def setUp(self):
self.check_tolerance = True
self.test_save_load = True
self.num_epochs = 5
self.tolerance_threshold = 0.1
self.run_pre_training_eval = True
SummaryWriterContext._reset_globals()
def test_complete_experiment(env_name, config):
"""
Smoke test that runs a small Park QOpt experiment and fails if any exception during its execution was raised.
"""
try:
SummaryWriterContext._reset_globals()
with open(config) as f:
params = json.load(f)
checkpoint_freq = params["run_details"]["checkpoint_after_ts"]
# train agent
dataset = RLDataset(FILE_PATH)
# log experiment info to Tensorboard
evaluation_file = EVALUATION_PATH
config_file = config
experiment_name = config_file[config_file.rfind('/') +
1:config_file.rfind('.json')]
os.environ["TENSORBOARD_DIR"] = os.path.join(evaluation_file,
experiment_name)
average_reward_train, num_episodes_train, average_reward_eval, num_episodes_eval, timesteps_history, trainer, predictor, env = horizon_runner.run_gym(
params, False, None, -1, dataset)
if dataset:
dataset.save()
SummaryWriterContext._reset_globals()
except Exception:
pytest.fail('Running a small ' + str(env_name) +
' experiment in Horizon failed!')
def setUp(self):
self.check_tolerance = True
self.test_save_load = True
self.num_epochs = 5
self.tolerance_threshold = GridworldEvaluator.ABS_ERR_THRES * (
GridworldBase.REWARD_SCALE**2)
self.run_pre_training_eval = True
SummaryWriterContext._reset_globals()
def setUp(self):
self.layers = [-1, 128, -1]
self.activations = ["relu", "linear"]
self.state_dims = 5
self.action_dims = 2
self.num_samples = 1024 * 10 # multiple of minibatch_size (1024)
self.epochs = 24
SummaryWriterContext._reset_globals()
super().setUp()
def test_minibatches_per_step(self):
_epochs = self.epochs
self.epochs = 2
rl_parameters = RLParameters(gamma=0.95,
target_update_rate=0.9,
maxq_learning=True)
rainbow_parameters = RainbowDQNParameters(double_q_learning=True,
dueling_architecture=False)
training_parameters1 = TrainingParameters(
layers=self.layers,
activations=self.activations,
minibatch_size=1024,
minibatches_per_step=1,
learning_rate=0.25,
optimizer="ADAM",
)
training_parameters2 = TrainingParameters(
layers=self.layers,
activations=self.activations,
minibatch_size=128,
minibatches_per_step=8,
learning_rate=0.25,
optimizer="ADAM",
)
env1 = Env(self.state_dims, self.action_dims)
env2 = Env(self.state_dims, self.action_dims)
model_parameters1 = DiscreteActionModelParameters(
actions=env1.actions,
rl=rl_parameters,
rainbow=rainbow_parameters,
training=training_parameters1,
)
model_parameters2 = DiscreteActionModelParameters(
actions=env2.actions,
rl=rl_parameters,
rainbow=rainbow_parameters,
training=training_parameters2,
)
# minibatch_size / 8, minibatches_per_step * 8 should give the same result
logger.info("Training model 1")
trainer1 = self._train(model_parameters1, env1)
SummaryWriterContext._reset_globals()
logger.info("Training model 2")
trainer2 = self._train(model_parameters2, env2)
weight1 = trainer1.q_network.fc.layers[-1].weight.detach().numpy()
weight2 = trainer2.q_network.fc.layers[-1].weight.detach().numpy()
# Due to numerical stability this tolerance has to be fairly high
self.assertTrue(np.allclose(weight1, weight2, rtol=0.0, atol=1e-3))
self.epochs = _epochs
def setUp(self):
logging.getLogger().setLevel(logging.INFO)
SummaryWriterContext._reset_globals()
np.random.seed(SEED)
torch.manual_seed(SEED)
random.seed(SEED)
def tearDown(self):
SummaryWriterContext._reset_globals()
def setUp(self):
SummaryWriterContext._reset_globals()
def tearDown(self):
SummaryWriterContext._reset_globals()
def setUp(self):
SummaryWriterContext._reset_globals()
def setUp(self):
logging.getLogger().setLevel(logging.INFO)
SummaryWriterContext._reset_globals()
