
loaded_collector = DemonstrationCollector.load("./final_collections")
experiences_loaded = loaded_collector.GetDemonstrationExperiences()
print(experiences_loaded)
self.assertEqual(len(experiences_loaded), 2 * 3)
def test_general_evaluator(self):
params = ParameterServer()
bp = ContinuousSingleLaneBlueprint(params)
env = SingleAgentRuntime(blueprint=bp, render=True)
evaluator = GeneralEvaluator(params)
env._evaluator = evaluator
env.reset()
for _ in range(0, 4):
state, terminal, reward, info = env.step(np.array([0., 0.]))
print(terminal, reward)
