def test_meta_evaluator():
set_seed(100)
tasks = SetTaskSampler(PointEnv, wrapper=set_length)
max_episode_length = 200
with tempfile.TemporaryDirectory() as log_dir_name:
trainer = Trainer(
SnapshotConfig(snapshot_dir=log_dir_name,
snapshot_mode='last',
snapshot_gap=1))
env = PointEnv(max_episode_length=max_episode_length)
algo = OptimalActionInference(env=env,
max_episode_length=max_episode_length)
trainer.setup(algo, env)
meta_eval = MetaEvaluator(test_task_sampler=tasks, 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/__unnamed_task__/TerminationRate']) < 1.0
assert float(rows[0]['MetaTest/__unnamed_task__/Iteration']) == 0
assert (float(rows[0]['MetaTest/__unnamed_task__/MaxReturn']) >= float(
rows[0]['MetaTest/__unnamed_task__/AverageReturn']))
assert (float(rows[0]['MetaTest/__unnamed_task__/AverageReturn']) >=
float(rows[0]['MetaTest/__unnamed_task__/MinReturn']))
assert float(rows[1]['MetaTest/__unnamed_task__/Iteration']) == 1
def test_one_folder(self, meta_train_dir, itrs):
snapshot_config = SnapshotConfig(snapshot_dir=meta_train_dir,
snapshot_mode='all',
snapshot_gap=1)
runner = LocalRunner(snapshot_config=snapshot_config)
meta_sampler = AllSetTaskSampler(self.meta_task_cls)
runner.restore(meta_train_dir)
meta_evaluator = MetaEvaluator(
runner,
test_task_sampler=meta_sampler,
max_path_length=self.max_path_length,
n_test_tasks=meta_sampler.n_tasks,
n_exploration_traj=self.adapt_rollout_per_task,
prefix='')
for itr in itrs:
log_filename = os.path.join(meta_train_dir,
'meta-test-itr_{}.csv'.format(itr))
logger.add_output(CsvOutput(log_filename))
logger.log("Writing into {}".format(log_filename))
runner.restore(meta_train_dir, from_epoch=itr)
meta_evaluator.evaluate(runner._algo, self.test_rollout_per_task)
tabular.record('Iteration', runner._stats.total_epoch)
tabular.record('TotalEnvSteps', runner._stats.total_env_steps)
logger.log(tabular)
logger.dump_output_type(CsvOutput)
logger.remove_output_type(CsvOutput)
def test_meta_evaluator_with_tf():
set_seed(100)
tasks = SetTaskSampler(PointEnv, wrapper=set_length)
max_episode_length = 200
env = PointEnv()
n_eps = 3
with tempfile.TemporaryDirectory() as log_dir_name:
ctxt = SnapshotConfig(snapshot_dir=log_dir_name,
snapshot_mode='none',
snapshot_gap=1)
with TFTrainer(ctxt) as trainer:
meta_eval = MetaEvaluator(test_task_sampler=tasks,
n_test_tasks=10,
n_exploration_eps=n_eps)
policy = GaussianMLPPolicy(env.spec)
algo = MockAlgo(env, policy, max_episode_length, n_eps, meta_eval)
trainer.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)
tf.compat.v1.reset_default_graph()
with TFTrainer(ctxt) as trainer:
algo2 = cloudpickle.loads(algo_pickle)
trainer.setup(algo2, env)
trainer.train(10, 0)
def alg_train(ctxt=None):
get_args(parser)
args = parser.parse_args()
args.prefix = use_prefix
set_seed(args.seed)
env = GymEnv(args.env_name)
if args.env_norm:
env = normalize(env)
trainer = Trainer(ctxt)
logger.remove_all()
logger.add_output(StdLogger(args.log_interval))
if not args.no_wb:
wb_logger = WbOutput(args.log_interval, base_args)
logger.add_output(wb_logger)
algo = get_algo(env, trainer, args)
if args.cuda:
set_gpu_mode(True)
algo.to()
else:
set_gpu_mode(False)
trainer.train(n_epochs=args.n_epochs, batch_size=args.batch_size)
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 setup_method(self):
self.graph = tf.Graph()
self.sess = tf.Session(graph=self.graph)
self.sess.__enter__()
logger.add_output(NullOutput())
deterministic.set_seed(1)
# initialize global singleton_pool for each test case
from garage.sampler import singleton_pool
singleton_pool.initialize(1)
def setup_class(cls):
cls.reset_tf()
cls.log_dir = tempfile.TemporaryDirectory()
cls.prev_log_dir = snapshotter.snapshot_dir
cls.prev_mode = snapshotter.snapshot_mode
snapshotter.snapshot_dir = cls.log_dir.name
snapshotter.snapshot_mode = 'all'
logger.add_output(NullOutput())
def test_log_multitask_performance_task_id():
lengths = np.array([10, 5, 1, 1])
batch = TrajectoryBatch(
EnvSpec(akro.Box(np.array([0., 0., 0.]), np.array([1., 1., 1.])),
akro.Box(np.array([-1., -1.]), np.array([0., 0.]))),
observations=np.ones((sum(lengths), 3), dtype=np.float32),
last_observations=np.ones((len(lengths), 3), dtype=np.float32),
actions=np.zeros((sum(lengths), 2), dtype=np.float32),
rewards=np.array([
0.34026529, 0.58263177, 0.84307509, 0.97651095, 0.81723901,
0.22631398, 0.03421301, 0.97515046, 0.64311832, 0.65068933,
0.17657714, 0.04783857, 0.73904013, 0.41364329, 0.52235551,
0.24203526, 0.43328910
]),
terminals=np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1],
dtype=bool),
env_infos={
'success':
np.array([0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1],
dtype=bool),
'task_id':
np.array([1] * 10 + [3] * 5 + [1] + [4])
},
agent_infos={},
lengths=lengths)
log_file = tempfile.NamedTemporaryFile()
csv_output = dowel.CsvOutput(log_file.name)
logger.add_output(csv_output)
log_multitask_performance(7, batch, 0.8, {
1: 'env1',
3: 'env2',
4: 'env3',
5: 'env4'
})
logger.log(tabular)
logger.dump_output_type(dowel.CsvOutput)
with open(log_file.name, 'r') as file:
rows = list(csv.DictReader(file))
res = {k: float(r) for (k, r) in rows[0].items()}
assert res['env1/Iteration'] == 7
assert res['env2/Iteration'] == 7
assert res['env3/Iteration'] == 7
assert res['env4/Iteration'] == 7
assert res['env1/NumTrajs'] == 2
assert res['env2/NumTrajs'] == 1
assert res['env3/NumTrajs'] == 1
assert res['env4/NumTrajs'] == 0
assert math.isclose(res['env1/SuccessRate'], 0.5)
assert math.isclose(res['env2/SuccessRate'], 1.0)
assert math.isclose(res['env3/SuccessRate'], 1.0)
assert math.isnan(res['env4/SuccessRate'])
assert math.isnan(res['env4/AverageReturn'])
def setup_method(self):
"""Setup the Session and default Graph."""
self.graph = tf.Graph()
for c in self.graph.collections:
self.graph.clear_collection(c)
self.graph_manager = self.graph.as_default()
self.graph_manager.__enter__()
self.sess = tf.compat.v1.Session(graph=self.graph)
self.sess_manager = self.sess.as_default()
self.sess_manager.__enter__()
self.sess.__enter__()
logger.add_output(NullOutput())
deterministic.set_seed(1)
def setup_method(self):
self.graph = tf.Graph()
for c in self.graph.collections:
self.graph.clear_collection(c)
self.graph_manager = self.graph.as_default()
self.graph_manager.__enter__()
self.sess = tf.compat.v1.Session(graph=self.graph)
self.sess_manager = self.sess.as_default()
self.sess_manager.__enter__()
self.sess.__enter__()
logger.add_output(NullOutput())
deterministic.set_seed(1)
# initialize global singleton_pool for each test case
from metarl.sampler import singleton_pool
singleton_pool.initialize(1)
def test_log_performance():
lengths = np.array([10, 5, 1, 1])
batch = EpisodeBatch(
EnvSpec(akro.Box(np.array([0., 0., 0.]), np.array([1., 1., 1.])),
akro.Box(np.array([-1., -1.]), np.array([0., 0.]))),
observations=np.ones((sum(lengths), 3), dtype=np.float32),
last_observations=np.ones((len(lengths), 3), dtype=np.float32),
actions=np.zeros((sum(lengths), 2), dtype=np.float32),
rewards=np.array([
0.34026529, 0.58263177, 0.84307509, 0.97651095, 0.81723901,
0.22631398, 0.03421301, 0.97515046, 0.64311832, 0.65068933,
0.17657714, 0.04783857, 0.73904013, 0.41364329, 0.52235551,
0.24203526, 0.43328910
]),
step_types=np.array(
[StepType.FIRST] + [StepType.MID] * (lengths[0] - 2) +
[StepType.TERMINAL] + [StepType.FIRST] + [StepType.MID] *
(lengths[1] - 2) + [StepType.TERMINAL] + [StepType.FIRST] +
[StepType.FIRST],
dtype=StepType),
env_infos={
'success':
np.array([0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1],
dtype=bool)
},
agent_infos={},
lengths=lengths)
log_file = tempfile.NamedTemporaryFile()
csv_output = dowel.CsvOutput(log_file.name)
logger.add_output(csv_output)
log_performance(7, batch, 0.8, prefix='test_log_performance')
logger.log(tabular)
logger.dump_output_type(dowel.CsvOutput)
with open(log_file.name, 'r') as file:
rows = list(csv.DictReader(file))
res = {k: float(r) for (k, r) in rows[0].items()}
assert res['test_log_performance/Iteration'] == 7
assert res['test_log_performance/NumEpisodes'] == 4
assert math.isclose(res['test_log_performance/SuccessRate'], 0.75)
assert math.isclose(res['test_log_performance/TerminationRate'], 0.5)
assert math.isclose(res['test_log_performance/AverageDiscountedReturn'],
1.1131040640673113)
assert math.isclose(res['test_log_performance/AverageReturn'],
2.1659965525)
assert math.isclose(res['test_log_performance/StdReturn'],
2.354067152038576)
def eval(self, policy, n_episodes=20, greedy=True, load_from_file=False,
save_replay=False):
if load_from_file:
logger.add_output(dowel.StdOutput())
logger.log('Evaluating policy, {} episodes, greedy = {} ...'.format(
n_episodes, greedy))
n_won = 0
episode_rewards = []
pbar = ProgBarCounter(n_episodes)
for e in range(n_episodes):
obs = self.reset()
policy.reset([True])
info = {'battle_won': False}
terminated = False
episode_rewards.append(0)
while not terminated:
obs = np.array([obs]) # add [.] for vec_env
avail_actions = np.array([self.get_avail_actions()])
actions, agent_infos = policy.get_actions(obs,
avail_actions, greedy=greedy)
obs, reward, terminated, info = self.step(actions[0])
if not self.centralized:
terminated = all(terminated)
episode_rewards[-1] += np.mean(reward)
pbar.inc(1)
if save_replay:
self.save_replay()
# If case SC2 restarts during eval, KeyError: 'battle_won' can happen
# Take precaution
if type(info) == dict:
if 'battle_won' in info.keys():
n_won += 1 if info['battle_won'] else 0
pbar.stop()
policy.reset([True])
win_rate = n_won / n_episodes
avg_return = np.mean(episode_rewards)
logger.log('EvalWinRate: {}'.format(win_rate))
logger.log('EvalAvgReturn: {}'.format(avg_return))
if not load_from_file:
tabular.record('EvalWinRate', win_rate)
tabular.record('EvalAvgReturn', avg_return)
def _make_context(self, *args, **kwargs):
"""Make a context from the template information and variant args.
Currently, all arguments should be keyword arguments.
Args:
args (list): Should be empty.
kwargs (dict): Keyword arguments for the wrapped function. Will be
logged to `variant.json`
Returns:
ExperimentContext: The created experiment context.
Raises:
ValueError: If args is not empty.
"""
if args:
raise ValueError('garage.experiment currently only supports '
'keyword arguments')
name = self.name
if name is None:
name = self.function.__name__
if self.name_parameters:
name = self._augment_name(name, kwargs)
log_dir = self.log_dir
if log_dir is None:
log_dir = ('{data}/local/{prefix}/{name}'.format(
data=os.path.join(os.getcwd(), 'data'),
prefix=self.prefix,
name=name))
log_dir = _make_sequential_log_dir(log_dir)
tabular_log_file = os.path.join(log_dir, 'progress.csv')
text_log_file = os.path.join(log_dir, 'debug.log')
variant_log_file = os.path.join(log_dir, 'variant.json')
metadata_log_file = os.path.join(log_dir, 'metadata.json')
dump_json(variant_log_file, kwargs)
git_root_path, metadata = get_metadata()
dump_json(metadata_log_file, metadata)
if git_root_path and self.archive_launch_repo:
make_launcher_archive(git_root_path=git_root_path, log_dir=log_dir)
logger.add_output(dowel.TextOutput(text_log_file))
logger.add_output(dowel.CsvOutput(tabular_log_file))
logger.add_output(
dowel.TensorBoardOutput(log_dir, x_axis='TotalEnvSteps'))
logger.add_output(dowel.StdOutput())
logger.push_prefix('[{}] '.format(name))
logger.log('Logging to {}'.format(log_dir))
return ExperimentContext(snapshot_dir=log_dir,
snapshot_mode=self.snapshot_mode,
snapshot_gap=self.snapshot_gap)
def read_cmd(cls, env_cls):
logger.add_output(StdOutput())
parser = argparse.ArgumentParser()
parser.add_argument("folder", nargs="+")
# Adaptation parameters
parser.add_argument("--adapt-rollouts",
nargs="?",
default=10,
type=int)
parser.add_argument("--test-rollouts", nargs="?", default=10, type=int)
parser.add_argument("--max-path-length",
nargs="?",
default=100,
type=int)
# Number of workers
parser.add_argument("--parallel", nargs="?", default=0, type=int)
# Skip iteration that has existing meta-testing result.
parser.add_argument("--skip-exist", action='store_true', default=True)
# Merge all meta-testing result to meta-test.csv
parser.add_argument("--merge", action='store_true', default=True)
# Skip some iterations.
# e.g. stride=3 sample 1 iteration every 3 iterations.
parser.add_argument("--stride", default=1, type=int)
args = parser.parse_args()
meta_train_dirs = args.folder
workers = args.parallel
adapt_rollout_per_task = args.adapt_rollouts
test_rollout_per_task = args.test_rollouts
max_path_length = args.max_path_length
skip_existing = args.skip_exist
to_merge = args.merge
stride = args.stride
helper = cls(meta_task_cls=env_cls,
max_path_length=max_path_length,
adapt_rollout_per_task=adapt_rollout_per_task,
test_rollout_per_task=test_rollout_per_task)
helper.test_many_folders(folders=meta_train_dirs,
workers=workers,
skip_existing=skip_existing,
to_merge=to_merge,
stride=stride)
def __enter__(self):
tabular_log_file = os.path.join(self.log_dir, 'progress.csv')
text_log_file = os.path.join(self.log_dir, 'debug.log')
logger.add_output(dowel.TextOutput(text_log_file))
logger.add_output(dowel.CsvOutput(tabular_log_file))
logger.add_output(dowel.TensorBoardOutput(self.log_dir))
logger.add_output(dowel.StdOutput())
logger.push_prefix('[%s] ' % self.exp_name)
return self
def _make_context(cls, options, **kwargs):
"""Make a context from the template information and variant args.
Currently, all arguments should be keyword arguments.
Args:
options (dict): Options to `wrap_experiment` itself. See the
function documentation for details.
kwargs (dict): Keyword arguments for the wrapped function. Will be
logged to `variant.json`
Returns:
ExperimentContext: The created experiment context.
"""
name = options['name']
if name is None:
name = options['function'].__name__
name = cls._augment_name(options, name, kwargs)
log_dir = options['log_dir']
if log_dir is None:
log_dir = ('{data}/local/{prefix}/{name}'.format(
data=os.path.join(os.getcwd(), 'data'),
prefix=options['prefix'],
name=name))
if options['use_existing_dir']:
os.makedirs(log_dir, exist_ok=True)
else:
log_dir = _make_sequential_log_dir(log_dir)
tabular_log_file = os.path.join(log_dir, 'progress.csv')
text_log_file = os.path.join(log_dir, 'debug.log')
variant_log_file = os.path.join(log_dir, 'variant.json')
metadata_log_file = os.path.join(log_dir, 'metadata.json')
dump_json(variant_log_file, kwargs)
git_root_path, metadata = get_metadata()
dump_json(metadata_log_file, metadata)
if git_root_path and options['archive_launch_repo']:
make_launcher_archive(git_root_path=git_root_path, log_dir=log_dir)
logger.add_output(dowel.TextOutput(text_log_file))
logger.add_output(dowel.CsvOutput(tabular_log_file))
logger.add_output(
dowel.TensorBoardOutput(log_dir, x_axis='TotalEnvSteps'))
logger.add_output(dowel.StdOutput())
logger.push_prefix('[{}] '.format(name))
logger.log('Logging to {}'.format(log_dir))
return ExperimentContext(snapshot_dir=log_dir,
snapshot_mode=options['snapshot_mode'],
snapshot_gap=options['snapshot_gap'])
def restore_training(log_dir, exp_name, args, env_saved=True, env=None):
tabular_log_file = os.path.join(
log_dir, 'progress_restored.{}.{}.csv'.format(
str(time.time())[:10], socket.gethostname()))
text_log_file = os.path.join(
log_dir, 'debug_restored.{}.{}.log'.format(
str(time.time())[:10], socket.gethostname()))
logger.add_output(dowel.TextOutput(text_log_file))
logger.add_output(dowel.CsvOutput(tabular_log_file))
logger.add_output(dowel.TensorBoardOutput(log_dir))
logger.add_output(dowel.StdOutput())
logger.push_prefix('[%s] ' % exp_name)
ctxt = ExperimentContext(snapshot_dir=log_dir,
snapshot_mode='last',
snapshot_gap=1)
runner = LocalRunnerWrapper(ctxt,
eval=args.eval_during_training,
n_eval_episodes=args.n_eval_episodes,
eval_greedy=args.eval_greedy,
eval_epoch_freq=args.eval_epoch_freq,
save_env=env_saved)
saved = runner._snapshotter.load(log_dir, 'last')
runner._setup_args = saved['setup_args']
runner._train_args = saved['train_args']
runner._stats = saved['stats']
set_seed(runner._setup_args.seed)
algo = saved['algo']
# Compatibility patch
if not hasattr(algo, '_clip_grad_norm'):
setattr(algo, '_clip_grad_norm', args.clip_grad_norm)
if env_saved:
env = saved['env']
runner.setup(env=env,
algo=algo,
sampler_cls=runner._setup_args.sampler_cls,
sampler_args=runner._setup_args.sampler_args)
runner._train_args.start_epoch = runner._stats.total_epoch + 1
runner._train_args.n_epochs = runner._train_args.start_epoch + args.n_epochs
print('\nRestored checkpoint from epoch #{}...'.format(
runner._train_args.start_epoch))
print('To be trained for additional {} epochs...'.format(args.n_epochs))
print('Will be finished at epoch #{}...\n'.format(
runner._train_args.n_epochs))
return runner._algo.train(runner)
def ppo_cmb(env, seed, log_dir):
"""Create test continuous mlp baseline on ppo.
Args:
env (gym_env): Environment of the task.
seed (int): Random seed for the trial.
log_dir (str): Log dir path.
Returns:
str: training results in csv format.
"""
deterministic.set_seed(seed)
config = tf.compat.v1.ConfigProto(allow_soft_placement=True,
intra_op_parallelism_threads=num_proc,
inter_op_parallelism_threads=num_proc)
sess = tf.compat.v1.Session(config=config)
with LocalTFRunner(snapshot_config, sess=sess,
max_cpus=num_proc) as runner:
env = TfEnv(normalize(env))
policy = GaussianLSTMPolicy(
env_spec=env.spec,
hidden_dim=policy_params['policy_hidden_sizes'],
hidden_nonlinearity=policy_params['hidden_nonlinearity'],
)
baseline = ContinuousMLPBaseline(
env_spec=env.spec,
regressor_args=baseline_params['regressor_args'],
)
algo = PPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=algo_params['max_path_length'],
discount=algo_params['discount'],
gae_lambda=algo_params['gae_lambda'],
lr_clip_range=algo_params['lr_clip_range'],
entropy_method=algo_params['entropy_method'],
policy_ent_coeff=algo_params['policy_ent_coeff'],
optimizer_args=algo_params['optimizer_args'],
center_adv=algo_params['center_adv'],
stop_entropy_gradient=True)
# Set up logger since we are not using run_experiment
tabular_log_file = osp.join(log_dir, 'progress.csv')
dowel_logger.add_output(dowel.StdOutput())
dowel_logger.add_output(dowel.CsvOutput(tabular_log_file))
dowel_logger.add_output(dowel.TensorBoardOutput(log_dir))
runner.setup(algo,
env,
sampler_args=dict(n_envs=algo_params['n_envs']))
runner.train(n_epochs=algo_params['n_epochs'],
batch_size=algo_params['n_rollout_steps'])
dowel_logger.remove_all()
return tabular_log_file
def _make_context(self, *args, **kwargs):
"""Make a context from the template information and variant args.
Currently, all arguments should be keyword arguments.
Args:
args (list): Should be empty.
kwargs (dict): Keyword arguments for the wrapped function. Will be
logged to `variant.json`
Returns:
ExperimentContext: The created experiment context.
Raises:
ValueError: If args is not empty.
"""
if args:
raise ValueError('metarl.experiment currently only supports '
'keyword arguments')
log_dir = self.log_dir
if log_dir is None:
name = self.name
if name is None:
name = self.function.__name__
self.name = self.function.__name__
log_dir = ('{data}/local/{prefix}/{name}/{time}'.format(
data=osp.join(os.getcwd(), 'data'),
prefix=self.prefix,
name=name,
time=timestamp))
log_dir = _make_sequential_log_dir(log_dir)
tabular_log_file = os.path.join(log_dir, 'progress.csv')
text_log_file = os.path.join(log_dir, 'debug.log')
variant_log_file = os.path.join(log_dir, 'variant.json')
dump_json(variant_log_file, kwargs)
logger.add_output(dowel.TextOutput(text_log_file))
logger.add_output(dowel.CsvOutput(tabular_log_file))
logger.add_output(
dowel.TensorBoardOutput(log_dir, x_axis='TotalEnvSteps'))
logger.add_output(dowel.StdOutput())
logger.push_prefix('[%s] ' % self.name)
return ExperimentContext(snapshot_dir=log_dir,
snapshot_mode=self.snapshot_mode,
snapshot_gap=self.snapshot_gap)
def run_metarl(env, seed, log_dir):
'''
Create metarl model and training.
Replace the ddpg with the algorithm you want to run.
:param env: Environment of the task.
:param seed: Random seed for the trial.
:param log_dir: Log dir path.
:return:
'''
deterministic.set_seed(seed)
with LocalTFRunner(snapshot_config) as runner:
env = TfEnv(normalize(env))
# Set up params for ddpg
action_noise = OUStrategy(env.spec, sigma=params['sigma'])
policy = ContinuousMLPPolicy(
env_spec=env.spec,
hidden_sizes=params['policy_hidden_sizes'],
hidden_nonlinearity=tf.nn.relu,
output_nonlinearity=tf.nn.tanh)
qf = ContinuousMLPQFunction(env_spec=env.spec,
hidden_sizes=params['qf_hidden_sizes'],
hidden_nonlinearity=tf.nn.relu)
replay_buffer = SimpleReplayBuffer(
env_spec=env.spec,
size_in_transitions=params['replay_buffer_size'],
time_horizon=params['n_rollout_steps'])
ddpg = DDPG(env_spec=env.spec,
policy=policy,
qf=qf,
replay_buffer=replay_buffer,
steps_per_epoch=params['steps_per_epoch'],
policy_lr=params['policy_lr'],
qf_lr=params['qf_lr'],
target_update_tau=params['tau'],
n_train_steps=params['n_train_steps'],
discount=params['discount'],
min_buffer_size=int(1e4),
exploration_strategy=action_noise,
policy_optimizer=tf.train.AdamOptimizer,
qf_optimizer=tf.train.AdamOptimizer)
# Set up logger since we are not using run_experiment
tabular_log_file = osp.join(log_dir, 'progress.csv')
tensorboard_log_dir = osp.join(log_dir)
dowel_logger.add_output(dowel.StdOutput())
dowel_logger.add_output(dowel.CsvOutput(tabular_log_file))
dowel_logger.add_output(dowel.TensorBoardOutput(tensorboard_log_dir))
runner.setup(ddpg, env)
runner.train(n_epochs=params['n_epochs'],
batch_size=params['n_rollout_steps'])
dowel_logger.remove_all()
return tabular_log_file
def run_garage_pytorch(env, seed, log_dir):
"""Create garage PyTorch VPG model and training.
Args:
env (dict): Environment of the task.
seed (int): Random positive integer for the trial.
log_dir (str): Log dir path.
Returns:
str: Path to output csv file
"""
env = TfEnv(normalize(env))
deterministic.set_seed(seed)
runner = LocalRunner(snapshot_config)
policy = PyTorch_GMP(env.spec,
hidden_sizes=hyper_parameters['hidden_sizes'],
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
value_function = GaussianMLPValueFunction(env_spec=env.spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
policy_optimizer = OptimizerWrapper((torch.optim.Adam, dict(lr=2.5e-4)),
policy,
max_optimization_epochs=10,
minibatch_size=64)
vf_optimizer = OptimizerWrapper((torch.optim.Adam, dict(lr=2.5e-4)),
value_function,
max_optimization_epochs=10,
minibatch_size=64)
algo = PyTorch_VPG(env_spec=env.spec,
policy=policy,
value_function=value_function,
policy_optimizer=policy_optimizer,
vf_optimizer=vf_optimizer,
max_path_length=hyper_parameters['max_path_length'],
discount=hyper_parameters['discount'],
center_adv=hyper_parameters['center_adv'])
# Set up logger since we are not using run_experiment
tabular_log_file = osp.join(log_dir, 'progress.csv')
dowel_logger.add_output(dowel.StdOutput())
dowel_logger.add_output(dowel.CsvOutput(tabular_log_file))
dowel_logger.add_output(dowel.TensorBoardOutput(log_dir))
runner.setup(algo, env)
runner.train(n_epochs=hyper_parameters['n_epochs'],
batch_size=hyper_parameters['batch_size'])
dowel_logger.remove_all()
return tabular_log_file
def test_meta_evaluator_n_traj():
set_seed(100)
tasks = SetTaskSampler(PointEnv)
max_path_length = 200
env = MetaRLEnv(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)
def run_metarl(env, seed, log_dir):
'''
Create metarl model and training.
Replace the ddpg with the algorithm you want to run.
:param env: Environment of the task.
:param seed: Random seed for the trial.
:param log_dir: Log dir path.
:return:
'''
deterministic.set_seed(seed)
runner = LocalRunner(snapshot_config)
# Set up params for ddpg
policy = TanhGaussianMLPPolicy2(env_spec=env.spec,
hidden_sizes=params['policy_hidden_sizes'],
hidden_nonlinearity=nn.ReLU,
output_nonlinearity=None)
qf1 = ContinuousMLPQFunction(env_spec=env.spec,
hidden_sizes=params['qf_hidden_sizes'],
hidden_nonlinearity=F.relu)
qf2 = ContinuousMLPQFunction(env_spec=env.spec,
hidden_sizes=params['qf_hidden_sizes'],
hidden_nonlinearity=F.relu)
replay_buffer = SACReplayBuffer(env_spec=env.spec,
max_size=params['replay_buffer_size'])
sampler_args = {
'agent': policy,
'max_path_length': 1000,
}
sac = SAC(env_spec=env.spec,
policy=policy,
qf1=qf1,
qf2=qf2,
gradient_steps_per_itr=params['gradient_steps_per_itr'],
replay_buffer=replay_buffer,
buffer_batch_size=params['buffer_batch_size'])
# Set up logger since we are not using run_experiment
tabular_log_file = osp.join(log_dir, 'progress.csv')
tensorboard_log_dir = osp.join(log_dir)
dowel_logger.add_output(dowel.StdOutput())
dowel_logger.add_output(dowel.CsvOutput(tabular_log_file))
dowel_logger.add_output(dowel.TensorBoardOutput(tensorboard_log_dir))
runner.setup(algo=sac,
env=env,
sampler_cls=SimpleSampler,
sampler_args=sampler_args)
runner.train(n_epochs=params['n_epochs'],
batch_size=params['gradient_steps_per_itr'])
dowel_logger.remove_all()
return tabular_log_file
def run_garage(env, seed, log_dir):
'''
Create garage model and training.
Replace the trpo with the algorithm you want to run.
:param env: Environment of the task.
:param seed: Random seed for the trial.
:param log_dir: Log dir path.
:return:import baselines.common.tf_util as U
'''
deterministic.set_seed(seed)
with LocalRunner() as runner:
env = TfEnv(normalize(env))
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None,
)
baseline = GaussianMLPBaseline(
env_spec=env.spec,
regressor_args=dict(
hidden_sizes=(32, 32),
use_trust_region=True,
),
)
algo = TRPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
gae_lambda=0.98,
max_kl_step=0.01,
policy_ent_coeff=0.0,
plot=False,
)
# Set up logger since we are not using run_experiment
tabular_log_file = osp.join(log_dir, 'progress.csv')
dowel_logger.add_output(dowel.CsvOutput(tabular_log_file))
dowel_logger.add_output(dowel.StdOutput())
dowel_logger.add_output(dowel.TensorBoardOutput(log_dir))
runner.setup(algo, env)
runner.train(n_epochs=976, batch_size=1024)
dowel_logger.remove_all()
return tabular_log_file
def run_garage(env, seed, log_dir):
"""Create garage model and training.
Replace the ppo with the algorithm you want to run.
Args:
env (gym.Env): Environment of the task.
seed (int): Random seed for the trial.
log_dir (str): Log dir path.
Returns:
str: Path to output csv file
"""
deterministic.set_seed(seed)
config = tf.compat.v1.ConfigProto(allow_soft_placement=True,
intra_op_parallelism_threads=12,
inter_op_parallelism_threads=12)
sess = tf.compat.v1.Session(config=config)
with LocalTFRunner(snapshot_config, sess=sess, max_cpus=12) as runner:
env = TfEnv(normalize(env))
policy = CategoricalGRUPolicy(
env_spec=env.spec,
hidden_dim=32,
hidden_nonlinearity=tf.nn.tanh,
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = PPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
policy_ent_coeff=0.0,
optimizer_args=dict(
batch_size=32,
max_epochs=10,
tf_optimizer_args=dict(learning_rate=1e-3),
),
)
# Set up logger since we are not using run_experiment
tabular_log_file = osp.join(log_dir, 'progress.csv')
dowel_logger.add_output(dowel.StdOutput())
dowel_logger.add_output(dowel.CsvOutput(tabular_log_file))
dowel_logger.add_output(dowel.TensorBoardOutput(log_dir))
runner.setup(algo, env, sampler_args=dict(n_envs=12))
runner.train(n_epochs=488, batch_size=2048)
dowel_logger.remove_all()
return tabular_log_file
def run_garage_tf(env, seed, log_dir):
"""Create garage TensorFlow PPO model and training.
Args:
env (dict): Environment of the task.
seed (int): Random positive integer for the trial.
log_dir (str): Log dir path.
Returns:
str: Path to output csv file
"""
deterministic.set_seed(seed)
with LocalTFRunner(snapshot_config) as runner:
env = TfEnv(normalize(env))
policy = TF_GMP(
env_spec=env.spec,
hidden_sizes=hyper_parameters['hidden_sizes'],
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None,
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TF_PPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=hyper_parameters['max_path_length'],
discount=hyper_parameters['discount'],
gae_lambda=hyper_parameters['gae_lambda'],
center_adv=hyper_parameters['center_adv'],
lr_clip_range=hyper_parameters['lr_clip_range'],
optimizer_args=dict(
batch_size=None,
max_epochs=1,
tf_optimizer_args=dict(
learning_rate=hyper_parameters['learning_rate']),
verbose=True)) # yapf: disable
# Set up logger since we are not using run_experiment
tabular_log_file = osp.join(log_dir, 'progress.csv')
dowel_logger.add_output(dowel.StdOutput())
dowel_logger.add_output(dowel.CsvOutput(tabular_log_file))
dowel_logger.add_output(dowel.TensorBoardOutput(log_dir))
runner.setup(algo, env)
runner.train(n_epochs=hyper_parameters['n_epochs'],
batch_size=hyper_parameters['batch_size'])
dowel_logger.remove_all()
return tabular_log_file
def run_metarl(env, seed, log_dir):
"""Create metarl PyTorch MAML model and training.
Args:
env (MetaRLEnv): Environment of the task.
seed (int): Random positive integer for the trial.
log_dir (str): Log dir path.
Returns:
str: Path to output csv file
"""
deterministic.set_seed(seed)
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=hyper_parameters['hidden_sizes'],
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None,
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = MAMLTRPO(env=env,
policy=policy,
baseline=baseline,
max_path_length=hyper_parameters['max_path_length'],
discount=hyper_parameters['discount'],
gae_lambda=hyper_parameters['gae_lambda'],
meta_batch_size=hyper_parameters['meta_batch_size'],
inner_lr=hyper_parameters['inner_lr'],
max_kl_step=hyper_parameters['max_kl'],
num_grad_updates=hyper_parameters['num_grad_update'])
# Set up logger since we are not using run_experiment
tabular_log_file = osp.join(log_dir, 'progress.csv')
dowel_logger.add_output(dowel.StdOutput())
dowel_logger.add_output(dowel.CsvOutput(tabular_log_file))
dowel_logger.add_output(dowel.TensorBoardOutput(log_dir))
snapshot_config = SnapshotConfig(snapshot_dir=log_dir,
snapshot_mode='all',
snapshot_gap=1)
runner = LocalRunner(snapshot_config=snapshot_config)
runner.setup(algo, env, sampler_args=dict(n_envs=5))
runner.train(n_epochs=hyper_parameters['n_epochs'],
batch_size=(hyper_parameters['fast_batch_size'] *
hyper_parameters['max_path_length']))
dowel_logger.remove_all()
return tabular_log_file
def run_garage_pytorch(env, seed, log_dir):
"""Create garage PyTorch PPO model and training.
Args:
env (dict): Environment of the task.
seed (int): Random positive integer for the trial.
log_dir (str): Log dir path.
Returns:
str: Path to output csv file
"""
env = TfEnv(normalize(env))
deterministic.set_seed(seed)
runner = LocalRunner(snapshot_config)
policy = PyTorch_GMP(env.spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
value_functions = LinearFeatureBaseline(env_spec=env.spec)
algo = PyTorch_PPO(env_spec=env.spec,
policy=policy,
value_function=value_functions,
optimizer=torch.optim.Adam,
policy_lr=3e-4,
max_path_length=hyper_parameters['max_path_length'],
discount=0.99,
gae_lambda=0.95,
center_adv=True,
lr_clip_range=0.2,
minibatch_size=128,
max_optimization_epochs=10)
# Set up logger since we are not using run_experiment
tabular_log_file = osp.join(log_dir, 'progress.csv')
dowel_logger.add_output(dowel.StdOutput())
dowel_logger.add_output(dowel.CsvOutput(tabular_log_file))
dowel_logger.add_output(dowel.TensorBoardOutput(log_dir))
runner.setup(algo, env)
runner.train(n_epochs=hyper_parameters['n_epochs'],
batch_size=hyper_parameters['batch_size'])
dowel_logger.remove_all()
return tabular_log_file
def run_garage_pytorch(env, seed, log_dir):
"""Create garage PyTorch PPO model and training.
Args:
env (dict): Environment of the task.
seed (int): Random positive integer for the trial.
log_dir (str): Log dir path.
Returns:
str: Path to output csv file
"""
env = TfEnv(normalize(env))
deterministic.set_seed(seed)
runner = LocalRunner(snapshot_config)
policy = PyTorch_GMP(env.spec,
hidden_sizes=hyper_parameters['hidden_sizes'],
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = PyTorch_TRPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=hyper_parameters['max_path_length'],
discount=hyper_parameters['discount'],
gae_lambda=hyper_parameters['gae_lambda'],
max_kl=hyper_parameters['max_kl'],
)
# Set up logger since we are not using run_experiment
tabular_log_file = osp.join(log_dir, 'progress.csv')
dowel_logger.add_output(dowel.StdOutput())
dowel_logger.add_output(dowel.CsvOutput(tabular_log_file))
dowel_logger.add_output(dowel.TensorBoardOutput(log_dir))
runner.setup(algo, env)
runner.train(n_epochs=hyper_parameters['n_epochs'],
batch_size=hyper_parameters['batch_size'])
dowel_logger.remove_all()
return tabular_log_file
