def setup_experiment(
env_name: str,
algo_name: str,
extra_info: str = None,
base_dir: str = pyrado.TEMP_DIR,
include_slurm_id: bool = True,
):
"""
Setup a new experiment for recording.
:param env_name: environment trained on
:param algo_name: algorithm trained with, usually also includes the policy type, e.g. 'a2c_fnn'
:param extra_info: additional information on the experiment (free form)
:param base_dir: base storage directory
:param include_slurm_id: if a SLURM ID is present in the environment variables, include them in the experiment ID
"""
# Create experiment object
exp = Experiment(env_name,
algo_name,
extra_info,
base_dir=base_dir,
include_slurm_id=include_slurm_id)
# Create the folder
os.makedirs(exp, exist_ok=True)
# Set the global logger variable
set_log_prefix_dir(exp)
return exp
def setup_experiment(env_name: str,
algo_name: str,
extra_info: str = None,
base_dir: str = pyrado.TEMP_DIR):
""" Setup a new experiment for recording. """
# Create experiment object
exp = Experiment(env_name, algo_name, extra_info, base_dir=base_dir)
# Create the folder
os.makedirs(exp, exist_ok=True)
# Set the global logger variable
set_log_prefix_dir(exp)
return exp
def test_parallel_sampling_deterministic_smoke_test_w_min_steps(
tmpdir_factory, env: SimEnv, policy: Policy, algo, min_rollouts: int,
min_steps: int):
env.max_steps = 20
seeds = (0, 1)
nums_workers = (1, 2, 4)
logging_results = []
rollout_results: List[List[List[List[StepSequence]]]] = []
for seed in seeds:
logging_results.append((seed, []))
rollout_results.append([])
for num_workers in nums_workers:
pyrado.set_seed(seed)
policy.init_param(None)
ex_dir = str(
tmpdir_factory.mktemp(
f"seed={seed}-num_workers={num_workers}"))
set_log_prefix_dir(ex_dir)
vfcn = FNN(input_size=env.obs_space.flat_dim,
output_size=1,
hidden_sizes=[16, 16],
hidden_nonlin=to.tanh)
critic = GAE(vfcn,
gamma=0.98,
lamda=0.95,
batch_size=32,
lr=1e-3,
standardize_adv=False)
alg = algo(
ex_dir,
env,
policy,
critic,
max_iter=3,
min_rollouts=min_rollouts,
min_steps=min_steps * env.max_steps,
num_workers=num_workers,
)
alg.sampler = RolloutSavingWrapper(alg.sampler)
alg.train()
with open(f"{ex_dir}/progress.csv") as f:
logging_results[-1][1].append(str(f.read()))
rollout_results[-1].append(alg.sampler.rollouts)
# Test that the observations for all number of workers are equal.
for rollouts in rollout_results:
for ros_a, ros_b in [(a, b) for a in rollouts for b in rollouts]:
assert len(ros_a) == len(ros_b)
for ro_a, ro_b in zip(ros_a, ros_b):
assert len(ro_a) == len(ro_b)
for r_a, r_b in zip(ro_a, ro_b):
assert r_a.observations == pytest.approx(r_b.observations)
# Test that different seeds actually produce different results.
for results_a, results_b in [(a, b) for seed_a, a in logging_results
for seed_b, b in logging_results
if seed_a != seed_b]:
for result_a, result_b in [(a, b) for a in results_a for b in results_b
if a is not b]:
assert result_a != result_b
# Test that same seeds produce same results.
for _, results in logging_results:
for result_a, result_b in [(a, b) for a in results for b in results]:
assert result_a == result_b
def ex_dir(tmpdir):
# Fixture providing an experiment directory
set_log_prefix_dir(tmpdir)
return tmpdir
def ex_dir(tmpdir):
set_log_prefix_dir(tmpdir)
return tmpdir