def test_listEnvs_configExist_returnsEnvs(self):
config = TestEnvConfig.get()
experiment = Experiment(config)
envs = experiment.list_envs()
assert len(envs) > 1
def test_listEnvs_configExist_returnsEnvs(self):
config = Config(test=True)
experiment = Experiment(config)
envs = experiment.list_envs()
assert len(envs) > 1
def test_listTrainExperiments_selectExperiment_printsConfig(self):
config = Config(test=True)
explorer = Explorer(config=config)
experiment = Experiment(config)
analyzer = Analyzer(config=config, session_id=experiment.get_session_id())
experiments = explorer.list_train_experiments()
analyzer.compare_train_config(experiments)
def test_train_agent_dqn(self):
config = TestAgentConfig.get()
experiment = Experiment(config)
experiment.set_env('lunarlander-dqn')
scores = experiment.train()
assert len(scores) == (
config.get_current_exp_cfg().trainer_cfg.max_steps /
config.get_current_exp_cfg().trainer_cfg.max_episode_steps)
def test_train_agent_dqn_with_disabled_prioritized_replay(self):
config = TestAgentConfig.get()
experiment = Experiment(config)
experiment.set_env('lunarlander-dqn-noprio')
scores = experiment.train()
assert len(scores) == (
config.get_current_exp_cfg().trainer_cfg.max_steps /
config.get_current_exp_cfg().trainer_cfg.max_episode_steps)
def test_train_environment_gymai_lunarlander(self):
config = TestEnvConfig.get()
experiment = Experiment(config)
experiment.set_env('lunarlander')
scores = experiment.train()
assert len(scores) == (
config.get_current_exp_cfg().trainer_cfg.max_steps /
config.get_current_exp_cfg().trainer_cfg.max_episode_steps)
def test_train_environment_unity_banana(self):
config = TestEnvConfig.get()
experiment = Experiment(config)
experiment.set_env('banana')
scores = experiment.train()
assert len(scores) == (
config.get_current_exp_cfg().trainer_cfg.max_steps /
config.get_current_exp_cfg().trainer_cfg.max_episode_steps)
def test_train_environment_atari_breakout_rgb(self):
config = TestEnvConfig.get()
experiment = Experiment(config)
experiment.set_env('breakout-rgb')
scores = experiment.train()
assert len(scores) == (
config.get_current_exp_cfg().trainer_cfg.max_steps /
config.get_current_exp_cfg().trainer_cfg.max_episode_steps)
def test_listTrainExperiments_selectExperiment_compareEpochScore(self):
config = Config(test=True)
explorer = Explorer(config=config)
experiment = Experiment(config)
analyzer = Analyzer(config=config, session_id=experiment.get_session_id())
experiments = explorer.list_train_experiments()
for experiment in experiments:
file = analyzer.compare_train_epoch_score(experiment)
assert file is not None
def test_listTrainExperiments_selectExperiments_compareEpochData(self):
config = Config(test=True)
explorer = Explorer(config=config)
experiment = Experiment(config)
analyzer = Analyzer(config=config, session_id=experiment.get_session_id())
experiments = explorer.list_train_experiments()
file = analyzer.compare_train_epoch_cols(experiments, 'avg_score')
assert file is not None
def test_playDummy_configExist_playsWithDummyAgent(self):
config = TestEnvConfig.get()
experiment = Experiment(config)
envs = experiment.list_envs()
for env in envs:
experiment.set_env(env)
if config.get_current_exp_cfg().environment_cfg.env_type == 'unity':
break
def test_record_recordsParameters_multipleSavesOverwrites(self):
config = Configuration(test_flag=True)
session_id = Experiment(config).get_session_id()
experiments_path = config.get_app_experiments_path(train_mode=False)
model = 'model123'
header = ['episode', 'step', 'action', 'reward']
recorder = Recorder(header=header,
experiments_path=experiments_path,
session_id=session_id,
model=model,
configuration=config.get_current_exp_cfg())
header_result = recorder.get_header()
assert all([a == b for a, b in zip(header, header_result)])
parameters1 = [1, 1, 0, 0]
parameters2 = [1, 2, 1, 0]
parameters3 = [1, 3, 0, 10]
# episode 1
recorder.record(parameters1)
recorder.record(parameters2)
recorder.record(parameters3)
recorder.save()
df = recorder.get_dataframe()
(config, log) = recorder.load()
assert df.shape[0] == log.shape[0]
# episode 2
recorder.record(parameters1)
recorder.record(parameters2)
recorder.record(parameters3)
recorder.save()
df = recorder.get_dataframe()
(config, log) = recorder.load()
assert df.shape[0] == log.shape[0]
assert config['session_id'] == session_id
assert config['model'] == model
def test_playDummy_configExist_playsWithDummyAgent(self):
config = Config(test=True)
experiment = Experiment(config)
envs = experiment.list_envs()
for env in envs:
experiment.set_env(env)
if config.get_env_type() != 'unity':
experiment.play_dummy(mode='rgb-array',
model=None,
num_episodes=3,
num_steps=10)
def test_train_configExist_canTrain1Episode(self):
config = Config(test=True)
experiment = Experiment(config)
envs = experiment.list_envs()
for env in envs:
experiment.set_env(env)
if config.get_env_type() != 'unity':
max_steps = 128
max_episode_steps = 2
scores = experiment.train(max_steps=max_steps,
eval_frequency=16,
eval_steps=4,
max_episode_steps=max_episode_steps)
assert len(scores) == max_steps / max_episode_steps