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_iqn_agent(self):
params = ParameterServer()
params["ML"]["BaseAgent"]["NumSteps"] = 2
params["ML"]["BaseAgent"]["MaxEpisodeSteps"] = 2
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)
iqn_agent = IQNAgent(agent_save_dir="./save_dir",
env=env,
params=params)
iqn_agent.train_episode()
iqn_agent.save(checkpoint_type="best")
iqn_agent.save(checkpoint_type="last")
loaded_agent = IQNAgent(agent_save_dir="./save_dir",
checkpoint_load="best")
loaded_agent2 = IQNAgent(agent_save_dir="./save_dir",
checkpoint_load="last")
loaded_agent_with_env = IQNAgent(env=env,
agent_save_dir="./save_dir",
checkpoint_load="last")
loaded_agent_with_env.train_episode()
self.assertEqual(loaded_agent.ml_behavior.action_space.n,
iqn_agent.ml_behavior.action_space.n)
return
