def test_sampler_config_2():
config = sampler_config_2()
sampler = SamplerManager(config)
assert sampler.is_empty() is False
assert isinstance(sampler.samplers["angle"], GaussianSampler)
# Check angle gaussian sampler
assert sampler.samplers["angle"].mean == config["angle"]["mean"]
assert sampler.samplers["angle"].st_dev == config["angle"]["st_dev"]
def test_empty_samplers():
empty_sampler = SamplerManager({})
assert empty_sampler.is_empty()
empty_cur_sample = empty_sampler.sample_all()
assert empty_cur_sample == {}
none_sampler = SamplerManager(None)
assert none_sampler.is_empty()
none_cur_sample = none_sampler.sample_all()
assert none_cur_sample == {}
def test_simple():
config = """
default:
trainer: ppo
batch_size: 16
beta: 5.0e-3
buffer_size: 64
epsilon: 0.2
hidden_units: 128
lambd: 0.95
learning_rate: 5.0e-3
max_steps: 2500
memory_size: 256
normalize: false
num_epoch: 3
num_layers: 2
time_horizon: 64
sequence_length: 64
summary_freq: 500
use_recurrent: false
reward_signals:
extrinsic:
strength: 1.0
gamma: 0.99
"""
# Create controller and begin training.
with tempfile.TemporaryDirectory() as dir:
run_id = "id"
save_freq = 99999
tc = TrainerController(
dir,
dir,
run_id,
save_freq,
meta_curriculum=None,
load=False,
train=True,
keep_checkpoints=1,
lesson=None,
training_seed=1337,
fast_simulation=True,
sampler_manager=SamplerManager(None),
resampling_interval=None,
)
# Begin training
env = Simple1DEnvironment()
env_manager = SimpleEnvManager(env)
trainer_config = yaml.safe_load(config)
tc.start_learning(env_manager, trainer_config)
for brain_name, mean_reward in tc._get_measure_vals().items():
assert not math.isnan(mean_reward)
assert mean_reward > 0.99
def test_sampler_config_1():
config = sampler_config_1()
sampler = SamplerManager(config)
assert sampler.is_empty() is False
assert isinstance(sampler.samplers["mass"], UniformSampler)
assert isinstance(sampler.samplers["gravity"], MultiRangeUniformSampler)
cur_sample = sampler.sample_all()
# Check uniform sampler for mass
assert sampler.samplers["mass"].min_value == config["mass"]["min_value"]
assert sampler.samplers["mass"].max_value == config["mass"]["max_value"]
assert config["mass"]["min_value"] <= cur_sample["mass"]
assert config["mass"]["max_value"] >= cur_sample["mass"]
# Check multirange_uniform sampler for gravity
assert sampler.samplers["gravity"].intervals == config["gravity"][
"intervals"]
assert check_value_in_intervals(cur_sample["gravity"],
sampler.samplers["gravity"].intervals)
def basic_trainer_controller():
return TrainerController(
trainer_factory=None,
model_path="test_model_path",
summaries_dir="test_summaries_dir",
run_id="test_run_id",
save_freq=100,
meta_curriculum=None,
train=True,
training_seed=99,
sampler_manager=SamplerManager({}),
resampling_interval=None,
)
def test_initialization_seed(numpy_random_seed, tensorflow_set_seed):
seed = 27
TrainerController(
trainer_factory=None,
model_path="",
summaries_dir="",
run_id="1",
save_freq=1,
meta_curriculum=None,
train=True,
training_seed=seed,
sampler_manager=SamplerManager({}),
resampling_interval=None,
)
numpy_random_seed.assert_called_with(seed)
tensorflow_set_seed.assert_called_with(seed)
def _check_environment_trains(env, config):
# Create controller and begin training.
with tempfile.TemporaryDirectory() as dir:
run_id = "id"
save_freq = 99999
seed = 1337
trainer_config = yaml.safe_load(config)
env_manager = SimpleEnvManager(env)
trainer_factory = TrainerFactory(
trainer_config=trainer_config,
summaries_dir=dir,
run_id=run_id,
model_path=dir,
keep_checkpoints=1,
train_model=True,
load_model=False,
seed=seed,
meta_curriculum=None,
multi_gpu=False,
)
tc = TrainerController(
trainer_factory=trainer_factory,
summaries_dir=dir,
model_path=dir,
run_id=run_id,
meta_curriculum=None,
train=True,
training_seed=seed,
fast_simulation=True,
sampler_manager=SamplerManager(None),
resampling_interval=None,
save_freq=save_freq,
)
# Begin training
tc.start_learning(env_manager)
print(tc._get_measure_vals())
for brain_name, mean_reward in tc._get_measure_vals().items():
assert not math.isnan(mean_reward)
assert mean_reward > 0.99
def create_sampler_manager(sampler_file_path, env_reset_params):
sampler_config = None
resample_interval = None
if sampler_file_path is not None:
sampler_config = load_config(sampler_file_path)
if ("resampling-interval") in sampler_config:
# Filter arguments that do not exist in the environment
resample_interval = sampler_config.pop("resampling-interval")
if (resample_interval <= 0) or (not isinstance(
resample_interval, int)):
raise SamplerException(
"Specified resampling-interval is not valid. Please provide"
" a positive integer value for resampling-interval")
else:
raise SamplerException(
"Resampling interval was not specified in the sampler file."
" Please specify it with the 'resampling-interval' key in the sampler config file."
)
sampler_manager = SamplerManager(sampler_config)
return sampler_manager, resample_interval
def test_incorrect_sampler():
config = incorrect_sampler_config()
with pytest.raises(UnityException):
SamplerManager(config)