def test_meta_evaluator():
set_seed(100)
tasks = SetTaskSampler(PointEnv)
max_path_length = 200
with tempfile.TemporaryDirectory() as log_dir_name:
runner = LocalRunner(
SnapshotConfig(snapshot_dir=log_dir_name,
snapshot_mode='last',
snapshot_gap=1))
env = GarageEnv(PointEnv())
algo = OptimalActionInference(env=env, max_path_length=max_path_length)
runner.setup(algo, env)
meta_eval = MetaEvaluator(runner,
test_task_sampler=tasks,
max_path_length=max_path_length,
n_test_tasks=10)
log_file = tempfile.NamedTemporaryFile()
csv_output = CsvOutput(log_file.name)
logger.add_output(csv_output)
meta_eval.evaluate(algo)
logger.log(tabular)
meta_eval.evaluate(algo)
logger.log(tabular)
logger.dump_output_type(CsvOutput)
logger.remove_output_type(CsvOutput)
with open(log_file.name, 'r') as file:
rows = list(csv.DictReader(file))
assert len(rows) == 2
assert float(rows[0]['MetaTest/CompletionRate']) < 1.0
assert float(rows[0]['MetaTest/Iteration']) == 0
assert (float(rows[0]['MetaTest/MaxReturn']) >= float(
rows[0]['MetaTest/AverageReturn']))
assert (float(rows[0]['MetaTest/AverageReturn']) >= float(
rows[0]['MetaTest/MinReturn']))
assert float(rows[1]['MetaTest/Iteration']) == 1
def test_meta_evaluator_with_tf():
set_seed(100)
tasks = SetTaskSampler(lambda: GarageEnv(PointEnv()))
max_path_length = 200
env = GarageEnv(PointEnv())
n_traj = 3
with tempfile.TemporaryDirectory() as log_dir_name:
ctxt = SnapshotConfig(snapshot_dir=log_dir_name,
snapshot_mode='none',
snapshot_gap=1)
with LocalTFRunner(ctxt) as runner:
meta_eval = MetaEvaluator(test_task_sampler=tasks,
max_path_length=max_path_length,
n_test_tasks=10,
n_exploration_traj=n_traj)
policy = GaussianMLPPolicy(env.spec)
algo = MockAlgo(env, policy, max_path_length, n_traj, meta_eval)
runner.setup(algo, env)
log_file = tempfile.NamedTemporaryFile()
csv_output = CsvOutput(log_file.name)
logger.add_output(csv_output)
meta_eval.evaluate(algo)
algo_pickle = cloudpickle.dumps(algo)
with tf.Graph().as_default():
with LocalTFRunner(ctxt) as runner:
algo2 = cloudpickle.loads(algo_pickle)
runner.setup(algo2, env)
runner.train(10, 0)
def test_pickle_meta_evaluator():
set_seed(100)
tasks = SetTaskSampler(lambda: GarageEnv(PointEnv()))
max_path_length = 200
env = GarageEnv(PointEnv())
n_traj = 3
with tempfile.TemporaryDirectory() as log_dir_name:
runner = LocalRunner(
SnapshotConfig(snapshot_dir=log_dir_name,
snapshot_mode='last',
snapshot_gap=1))
meta_eval = MetaEvaluator(test_task_sampler=tasks,
max_path_length=max_path_length,
n_test_tasks=10,
n_exploration_traj=n_traj)
policy = RandomPolicy(env.spec.action_space)
algo = MockAlgo(env, policy, max_path_length, n_traj, meta_eval)
runner.setup(algo, env)
log_file = tempfile.NamedTemporaryFile()
csv_output = CsvOutput(log_file.name)
logger.add_output(csv_output)
meta_eval.evaluate(algo)
meta_eval_pickle = cloudpickle.dumps(meta_eval)
meta_eval2 = cloudpickle.loads(meta_eval_pickle)
meta_eval2.evaluate(algo)
def test_meta_evaluator_n_traj():
set_seed(100)
tasks = SetTaskSampler(PointEnv)
max_path_length = 200
env = GarageEnv(PointEnv())
n_traj = 3
with tempfile.TemporaryDirectory() as log_dir_name:
runner = LocalRunner(
SnapshotConfig(snapshot_dir=log_dir_name,
snapshot_mode='last',
snapshot_gap=1))
algo = MockAlgo(env, max_path_length, n_traj)
runner.setup(algo, env)
meta_eval = MetaEvaluator(runner,
test_task_sampler=tasks,
max_path_length=max_path_length,
n_test_tasks=10,
n_exploration_traj=n_traj)
log_file = tempfile.NamedTemporaryFile()
csv_output = CsvOutput(log_file.name)
logger.add_output(csv_output)
meta_eval.evaluate(algo)
