def _generate_test_rollouts(tmpdir: str, env_named_config: str) -> str:
expert_demos_ex.run(named_configs=[env_named_config, "fast"],
config_updates=dict(
rollout_save_interval=0,
log_dir=tmpdir,
))
rollout_path = osp.abspath(f"{tmpdir}/rollouts/final.pkl")
return rollout_path
def test_transfer_learning():
"""Transfer learning smoke test.
Save a dummy AIRL test reward, then load it for transfer learning."""
with tempfile.TemporaryDirectory(prefix='imitation-transfer',
) as tmpdir:
log_dir_train = osp.join(tmpdir, "train")
run = train_ex.run(
named_configs=['cartpole', 'airl', 'fast'],
config_updates=dict(
rollout_glob="tests/data/rollouts/CartPole*.pkl",
log_dir=log_dir_train,
),
)
assert run.status == 'COMPLETED'
log_dir_data = osp.join(tmpdir, "expert_demos")
discrim_path = osp.join(log_dir_train, "checkpoints", "final", "discrim")
run = expert_demos_ex.run(
named_configs=['cartpole', 'fast'],
config_updates=dict(
log_dir=log_dir_data,
reward_type='DiscrimNetAIRL',
reward_path=discrim_path,
),
)
assert run.status == 'COMPLETED'
def test_transfer_learning(tmpdir):
"""Transfer learning smoke test.
Saves a dummy AIRL test reward, then loads it for transfer learning.
"""
log_dir_train = osp.join(tmpdir, "train")
run = train_ex.run(
named_configs=["cartpole", "airl", "fast"],
config_updates=dict(
rollout_path=
"tests/data/expert_models/cartpole_0/rollouts/final.pkl",
log_dir=log_dir_train,
),
)
assert run.status == "COMPLETED"
_check_train_ex_result(run.result)
_check_rollout_stats(run.result["imit_stats"])
log_dir_data = osp.join(tmpdir, "expert_demos")
discrim_path = osp.join(log_dir_train, "checkpoints", "final", "discrim")
run = expert_demos_ex.run(
named_configs=["cartpole", "fast"],
config_updates=dict(
log_dir=log_dir_data,
reward_type="DiscrimNet",
reward_path=discrim_path,
),
)
assert run.status == "COMPLETED"
_check_rollout_stats(run.result)
def test_expert_demos_main(tmpdir):
"""Smoke test for imitation.scripts.expert_demos.rollouts_and_policy."""
run = expert_demos_ex.run(
named_configs=["cartpole", "fast"],
config_updates=dict(log_root=tmpdir, ),
)
assert run.status == "COMPLETED"
assert isinstance(run.result, dict)
def test_expert_demos_main():
"""Smoke test for imitation.scripts.expert_demos.rollouts_and_policy"""
with tempfile.TemporaryDirectory(prefix='imitation-data_collect-main',
) as tmpdir:
run = expert_demos_ex.run(
named_configs=['cartpole', 'fast'],
config_updates=dict(
log_root=tmpdir,
),
)
assert run.status == 'COMPLETED'
def test_expert_demos_rollouts_from_policy(tmpdir):
"""Smoke test for imitation.scripts.expert_demos.rollouts_from_policy."""
run = expert_demos_ex.run(
command_name="rollouts_from_policy",
named_configs=["cartpole", "fast"],
config_updates=dict(
log_root=tmpdir,
rollout_save_path=osp.join(tmpdir, "rollouts", "test.pkl"),
policy_path="tests/data/expert_models/cartpole_0/policies/final/",
),
)
assert run.status == "COMPLETED"
def test_expert_demos_rollouts_from_policy():
"""Smoke test for imitation.scripts.expert_demos.rollouts_from_policy"""
with tempfile.TemporaryDirectory(prefix='imitation-data_collect-policy',
) as tmpdir:
run = expert_demos_ex.run(
command_name="rollouts_from_policy",
named_configs=['cartpole', 'fast'],
config_updates=dict(
log_root=tmpdir,
rollout_save_dir=osp.join(tmpdir, "rollouts"),
policy_path="expert_models/PPO2_CartPole-v1_0",
))
assert run.status == 'COMPLETED'
