def main(_):
tf.config.experimental_run_functions_eagerly(FLAGS.eager)
tf.random.set_seed(FLAGS.seed)
np.random.seed(FLAGS.seed)
random.seed(FLAGS.seed)
FLAGS.set_default('max_timesteps', FLAGS.max_timesteps // FLAGS.action_repeat)
if 'pixels-dm' in FLAGS.env_name:
if 'distractor' in FLAGS.env_name:
_, _, domain_name, _, _ = FLAGS.env_name.split('-')
else:
_, _, domain_name, _ = FLAGS.env_name.split('-')
if domain_name in ['cartpole']:
FLAGS.set_default('action_repeat', 8)
elif domain_name in ['reacher', 'cheetah', 'ball_in_cup', 'hopper']:
FLAGS.set_default('action_repeat', 4)
elif domain_name in ['finger', 'walker']:
FLAGS.set_default('action_repeat', 2)
print('Loading env')
env, _ = utils.load_env(FLAGS.env_name, FLAGS.seed, FLAGS.action_repeat,
FLAGS.frame_stack)
eval_env, _ = utils.load_env(FLAGS.env_name, FLAGS.seed,
FLAGS.action_repeat, FLAGS.frame_stack)
print('Env loaded')
else:
raise Exception('Unsupported env')
is_image_obs = (isinstance(env.observation_spec(), TensorSpec) and
len(env.observation_spec().shape) == 3)
spec = (
env.observation_spec(),
env.action_spec(),
env.reward_spec(),
env.reward_spec(), # discount spec
env.observation_spec() # next observation spec
)
print('Init replay')
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
spec, batch_size=1, max_length=FLAGS.max_length_replay_buffer)
print('Replay created')
@tf.function
def add_to_replay(state, action, reward, discount, next_states):
replay_buffer.add_batch((state, action, reward, discount, next_states))
hparam_str = utils.make_hparam_string(
FLAGS.xm_parameters, seed=FLAGS.seed, env_name=FLAGS.env_name,
algo_name=FLAGS.algo_name)
summary_writer = tf.summary.create_file_writer(
os.path.join(FLAGS.save_dir, 'tb', hparam_str))
results_writer = tf.summary.create_file_writer(
os.path.join(FLAGS.save_dir, 'results', hparam_str))
print('Init actor')
if 'ddpg' in FLAGS.algo_name:
model = ddpg.DDPG(
env.observation_spec(),
env.action_spec(),
cross_norm='crossnorm' in FLAGS.algo_name)
elif 'crr' in FLAGS.algo_name:
model = awr.AWR(
env.observation_spec(),
env.action_spec(), f='bin_max')
elif 'awr' in FLAGS.algo_name:
model = awr.AWR(
env.observation_spec(),
env.action_spec(), f='exp_mean')
elif 'sac_v1' in FLAGS.algo_name:
model = sac_v1.SAC(
env.observation_spec(),
env.action_spec(),
target_entropy=-env.action_spec().shape[0])
elif 'asac' in FLAGS.algo_name:
model = asac.ASAC(
env.observation_spec(),
env.action_spec(),
target_entropy=-env.action_spec().shape[0])
elif 'sac' in FLAGS.algo_name:
model = sac.SAC(
env.observation_spec(),
env.action_spec(),
target_entropy=-env.action_spec().shape[0],
cross_norm='crossnorm' in FLAGS.algo_name,
pcl_actor_update='pc' in FLAGS.algo_name)
elif 'pcl' in FLAGS.algo_name:
model = pcl.PCL(
env.observation_spec(),
env.action_spec(),
target_entropy=-env.action_spec().shape[0])
print('Init random policy for warmup')
initial_collect_policy = random_tf_policy.RandomTFPolicy(
env.time_step_spec(), env.action_spec())
print('Init replay buffer')
dataset = replay_buffer.as_dataset(
num_parallel_calls=tf.data.AUTOTUNE,
sample_batch_size=FLAGS.sample_batch_size)
if is_image_obs:
dataset = dataset.map(image_aug,
num_parallel_calls=tf.data.AUTOTUNE,
deterministic=False).prefetch(3)
else:
dataset = dataset.prefetch(3)
def repack(*data):
return data[0]
dataset = dataset.map(repack)
replay_buffer_iter = iter(dataset)
previous_time = time.time()
timestep = env.reset()
episode_return = 0
episode_timesteps = 0
step_mult = 1 if FLAGS.action_repeat < 1 else FLAGS.action_repeat
print('Starting training')
for i in tqdm.tqdm(range(FLAGS.max_timesteps)):
if i % FLAGS.deployment_batch_size == 0:
for _ in range(FLAGS.deployment_batch_size):
last_timestep = timestep.is_last()
if last_timestep:
if episode_timesteps > 0:
current_time = time.time()
with summary_writer.as_default():
tf.summary.scalar(
'train/returns',
episode_return,
step=(i + 1) * step_mult)
tf.summary.scalar(
'train/FPS',
episode_timesteps / (current_time - previous_time),
step=(i + 1) * step_mult)
timestep = env.reset()
episode_return = 0
episode_timesteps = 0
previous_time = time.time()
if (replay_buffer.num_frames() < FLAGS.num_random_actions or
replay_buffer.num_frames() < FLAGS.deployment_batch_size):
# Use policy only after the first deployment.
policy_step = initial_collect_policy.action(timestep)
action = policy_step.action
else:
action = model.actor(timestep.observation, sample=True)
next_timestep = env.step(action)
add_to_replay(timestep.observation, action, next_timestep.reward,
next_timestep.discount, next_timestep.observation)
episode_return += next_timestep.reward[0]
episode_timesteps += 1
timestep = next_timestep
if i + 1 >= FLAGS.start_training_timesteps:
with summary_writer.as_default():
info_dict = model.update_step(replay_buffer_iter)
if (i + 1) % FLAGS.log_interval == 0:
with summary_writer.as_default():
for k, v in info_dict.items():
tf.summary.scalar(f'training/{k}', v, step=(i + 1) * step_mult)
if (i + 1) % FLAGS.eval_interval == 0:
logging.info('Performing policy eval.')
average_returns, evaluation_timesteps = evaluation.evaluate(
eval_env, model)
with results_writer.as_default():
tf.summary.scalar(
'evaluation/returns', average_returns, step=(i + 1) * step_mult)
tf.summary.scalar(
'evaluation/length', evaluation_timesteps, step=(i+1) * step_mult)
logging.info('Eval at %d: ave returns=%f, ave episode length=%f',
(i + 1) * step_mult, average_returns, evaluation_timesteps)
if ((i + 1) * step_mult) % 50_000 == 0:
model.save_weights(
os.path.join(FLAGS.save_dir, 'results', FLAGS.env_name + '__' + str(
(i + 1) * step_mult)))
def main(_):
tf.config.experimental_run_functions_eagerly(FLAGS.eager)
print('Num GPUs Available: ', len(tf.config.list_physical_devices('GPU')))
if FLAGS.env_name.startswith('procgen'):
print('Test env: %s' % FLAGS.env_name)
_, env_name, train_levels, _ = FLAGS.env_name.split('-')
print('Train env: %s' % FLAGS.env_name)
env = tf_py_environment.TFPyEnvironment(
procgen_wrappers.TFAgentsParallelProcGenEnv(
1,
normalize_rewards=False, # no normalization for evaluation
env_name=env_name,
num_levels=int(train_levels),
start_level=0))
env_all = tf_py_environment.TFPyEnvironment(
procgen_wrappers.TFAgentsParallelProcGenEnv(
1,
normalize_rewards=False, # no normalization for evaluation
env_name=env_name,
num_levels=0,
start_level=0))
if int(train_levels) == 0:
train_levels = '200'
elif FLAGS.env_name.startswith('pixels-dm'):
if 'distractor' in FLAGS.env_name:
_, _, domain_name, _, _ = FLAGS.env_name.split('-')
else:
_, _, domain_name, _ = FLAGS.env_name.split('-')
if domain_name in ['cartpole']:
FLAGS.set_default('action_repeat', 8)
elif domain_name in ['reacher', 'cheetah', 'ball_in_cup', 'hopper']:
FLAGS.set_default('action_repeat', 4)
elif domain_name in ['finger', 'walker']:
FLAGS.set_default('action_repeat', 2)
env, _ = utils.load_env(FLAGS.env_name, FLAGS.seed,
FLAGS.action_repeat, FLAGS.frame_stack,
FLAGS.obs_type)
if FLAGS.obs_type == 'pixels':
env, _ = utils.load_env(FLAGS.env_name, FLAGS.seed,
FLAGS.action_repeat, FLAGS.frame_stack,
FLAGS.obs_type)
else:
_, env = utils.load_env(FLAGS.env_name, FLAGS.seed,
FLAGS.action_repeat, FLAGS.frame_stack,
FLAGS.obs_type)
if FLAGS.obs_type != 'state':
if FLAGS.env_name.startswith('procgen'):
bcq = bcq_pixel
cql = cql_pixel
fisher_brac = fisher_brac_pixel
deepmdp = deepmdp_pixel
vpn = vpn_pixel
cssc = cssc_pixel
pse = pse_pixel
else:
bcq = bcq_state
cql = cql_state
print('Loading dataset')
# Use load_tfrecord_dataset_sequence to load transitions of size k>=2.
if FLAGS.numpy_dataset:
n_shards = 10
def shard_fn(shard):
return ('experiments/'
'20210617_0105.dataset_dmc_50k,100k,'
'200k_SAC_pixel_numpy/datasets/'
'%s__%d__%d__%d.npy' %
(FLAGS.env_name, FLAGS.ckpt_timesteps, FLAGS.max_timesteps,
shard))
np_observer = tf_utils.NumpyObserver(shard_fn, env)
dataset = np_observer.load(n_shards)
else:
if FLAGS.env_name.startswith('procgen'):
if FLAGS.n_step_returns > 0:
if FLAGS.max_timesteps == 100_000:
dataset_path = ('experiments/'
'20210624_2033.dataset_procgen__ppo_pixel/'
'datasets/%s__%d__%d.tfrecord' %
(FLAGS.env_name, FLAGS.ckpt_timesteps,
FLAGS.max_timesteps))
elif FLAGS.max_timesteps == 3_000_000:
if int(train_levels) == 1:
print('Using dataset with 1 level')
dataset_path = (
'experiments/'
'20210713_1557.dataset_procgen__ppo_pixel_1_level/'
'datasets/%s__%d__%d.tfrecord' %
(FLAGS.env_name, FLAGS.ckpt_timesteps,
FLAGS.max_timesteps))
elif int(train_levels) == 200:
print('Using dataset with 200 levels')
# Mixture dataset between 10M,15M,20M and 25M in equal amounts
# dataset_path = 'experiments/
# 20210718_1522.dataset_procgen__ppo_pixel_mixture10,15,20,25M/
# datasets/%s__%d__%d.tfrecord'%(FLAGS.env_name,
# FLAGS.ckpt_timesteps,FLAGS.max_timesteps)
# PPO after 25M steps
dataset_path = (
'experiments/'
'20210702_2234.dataset_procgen__ppo_pixel/'
'datasets/%s__%d__%d.tfrecord' %
(FLAGS.env_name, FLAGS.ckpt_timesteps,
FLAGS.max_timesteps))
elif FLAGS.max_timesteps == 5_000_000:
# epsilon-greedy, eps: 0.1->0.001
dataset_path = (
'experiments/'
'20210805_1958.dataset_procgen__ppo_pixel_'
'egreedy_levelIDs/datasets/'
'%s__%d__%d.tfrecord*' %
(FLAGS.env_name, FLAGS.ckpt_timesteps, 100000))
# Pure greedy (epsilon=0)
# dataset_path = ('experiments/'
# '20210820_1348.dataset_procgen__ppo_pixel_'
# 'egreedy_levelIDs/datasets/'
# '%s__%d__%d.tfrecord*' %
# (FLAGS.env_name, FLAGS.ckpt_timesteps, 100000))
elif FLAGS.env_name.startswith('pixels-dm'):
if 'distractor' in FLAGS.env_name:
dataset_path = (
'experiments/'
'20210623_1749.dataset_dmc__sac_pixel/datasets/'
'%s__%d__%d.tfrecord' %
(FLAGS.env_name, FLAGS.ckpt_timesteps,
FLAGS.max_timesteps))
else:
if FLAGS.obs_type == 'pixels':
dataset_path = (
'experiments/'
'20210612_1644.dataset_dmc_50k,100k,200k_SAC_pixel/'
'datasets/%s__%d__%d.tfrecord' %
(FLAGS.env_name, FLAGS.ckpt_timesteps,
FLAGS.max_timesteps))
else:
dataset_path = (
'experiments/'
'20210621_1436.dataset_dmc__SAC_pixel/datasets/'
'%s__%d__%d.tfrecord' %
(FLAGS.env_name, FLAGS.ckpt_timesteps,
FLAGS.max_timesteps))
shards = tf.io.gfile.glob(dataset_path)
shards = [s for s in shards if not s.endswith('.spec')]
print('Found %d shards under path %s' % (len(shards), dataset_path))
if FLAGS.n_step_returns > 1:
# Load sequences of length N
dataset = load_tfrecord_dataset_sequence(
shards,
buffer_size_per_shard=FLAGS.dataset_size // len(shards),
deterministic=False,
compress_image=True,
seq_len=FLAGS.n_step_returns) # spec=data_spec,
dataset = dataset.take(FLAGS.dataset_size).shuffle(
buffer_size=FLAGS.batch_size,
reshuffle_each_iteration=False).batch(
FLAGS.batch_size,
drop_remainder=True).prefetch(1).repeat()
dataset_iter = iter(dataset)
else:
dataset_iter = tf_utils.create_data_iterator(
('experiments/20210805'
'_1958.dataset_procgen__ppo_pixel_egreedy_'
'levelIDs/datasets/%s__%d__%d.tfrecord.shard-*-of-*' %
(FLAGS.env_name, FLAGS.ckpt_timesteps, 100000)),
FLAGS.batch_size,
shuffle_buffer_size=FLAGS.batch_size,
obs_to_float=False)
tf.random.set_seed(FLAGS.seed)
hparam_str = utils.make_hparam_string(
FLAGS.xm_parameters,
algo_name=FLAGS.algo_name,
seed=FLAGS.seed,
task_name=FLAGS.env_name,
ckpt_timesteps=FLAGS.ckpt_timesteps,
rep_learn_keywords=FLAGS.rep_learn_keywords)
summary_writer = tf.summary.create_file_writer(
os.path.join(FLAGS.save_dir, 'tb', hparam_str))
result_writer = tf.summary.create_file_writer(
os.path.join(FLAGS.save_dir, 'results', hparam_str))
pretrain = (FLAGS.pretrain > 0)
if FLAGS.env_name.startswith('procgen'):
# disable entropy reg for discrete spaces
action_dim = env.action_spec().maximum.item() + 1
else:
action_dim = env.action_spec().shape[0]
if 'cql' in FLAGS.algo_name:
model = cql.CQL(env.observation_spec(),
env.action_spec(),
reg=FLAGS.f_reg,
target_entropy=-action_dim,
num_augmentations=FLAGS.num_data_augs,
rep_learn_keywords=FLAGS.rep_learn_keywords,
batch_size=FLAGS.batch_size)
elif 'bcq' in FLAGS.algo_name:
model = bcq.BCQ(env.observation_spec(),
env.action_spec(),
num_augmentations=FLAGS.num_data_augs)
elif 'fbrac' in FLAGS.algo_name:
model = fisher_brac.FBRAC(env.observation_spec(),
env.action_spec(),
target_entropy=-action_dim,
f_reg=FLAGS.f_reg,
reward_bonus=FLAGS.reward_bonus,
num_augmentations=FLAGS.num_data_augs,
env_name=FLAGS.env_name,
batch_size=FLAGS.batch_size)
elif 'ours' in FLAGS.algo_name:
model = ours.OURS(env.observation_spec(),
env.action_spec(),
target_entropy=-action_dim,
f_reg=FLAGS.f_reg,
reward_bonus=FLAGS.reward_bonus,
num_augmentations=FLAGS.num_data_augs,
env_name=FLAGS.env_name,
rep_learn_keywords=FLAGS.rep_learn_keywords,
batch_size=FLAGS.batch_size,
n_quantiles=FLAGS.n_quantiles,
temp=FLAGS.temp,
num_training_levels=train_levels)
bc_pretraining_steps = FLAGS.pretrain
if pretrain:
model_save_path = os.path.join(FLAGS.save_dir, 'weights',
hparam_str)
checkpoint = tf.train.Checkpoint(**model.model_dict)
tf_step_counter = tf.Variable(0, dtype=tf.int32)
manager = tf.train.CheckpointManager(
checkpoint,
directory=model_save_path,
max_to_keep=1,
checkpoint_interval=FLAGS.save_interval,
step_counter=tf_step_counter)
# Load the checkpoint in case it exists
state = manager.restore_or_initialize()
if state is not None:
# loaded variables from checkpoint folder
timesteps_already_done = int(
re.findall('ckpt-([0-9]*)',
state)[0]) #* FLAGS.save_interval
print('Loaded model from timestep %d' % timesteps_already_done)
else:
print('Training from scratch')
timesteps_already_done = 0
tf_step_counter.assign(timesteps_already_done)
print('Pretraining')
for i in tqdm.tqdm(range(bc_pretraining_steps)):
info_dict = model.update_step(dataset_iter,
train_target='encoder')
# (quantile_states, quantile_bins)
if i % FLAGS.log_interval == 0:
with summary_writer.as_default():
for k, v in info_dict.items():
v = tf.reduce_mean(v)
tf.summary.scalar(f'pretrain/{k}', v, step=i)
tf_step_counter.assign(i)
manager.save(checkpoint_number=i)
elif 'bc' in FLAGS.algo_name:
model = bc_pixel.BehavioralCloning(
env.observation_spec(),
env.action_spec(),
mixture=False,
encoder=None,
num_augmentations=FLAGS.num_data_augs,
rep_learn_keywords=FLAGS.rep_learn_keywords,
env_name=FLAGS.env_name,
batch_size=FLAGS.batch_size)
elif 'deepmdp' in FLAGS.algo_name:
model = deepmdp.DeepMdpLearner(
env.observation_spec(),
env.action_spec(),
embedding_dim=512,
num_distributions=1,
sequence_length=2,
learning_rate=3e-4,
num_augmentations=FLAGS.num_data_augs,
rep_learn_keywords=FLAGS.rep_learn_keywords,
batch_size=FLAGS.batch_size)
elif 'vpn' in FLAGS.algo_name:
model = vpn.ValuePredictionNetworkLearner(
env.observation_spec(),
env.action_spec(),
embedding_dim=512,
learning_rate=3e-4,
num_augmentations=FLAGS.num_data_augs,
rep_learn_keywords=FLAGS.rep_learn_keywords,
batch_size=FLAGS.batch_size)
elif 'cssc' in FLAGS.algo_name:
model = cssc.CSSC(env.observation_spec(),
env.action_spec(),
embedding_dim=512,
actor_lr=3e-4,
critic_lr=3e-4,
num_augmentations=FLAGS.num_data_augs,
rep_learn_keywords=FLAGS.rep_learn_keywords,
batch_size=FLAGS.batch_size)
elif 'pse' in FLAGS.algo_name:
model = pse.PSE(env.observation_spec(),
env.action_spec(),
embedding_dim=512,
actor_lr=3e-4,
critic_lr=3e-4,
num_augmentations=FLAGS.num_data_augs,
rep_learn_keywords=FLAGS.rep_learn_keywords,
batch_size=FLAGS.batch_size,
temperature=FLAGS.temp)
bc_pretraining_steps = FLAGS.pretrain
if pretrain:
print('Pretraining')
for i in tqdm.tqdm(range(bc_pretraining_steps)):
info_dict = model.update_step(dataset_iter,
train_target='encoder')
if i % FLAGS.log_interval == 0:
with summary_writer.as_default():
for k, v in info_dict.items():
v = tf.reduce_mean(v)
tf.summary.scalar(f'pretrain/{k}', v, step=i)
if 'fbrac' in FLAGS.algo_name or FLAGS.algo_name == 'bc':
# Either load the online policy:
if FLAGS.load_bc and FLAGS.env_name.startswith('procgen'):
env_id = [
i for i, name in enumerate(PROCGEN_ENVS) if name == env_name
][0] + 1 # map env string to digit [1,16]
if FLAGS.ckpt_timesteps == 10_000_000:
ckpt_iter = '0000020480'
elif FLAGS.ckpt_timesteps == 25_000_000:
ckpt_iter = '0000051200'
policy_weights_dir = (
'ppo_darts/'
'2021-06-22-16-36-54/%d/policies/checkpoints/'
'policy_checkpoint_%s/' % (env_id, ckpt_iter))
policy_def_dir = ('ppo_darts/'
'2021-06-22-16-36-54/%d/policies/policy/' %
(env_id))
bc = py_tf_eager_policy.SavedModelPyTFEagerPolicy(
policy_def_dir,
time_step_spec=env._time_step_spec, # pylint: disable=protected-access
action_spec=env._action_spec, # pylint: disable=protected-access
policy_state_spec=env._observation_spec, # pylint: disable=protected-access
info_spec=tf.TensorSpec(shape=(None, )),
load_specs_from_pbtxt=False)
bc.update_from_checkpoint(policy_weights_dir)
model.bc.policy = tf_utils.TfAgentsPolicy(bc)
else:
if FLAGS.algo_name == 'fbrac':
bc_pretraining_steps = 100_000
elif FLAGS.algo_name == 'bc':
bc_pretraining_steps = 1_000_000
if 'fbrac' in FLAGS.algo_name:
bc = model.bc
else:
bc = model
for i in tqdm.tqdm(range(bc_pretraining_steps)):
info_dict = bc.update_step(dataset_iter)
if i % FLAGS.log_interval == 0:
with summary_writer.as_default():
for k, v in info_dict.items():
v = tf.reduce_mean(v)
tf.summary.scalar(f'bc/{k}', v, step=i)
if FLAGS.algo_name == 'bc':
if (i + 1) % FLAGS.eval_interval == 0:
average_returns, average_length = evaluation.evaluate(
env, bc) # (FLAGS.env_name.startswith('procgen'))
average_returns_all, average_length_all = evaluation.evaluate(
env_all, bc)
with result_writer.as_default():
tf.summary.scalar('evaluation/returns',
average_returns,
step=i + 1)
tf.summary.scalar('evaluation/length',
average_length,
step=i + 1)
tf.summary.scalar('evaluation/returns-all',
average_returns_all,
step=i + 1)
tf.summary.scalar('evaluation/length-all',
average_length_all,
step=i + 1)
if FLAGS.algo_name == 'bc':
exit()
if not (FLAGS.algo_name == 'ours' and pretrain):
model_save_path = os.path.join(FLAGS.save_dir, 'weights', hparam_str)
checkpoint = tf.train.Checkpoint(**model.model_dict)
tf_step_counter = tf.Variable(0, dtype=tf.int32)
manager = tf.train.CheckpointManager(
checkpoint,
directory=model_save_path,
max_to_keep=1,
checkpoint_interval=FLAGS.save_interval,
step_counter=tf_step_counter)
# Load the checkpoint in case it exists
weights_path = tf.io.gfile.glob(model_save_path + '/ckpt-*.index')
key_fn = lambda x: int(re.findall(r'(\d+)', x)[-1])
weights_path.sort(key=key_fn)
if weights_path:
weights_path = weights_path[-1] # take most recent
state = manager.restore_or_initialize() # restore(weights_path)
if state is not None:
# loaded variables from checkpoint folder
timesteps_already_done = int(
re.findall('ckpt-([0-9]*)', state)[0]) #* FLAGS.save_interval
print('Loaded model from timestep %d' % timesteps_already_done)
else:
print('Training from scratch')
timesteps_already_done = 0
tf_step_counter.assign(timesteps_already_done)
for i in tqdm.tqdm(range(timesteps_already_done, FLAGS.num_updates)):
with summary_writer.as_default():
info_dict = model.update_step(
dataset_iter, train_target='rl' if pretrain else 'both')
if i % FLAGS.log_interval == 0:
with summary_writer.as_default():
for k, v in info_dict.items():
v = tf.reduce_mean(v)
tf.summary.scalar(f'training/{k}', v, step=i)
if (i + 1) % FLAGS.eval_interval == 0:
average_returns, average_length = evaluation.evaluate(env, model)
average_returns_all, average_length_all = evaluation.evaluate(
env_all, model)
with result_writer.as_default():
tf.summary.scalar('evaluation/returns-200',
average_returns,
step=i + 1)
tf.summary.scalar('evaluation/length-200',
average_length,
step=i + 1)
tf.summary.scalar('evaluation/returns-all',
average_returns_all,
step=i + 1)
tf.summary.scalar('evaluation/length-all',
average_length_all,
step=i + 1)
tf_step_counter.assign(i)
manager.save(checkpoint_number=i)
def main(_):
if FLAGS.eager:
tf.config.experimental_run_functions_eagerly(FLAGS.eager)
tf.random.set_seed(FLAGS.seed)
# np.random.seed(FLAGS.seed)
# random.seed(FLAGS.seed)
if 'procgen' in FLAGS.env_name:
_, env_name, train_levels, _ = FLAGS.env_name.split('-')
env = procgen_wrappers.TFAgentsParallelProcGenEnv(
1,
normalize_rewards=False,
env_name=env_name,
num_levels=int(train_levels),
start_level=0)
elif FLAGS.env_name.startswith('pixels-dm'):
if 'distractor' in FLAGS.env_name:
_, _, domain_name, _, _ = FLAGS.env_name.split('-')
else:
_, _, domain_name, _ = FLAGS.env_name.split('-')
if domain_name in ['cartpole']:
FLAGS.set_default('action_repeat', 8)
elif domain_name in ['reacher', 'cheetah', 'ball_in_cup', 'hopper']:
FLAGS.set_default('action_repeat', 4)
elif domain_name in ['finger', 'walker']:
FLAGS.set_default('action_repeat', 2)
env, _ = utils.load_env(FLAGS.env_name, FLAGS.seed,
FLAGS.action_repeat, FLAGS.frame_stack,
FLAGS.obs_type)
hparam_str = utils.make_hparam_string(FLAGS.xm_parameters,
algo_name=FLAGS.algo_name,
seed=FLAGS.seed,
task_name=FLAGS.env_name,
ckpt_timesteps=FLAGS.ckpt_timesteps)
summary_writer = tf.summary.create_file_writer(
os.path.join(FLAGS.save_dir, 'tb', hparam_str))
if FLAGS.env_name.startswith('procgen'):
# map env string to digit [1,16]
env_id = [
i for i, name in enumerate(PROCGEN_ENVS) if name == env_name
][0] + 1
if FLAGS.ckpt_timesteps == 10_000_000:
ckpt_iter = '0000020480'
elif FLAGS.ckpt_timesteps == 25_000_000:
ckpt_iter = '0000051200'
policy_weights_dir = ('ppo_darts/'
'2021-06-22-16-36-54/%d/policies/checkpoints/'
'policy_checkpoint_%s/' % (env_id, ckpt_iter))
policy_def_dir = ('ppo_darts/'
'2021-06-22-16-36-54/%d/policies/policy/' % (env_id))
model = py_tf_eager_policy.SavedModelPyTFEagerPolicy(
policy_def_dir,
time_step_spec=env._time_step_spec, # pylint: disable=protected-access
action_spec=env._action_spec, # pylint: disable=protected-access
policy_state_spec=env._observation_spec, # pylint: disable=protected-access
info_spec=tf.TensorSpec(shape=(None, )),
load_specs_from_pbtxt=False)
model.update_from_checkpoint(policy_weights_dir)
model = TfAgentsPolicy(model)
else:
if 'ddpg' in FLAGS.algo_name:
model = ddpg.DDPG(env.observation_spec(),
env.action_spec(),
cross_norm='crossnorm' in FLAGS.algo_name)
elif 'crr' in FLAGS.algo_name:
model = awr.AWR(env.observation_spec(),
env.action_spec(),
f='bin_max')
elif 'awr' in FLAGS.algo_name:
model = awr.AWR(env.observation_spec(),
env.action_spec(),
f='exp_mean')
elif 'sac_v1' in FLAGS.algo_name:
model = sac_v1.SAC(env.observation_spec(),
env.action_spec(),
target_entropy=-env.action_spec().shape[0])
elif 'asac' in FLAGS.algo_name:
model = asac.ASAC(env.observation_spec(),
env.action_spec(),
target_entropy=-env.action_spec().shape[0])
elif 'sac' in FLAGS.algo_name:
model = sac.SAC(env.observation_spec(),
env.action_spec(),
target_entropy=-env.action_spec().shape[0],
cross_norm='crossnorm' in FLAGS.algo_name,
pcl_actor_update='pc' in FLAGS.algo_name)
elif 'pcl' in FLAGS.algo_name:
model = pcl.PCL(env.observation_spec(),
env.action_spec(),
target_entropy=-env.action_spec().shape[0])
if 'distractor' in FLAGS.env_name:
ckpt_path = os.path.join(
('experiments/20210622_2023.policy_weights_sac'
'_1M_dmc_distractor_hard_pixel/'), 'results',
FLAGS.env_name + '__' + str(FLAGS.ckpt_timesteps))
else:
ckpt_path = os.path.join(
('experiments/20210607_2023.'
'policy_weights_dmc_1M_SAC_pixel'), 'results',
FLAGS.env_name + '__' + str(FLAGS.ckpt_timesteps))
model.load_weights(ckpt_path)
print('Loaded model weights')
with summary_writer.as_default():
env = procgen_wrappers.TFAgentsParallelProcGenEnv(
1,
normalize_rewards=False,
env_name=env_name,
num_levels=0,
start_level=0)
(avg_returns,
avg_len) = evaluation.evaluate(env,
model,
num_episodes=100,
return_distributions=False)
tf.summary.scalar('evaluation/returns-all', avg_returns, step=0)
tf.summary.scalar('evaluation/length-all', avg_len, step=0)
def main(_):
tf.config.experimental_run_functions_eagerly(FLAGS.eager)
gym_env, dataset = d4rl_utils.create_d4rl_env_and_dataset(
task_name=FLAGS.task_name, batch_size=FLAGS.batch_size)
env = gym_wrapper.GymWrapper(gym_env)
env = tf_py_environment.TFPyEnvironment(env)
dataset_iter = iter(dataset)
tf.random.set_seed(FLAGS.seed)
hparam_str = utils.make_hparam_string(FLAGS.xm_parameters,
algo_name=FLAGS.algo_name,
seed=FLAGS.seed,
task_name=FLAGS.task_name,
data_name=FLAGS.data_name)
summary_writer = tf.summary.create_file_writer(
os.path.join(FLAGS.save_dir, 'tb', hparam_str))
result_writer = tf.summary.create_file_writer(
os.path.join(FLAGS.save_dir, 'results', hparam_str))
if FLAGS.algo_name == 'bc':
model = behavioral_cloning.BehavioralCloning(env.observation_spec(),
env.action_spec())
elif FLAGS.algo_name == 'bc_mix':
model = behavioral_cloning.BehavioralCloning(env.observation_spec(),
env.action_spec(),
mixture=True)
elif 'ddpg' in FLAGS.algo_name:
model = ddpg.DDPG(env.observation_spec(), env.action_spec())
elif 'crr' in FLAGS.algo_name:
model = awr.AWR(env.observation_spec(), env.action_spec(), f='bin_max')
elif 'awr' in FLAGS.algo_name:
model = awr.AWR(env.observation_spec(),
env.action_spec(),
f='exp_mean')
elif 'bcq' in FLAGS.algo_name:
model = bcq.BCQ(env.observation_spec(), env.action_spec())
elif 'asac' in FLAGS.algo_name:
model = asac.ASAC(env.observation_spec(),
env.action_spec(),
target_entropy=-env.action_spec().shape[0])
elif 'sac' in FLAGS.algo_name:
model = sac.SAC(env.observation_spec(),
env.action_spec(),
target_entropy=-env.action_spec().shape[0])
elif 'cql' in FLAGS.algo_name:
model = cql.CQL(env.observation_spec(),
env.action_spec(),
target_entropy=-env.action_spec().shape[0])
elif 'brac' in FLAGS.algo_name:
if 'fbrac' in FLAGS.algo_name:
model = fisher_brac.FBRAC(
env.observation_spec(),
env.action_spec(),
target_entropy=-env.action_spec().shape[0],
f_reg=FLAGS.f_reg,
reward_bonus=FLAGS.reward_bonus)
else:
model = brac.BRAC(env.observation_spec(),
env.action_spec(),
target_entropy=-env.action_spec().shape[0])
model_folder = os.path.join(
FLAGS.save_dir, 'models',
f'{FLAGS.task_name}_{FLAGS.data_name}_{FLAGS.seed}')
if not tf.gfile.io.isdir(model_folder):
bc_pretraining_steps = 1_000_000
for i in tqdm.tqdm(range(bc_pretraining_steps)):
info_dict = model.bc.update_step(dataset_iter)
if i % FLAGS.log_interval == 0:
with summary_writer.as_default():
for k, v in info_dict.items():
tf.summary.scalar(f'training/{k}',
v,
step=i - bc_pretraining_steps)
# model.bc.policy.save_weights(os.path.join(model_folder, 'model'))
else:
model.bc.policy.load_weights(os.path.join(model_folder, 'model'))
for i in tqdm.tqdm(range(FLAGS.num_updates)):
with summary_writer.as_default():
info_dict = model.update_step(dataset_iter)
if i % FLAGS.log_interval == 0:
with summary_writer.as_default():
for k, v in info_dict.items():
tf.summary.scalar(f'training/{k}', v, step=i)
if (i + 1) % FLAGS.eval_interval == 0:
average_returns, average_length = evaluation.evaluate(env, model)
if FLAGS.data_name is None:
average_returns = gym_env.get_normalized_score(
average_returns) * 100.0
with result_writer.as_default():
tf.summary.scalar('evaluation/returns',
average_returns,
step=i + 1)
tf.summary.scalar('evaluation/length',
average_length,
step=i + 1)