def eval_performance(policy,
env,
period,
max_path_length,
num_rollouts,
seed=0):
# import ipdb; ipdb.set_trace()
# change the policy period
# do the rollouts and aggregate the performances
ext.set_seed(seed)
returns = []
if isinstance(policy, HierarchicalPolicyRandomTime):
with policy.fix_period(period):
for _ in trange(num_rollouts):
returns.append(
np.sum(
rollout(env, policy,
max_path_length=max_path_length)['rewards']))
# policy.curr_period = period
# policy.random_period = False
# with policy.manager.set_std_to_0():
# for _ in trange(num_rollouts):
# returns.append(np.sum(rollout(env, policy, max_path_length=max_path_length)['rewards']))
else:
policy.period = period
# with policy.manager.set_std_to_0():
for _ in trange(num_rollouts):
returns.append(
np.sum(
rollout(env, policy,
max_path_length=max_path_length)['rewards']))
return returns
def get_velocities(policy, env, max_path_length, num_rollouts, seed=0):
ext.set_seed(seed)
angles = []
for _ in trange(num_rollouts):
rollout_result = rollout(env, policy, max_path_length=max_path_length)
angles.append(rollout_result['env_infos']['joint_angles'])
return angles
def create_rllab_env(env_name, init_seed):
"""
create the rllab env
"""
env = eval(env_name)()
ext.set_seed(init_seed)
return env
def __init__(self, env, args):
self.env = env
self.args = args
# Parallel setup
parallel_sampler.initialize(n_parallel=args.n_parallel)
if args.seed is not None:
set_seed(args.seed)
parallel_sampler.set_seed(args.seed)
def set_seed(seed, env, framework):
if framework == 'gym':
env.unwrapped.seed(seed)
elif framework == 'rllab':
ext.set_seed(seed)
else:
raise("framework not supported")
return env
def __init__(self, env, args):
self.env = env
self.args = args
# Parallel setup
parallel_sampler.initialize(n_parallel=args.n_parallel)
if args.seed is not None:
set_seed(args.seed)
parallel_sampler.set_seed(args.seed)
def initialize_worker(group, rank, seed, cpu):
log_str = "MbSampler rank: {} initialized".format(rank)
try:
p = psutil.Process()
p.cpu_affinity([cpu])
log_str += ", CPU Affinity: {}".format(p.cpu_affinity())
except AttributeError:
pass
if seed is not None:
ext.set_seed(seed)
time.sleep(0.3) # (so the printing from set_seed is not intermixed)
log_str += ", Seed: {}".format(seed)
logger.log(log_str)
def get_latent_info(policy, env, period, max_path_length, num_rollouts):
# change the policy period
#do the rollouts and aggregate the performances
policy.period = period
ext.set_seed(0)
latents = []
for i in trange(num_rollouts):
latent_infos = rollout(
env, policy,
max_path_length=max_path_length)['agent_infos']['latents']
latents.append(latent_infos[np.array(range(0, len(latent_infos), 10),
dtype=np.uint32)])
return latents
def init_rank(self, rank):
self.rank = rank
if self.set_cpu_affinity:
self._set_affinity(rank)
self.baseline.init_rank(rank)
self.optimizer.init_rank(rank)
if self.exemplar is not None:
self.exemplar.init_rank(rank)
seed = ext.get_seed()
if seed is None:
# NOTE: Not sure if this is a good source for seed?
seed = int(1e6 * np.random.rand())
ext.set_seed(seed + rank)
def eval_performance(policy, env, max_path_length, num_rollouts):
#do the rollouts and aggregate the performances
ext.set_seed(0)
returns = []
with policy.manager.set_std_to_0():
for i in trange(num_rollouts):
returns.append(
np.sum(
rollout(env, policy,
max_path_length=max_path_length)['rewards']))
# if i%50 == 0:
# print(np.mean(np.array(returns)))
return returns
def __init__(self, env, args):
self.env = env
self.args = args
# Parallel setup
parallel_sampler.initialize(n_parallel=args.n_parallel)
if args.seed is not None:
set_seed(args.seed)
parallel_sampler.set_seed(args.seed)
index = 0
env, policy = self.parse_env_args(env, args)
self.algo = self.setup(env, policy, start_itr=index)
def eval_performance(policy,
env,
to_remove,
max_path_length=5000,
num_rollouts=1000):
policy.manager.to_remove = to_remove
ext.set_seed(0)
returns = []
for i in trange(num_rollouts):
returns.append(
np.sum(
rollout(env, policy,
max_path_length=max_path_length)['rewards']))
return returns
def setup(seed, n_parallel, log_dir):
if seed is not None:
set_seed(seed)
if n_parallel > 0:
from rllab.sampler import parallel_sampler
parallel_sampler.initialize(n_parallel=n_parallel)
if seed is not None:
parallel_sampler.set_seed(seed)
if os.path.isdir(log_dir) == False:
os.makedirs(log_dir, exist_ok=True)
logger.set_snapshot_dir(log_dir)
logger.add_tabular_output(log_dir + '/progress.csv')
def eval_p(policy, env, max_path_length, num_rollouts, seed):
# change the policy period
#do the rollouts and aggregate the performances
ext.set_seed(seed)
returns = []
# with policy.manager.set_std_to_0():
# for i in trange(num_rollouts):
# returns.append(np.sum(rollout(env, policy, max_path_length=max_path_length)['rewards']))
# return returns
for i in trange(num_rollouts, desc="Rollouts", ncols=80):
returns.append(
np.sum(
rollout(env, policy,
max_path_length=max_path_length)['rewards']))
return returns
def run_experiment(algo,
n_parallel=0,
seed=0,
plot=False,
log_dir=None,
exp_name=None,
snapshot_mode='last',
snapshot_gap=1,
exp_prefix='experiment',
log_tabular_only=False):
default_log_dir = config.LOG_DIR + "/local/" + exp_prefix
set_seed(seed)
if exp_name is None:
now = datetime.datetime.now(dateutil.tz.tzlocal())
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S_%f_%Z')
exp_name = 'experiment_%s' % (timestamp)
if n_parallel > 0:
from rllab.sampler import parallel_sampler
parallel_sampler.initialize(n_parallel=n_parallel)
parallel_sampler.set_seed(seed)
if plot:
from rllab.plotter import plotter
plotter.init_worker()
if log_dir is None:
log_dir = osp.join(default_log_dir, exp_name)
tabular_log_file = osp.join(log_dir, 'progress.csv')
text_log_file = osp.join(log_dir, 'debug.log')
#params_log_file = osp.join(log_dir, 'params.json')
#logger.log_parameters_lite(params_log_file, args)
logger.add_text_output(text_log_file)
logger.add_tabular_output(tabular_log_file)
prev_snapshot_dir = logger.get_snapshot_dir()
prev_mode = logger.get_snapshot_mode()
logger.set_snapshot_dir(log_dir)
logger.set_snapshot_mode(snapshot_mode)
logger.set_snapshot_gap(snapshot_gap)
logger.set_log_tabular_only(log_tabular_only)
logger.push_prefix("[%s] " % exp_name)
algo.train()
logger.set_snapshot_mode(prev_mode)
logger.set_snapshot_dir(prev_snapshot_dir)
logger.remove_tabular_output(tabular_log_file)
logger.remove_text_output(text_log_file)
logger.pop_prefix()
def eval_policy(self, itr, gpu_device=None, gpu_frac=None):
if itr == -1:
itr = 0
while os.path.exists(self._itr_file(itr)):
itr += 1
itr -= 1
if self.params['seed'] is not None:
set_seed(self.params['seed'])
if gpu_device is None:
gpu_device = self.params['policy']['gpu_device']
if gpu_frac is None:
gpu_frac = self.params['policy']['gpu_frac']
sess, graph = MACPolicy.create_session_and_graph(gpu_device=gpu_device,
gpu_frac=gpu_frac)
with graph.as_default(), sess.as_default():
policy = self._load_itr_policy(itr)
logger.log('Evaluating policy for itr {0}'.format(itr))
n_envs = 1
if 'max_path_length' in self.params['alg']:
max_path_length = self.params['alg']['max_path_length']
else:
max_path_length = self.env.horizon
sampler = RNNCriticSampler(
policy=policy,
env=self.env,
n_envs=n_envs,
replay_pool_size=int(1e4),
max_path_length=max_path_length,
save_rollouts=True,
sampling_method=self.params['alg']['replay_pool_sampling'])
rollouts = []
step = 0
logger.log('Starting rollout {0}'.format(len(rollouts)))
while len(rollouts) < self._num_rollouts:
sampler.step(step)
step += n_envs
new_rollouts = sampler.get_recent_paths()
if len(new_rollouts) > 0:
rollouts += new_rollouts
logger.log('Starting rollout {0}'.format(len(rollouts)))
self.save_eval_rollouts(itr, rollouts)
def create_env(env_str, is_normalize=True, seed=None):
from rllab.envs.gym_env import GymEnv, FixedIntervalVideoSchedule
from sandbox.gkahn.gcg.envs.rccar.square_env import SquareEnv
from sandbox.gkahn.gcg.envs.rccar.square_cluttered_env import SquareClutteredEnv
from sandbox.gkahn.gcg.envs.rccar.cylinder_env import CylinderEnv
inner_env = eval(env_str)
if is_normalize:
inner_env = normalize(inner_env)
env = TfEnv(inner_env)
# set seed
if seed is not None:
set_seed(seed)
if isinstance(inner_env, GymEnv):
inner_env.env.seed(seed)
return env
def startup(self, master=True):
if self.seed is None:
self.seed = make_seed()
set_seed(self.seed)
env_spec, sample_size, horizon, mid_batch_reset = self.sampler.initialize(
seed=self.seed + 1,
affinities=self.affinities,
discount=getattr(self.algo, "discount", None),
need_extra_obs=self.algo.need_extra_obs,
)
self.init_policy(env_spec)
self.algo.initialize(
policy=self.policy,
env_spec=env_spec,
sample_size=sample_size,
horizon=horizon,
mid_batch_reset=mid_batch_reset,
)
self.sampler.policy_init(self.policy)
if master:
n_itr = self.get_n_itr(sample_size)
self.algo.set_n_itr(n_itr)
self.init_logging()
return n_itr
def _worker_set_seed(_, seed):
logger.log("Setting seed to %d" % seed)
ext.set_seed(seed)
# parser.add_argument("type", help="Type of DDPG to run: ['unified-decaying', 'unified-gated-decaying', 'unified', 'unified-gated', 'regular']")
parser.add_argument("env",
help="The environment name from OpenAIGym environments")
parser.add_argument("--num_epochs", default=100, type=int)
parser.add_argument("--data_dir", default="./data/")
parser.add_argument("--use_ec2",
action="store_true",
help="Use your ec2 instances if configured")
parser.add_argument(
"--dont_terminate_machine",
action="store_false",
help="Whether to terminate your spot instance or not. Be careful.")
args = parser.parse_args()
stub(globals())
ext.set_seed(1)
supported_gym_envs = [
"MountainCarContinuous-v0", "InvertedPendulum-v1",
"InvertedDoublePendulum-v1", "Hopper-v1", "Walker2d-v1", "Humanoid-v1",
"Reacher-v1", "HalfCheetah-v1", "Swimmer-v1", "HumanoidStandup-v1"
]
other_env_class_map = {"Cartpole": CartpoleEnv}
if args.env in supported_gym_envs:
gymenv = GymEnv(args.env,
force_reset=True,
record_video=False,
record_log=False)
# gymenv.env.seed(1)
def _worker_set_seed(_, seed):
logger.log("Setting seed to %d" % seed)
logger.log("Setting seed to %d" % seed)
ext.set_seed(seed)
logger.log("Done Setting seed to %d" % seed)
parser.add_argument("-is",
'--init_state',
type=str,
help='vector of init_state')
parser.add_argument(
"-cf",
'--collection_file',
type=str,
help='path to the pkl file with start positions Collection')
args = parser.parse_args()
policy = None
env = None
if args.seed >= 0:
set_seed(args.seed)
if args.collection_file:
all_feasible_starts = pickle.load(open(args.collection_file, 'rb'))
with tf.Session() as sess:
data = joblib.load(args.file)
if "algo" in data:
policy = data["algo"].policy
env = data["algo"].env
else:
policy = data['policy']
env = data['env']
# easiest to hardest
init_pos = [[0, 0]]
# init_pos = [[0, 0],
def run_experiment(argv):
# e2crawfo: These imports, in this order, were necessary for fixing issues on cedar.
import rllab.mujoco_py.mjlib
import tensorflow
default_log_dir = config.LOG_DIR
now = datetime.datetime.now(dateutil.tz.tzlocal())
# avoid name clashes when running distributed jobs
rand_id = str(uuid.uuid4())[:5]
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S_%f_%Z')
default_exp_name = 'experiment_%s_%s' % (timestamp, rand_id)
parser = argparse.ArgumentParser()
parser.add_argument(
'--n_parallel',
type=int,
default=1,
help=
'Number of parallel workers to perform rollouts. 0 => don\'t start any workers'
)
parser.add_argument('--exp_name',
type=str,
default=default_exp_name,
help='Name of the experiment.')
parser.add_argument('--log_dir',
type=str,
default=None,
help='Path to save the log and iteration snapshot.')
parser.add_argument('--snapshot_mode',
type=str,
default='all',
help='Mode to save the snapshot. Can be either "all" '
'(all iterations will be saved), "last" (only '
'the last iteration will be saved), or "none" '
'(do not save snapshots)')
parser.add_argument('--snapshot_gap',
type=int,
default=1,
help='Gap between snapshot iterations.')
parser.add_argument('--tabular_log_file',
type=str,
default='progress.csv',
help='Name of the tabular log file (in csv).')
parser.add_argument('--text_log_file',
type=str,
default='debug.log',
help='Name of the text log file (in pure text).')
parser.add_argument('--params_log_file',
type=str,
default='params.json',
help='Name of the parameter log file (in json).')
parser.add_argument('--variant_log_file',
type=str,
default='variant.json',
help='Name of the variant log file (in json).')
parser.add_argument(
'--resume_from',
type=str,
default=None,
help='Name of the pickle file to resume experiment from.')
parser.add_argument('--plot',
type=ast.literal_eval,
default=False,
help='Whether to plot the iteration results')
parser.add_argument(
'--log_tabular_only',
type=ast.literal_eval,
default=False,
help=
'Whether to only print the tabular log information (in a horizontal format)'
)
parser.add_argument('--seed', type=int, help='Random seed for numpy')
parser.add_argument('--args_data',
type=str,
help='Pickled data for stub objects')
parser.add_argument('--variant_data',
type=str,
help='Pickled data for variant configuration')
parser.add_argument('--use_cloudpickle',
type=ast.literal_eval,
default=False)
args = parser.parse_args(argv[1:])
if args.seed is not None:
set_seed(args.seed)
if args.n_parallel > 0:
from rllab.sampler import parallel_sampler
parallel_sampler.initialize(n_parallel=args.n_parallel)
if args.seed is not None:
parallel_sampler.set_seed(args.seed)
if args.plot:
from rllab.plotter import plotter
plotter.init_worker()
if args.log_dir is None:
log_dir = osp.join(default_log_dir, args.exp_name)
else:
log_dir = args.log_dir
tabular_log_file = osp.join(log_dir, args.tabular_log_file)
text_log_file = osp.join(log_dir, args.text_log_file)
params_log_file = osp.join(log_dir, args.params_log_file)
if args.variant_data is not None:
variant_data = pickle.loads(base64.b64decode(args.variant_data))
variant_log_file = osp.join(log_dir, args.variant_log_file)
logger.log_variant(variant_log_file, variant_data)
else:
variant_data = None
if not args.use_cloudpickle:
logger.log_parameters_lite(params_log_file, args)
logger.add_text_output(text_log_file)
logger.add_tabular_output(tabular_log_file)
prev_snapshot_dir = logger.get_snapshot_dir()
prev_mode = logger.get_snapshot_mode()
logger.set_snapshot_dir(log_dir)
logger.set_tf_summary_dir(osp.join(log_dir, "tf_summary"))
logger.set_snapshot_mode(args.snapshot_mode)
logger.set_snapshot_gap(args.snapshot_gap)
logger.set_log_tabular_only(args.log_tabular_only)
logger.push_prefix("[%s] " % args.exp_name)
if args.resume_from is not None:
data = joblib.load(args.resume_from)
assert 'algo' in data
algo = data['algo']
maybe_iter = algo.train()
if is_iterable(maybe_iter):
for _ in maybe_iter:
pass
else:
# read from stdin
if args.use_cloudpickle:
import cloudpickle
method_call = cloudpickle.loads(base64.b64decode(args.args_data))
method_call(variant_data)
else:
data = pickle.loads(base64.b64decode(args.args_data))
maybe_iter = concretize(data)
if is_iterable(maybe_iter):
for _ in maybe_iter:
pass
logger.set_snapshot_mode(prev_mode)
logger.set_snapshot_dir(prev_snapshot_dir)
logger.remove_tabular_output(tabular_log_file)
logger.remove_text_output(text_log_file)
logger.pop_prefix()
def _worker_set_seed(_, seed):
ext.set_seed(seed)
def _graph_setup(self):
### create session and graph
tf_sess = tf.get_default_session()
if tf_sess is None:
tf_sess, tf_graph = MACPolicy.create_session_and_graph(
gpu_device=self._gpu_device, gpu_frac=self._gpu_frac)
tf_graph = tf_sess.graph
with tf_sess.as_default(), tf_graph.as_default():
if ext.get_seed() is not None:
ext.set_seed(ext.get_seed())
### create input output placeholders
tf_obs_ph, tf_actions_ph, tf_dones_ph, tf_rewards_ph, tf_obs_target_ph, \
tf_test_es_ph_dict, tf_episode_timesteps_ph = self._graph_input_output_placeholders()
self.global_step = tf.Variable(0,
trainable=False,
name='global_step')
### policy
policy_scope = 'policy'
with tf.variable_scope(policy_scope):
### create preprocess placeholders
tf_preprocess = self._graph_preprocess_placeholders()
### process obs to lowd
tf_obs_lowd = self._graph_obs_to_lowd(tf_obs_ph,
tf_preprocess,
is_training=True)
### create training policy
tf_train_values, tf_train_values_softmax, _, _ = \
self._graph_inference(tf_obs_lowd, tf_actions_ph[:, :self._H, :],
self._values_softmax, tf_preprocess, is_training=True)
with tf.variable_scope(policy_scope, reuse=True):
tf_train_values_test, tf_train_values_softmax_test, _, _ = \
self._graph_inference(tf_obs_lowd, tf_actions_ph[:, :self._get_action_test['H'], :],
self._values_softmax, tf_preprocess, is_training=False)
tf_get_value = tf.reduce_sum(tf_train_values_softmax_test *
tf_train_values_test,
reduction_indices=1)
### action selection
tf_get_action, tf_get_action_value, tf_get_action_reset_ops = \
self._graph_get_action(tf_obs_ph, self._get_action_test,
policy_scope, True, policy_scope, True,
tf_episode_timesteps_ph)
### exploration strategy and logprob
tf_get_action_explore = self._graph_get_action_explore(
tf_get_action, tf_test_es_ph_dict)
### get policy variables
tf_policy_vars = sorted(tf.get_collection(
xplatform.global_variables_collection_name(),
scope=policy_scope),
key=lambda v: v.name)
tf_trainable_policy_vars = sorted(tf.get_collection(
xplatform.trainable_variables_collection_name(),
scope=policy_scope),
key=lambda v: v.name)
### create target network
if self._use_target:
target_scope = 'target' if self._separate_target_params else 'policy'
### action selection
tf_obs_target_ph_packed = xplatform.concat([
tf_obs_target_ph[:, h - self._obs_history_len:h, :]
for h in range(self._obs_history_len,
self._obs_history_len + self._N + 1)
], 0)
tf_target_get_action, tf_target_get_action_values, _ = self._graph_get_action(
tf_obs_target_ph_packed,
self._get_action_target,
scope_select=policy_scope,
reuse_select=True,
scope_eval=target_scope,
reuse_eval=(target_scope == policy_scope),
tf_episode_timesteps_ph=None) # TODO would need to fill in
tf_target_get_action_values = tf.transpose(
tf.reshape(tf_target_get_action_values,
(self._N + 1, -1)))[:, 1:]
else:
tf_target_get_action_values = tf.zeros(
[tf.shape(tf_train_values)[0], self._N])
### update target network
if self._use_target and self._separate_target_params:
tf_policy_vars_nobatchnorm = list(
filter(
lambda v: 'biased' not in v.name and 'local_step'
not in v.name, tf_policy_vars))
tf_target_vars = sorted(tf.get_collection(
xplatform.global_variables_collection_name(),
scope=target_scope),
key=lambda v: v.name)
assert (len(tf_policy_vars_nobatchnorm) == len(tf_target_vars))
tf_update_target_fn = []
for var, var_target in zip(tf_policy_vars_nobatchnorm,
tf_target_vars):
assert (var.name.replace(policy_scope,
'') == var_target.name.replace(
target_scope, ''))
tf_update_target_fn.append(var_target.assign(var))
tf_update_target_fn = tf.group(*tf_update_target_fn)
else:
tf_target_vars = None
tf_update_target_fn = None
### optimization
tf_cost, tf_mse = self._graph_cost(tf_train_values,
tf_train_values_softmax,
tf_rewards_ph, tf_dones_ph,
tf_target_get_action_values)
tf_opt, tf_lr_ph = self._graph_optimize(tf_cost,
tf_trainable_policy_vars)
### initialize
self._graph_init_vars(tf_sess)
### what to return
return {
'sess': tf_sess,
'graph': tf_graph,
'obs_ph': tf_obs_ph,
'actions_ph': tf_actions_ph,
'dones_ph': tf_dones_ph,
'rewards_ph': tf_rewards_ph,
'obs_target_ph': tf_obs_target_ph,
'test_es_ph_dict': tf_test_es_ph_dict,
'episode_timesteps_ph': tf_episode_timesteps_ph,
'preprocess': tf_preprocess,
'get_value': tf_get_value,
'get_action': tf_get_action,
'get_action_explore': tf_get_action_explore,
'get_action_value': tf_get_action_value,
'get_action_reset_ops': tf_get_action_reset_ops,
'update_target_fn': tf_update_target_fn,
'cost': tf_cost,
'mse': tf_mse,
'opt': tf_opt,
'lr_ph': tf_lr_ph,
'policy_vars': tf_policy_vars,
'target_vars': tf_target_vars
}
def main():
now = datetime.datetime.now(dateutil.tz.tzlocal())
rand_id = str(uuid.uuid4())[:5]
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S_%f_%Z')
default_exp_name = 'experiment_%s_%s' % (timestamp, rand_id)
parser = argparse.ArgumentParser()
parser.add_argument('--exp_name',
type=str,
default=default_exp_name,
help='Name of the experiment.')
parser.add_argument('--discount', type=float, default=0.99)
parser.add_argument('--gae_lambda', type=float, default=1.0)
parser.add_argument('--reward_scale', type=float, default=1.0)
parser.add_argument('--n_iter', type=int, default=250)
parser.add_argument('--sampler_workers', type=int, default=1)
parser.add_argument('--max_traj_len', type=int, default=250)
parser.add_argument('--update_curriculum',
action='store_true',
default=False)
parser.add_argument('--n_timesteps', type=int, default=8000)
parser.add_argument('--control', type=str, default='centralized')
parser.add_argument('--rectangle', type=str, default='10,10')
parser.add_argument('--map_type', type=str, default='rectangle')
parser.add_argument('--n_evaders', type=int, default=5)
parser.add_argument('--n_pursuers', type=int, default=2)
parser.add_argument('--obs_range', type=int, default=3)
parser.add_argument('--n_catch', type=int, default=2)
parser.add_argument('--urgency', type=float, default=0.0)
parser.add_argument('--pursuit', dest='train_pursuit', action='store_true')
parser.add_argument('--evade', dest='train_pursuit', action='store_false')
parser.set_defaults(train_pursuit=True)
parser.add_argument('--surround', action='store_true', default=False)
parser.add_argument('--constraint_window', type=float, default=1.0)
parser.add_argument('--sample_maps', action='store_true', default=False)
parser.add_argument('--map_file', type=str, default='../maps/map_pool.npy')
parser.add_argument('--flatten', action='store_true', default=False)
parser.add_argument('--reward_mech', type=str, default='global')
parser.add_argument('--catchr', type=float, default=0.1)
parser.add_argument('--term_pursuit', type=float, default=5.0)
parser.add_argument('--recurrent', type=str, default=None)
parser.add_argument('--policy_hidden_sizes', type=str, default='128,128')
parser.add_argument('--baselin_hidden_sizes', type=str, default='128,128')
parser.add_argument('--baseline_type', type=str, default='linear')
parser.add_argument('--conv', action='store_true', default=False)
parser.add_argument('--max_kl', type=float, default=0.01)
parser.add_argument('--checkpoint', type=str, default=None)
parser.add_argument('--log_dir', type=str, required=False)
parser.add_argument('--tabular_log_file',
type=str,
default='progress.csv',
help='Name of the tabular log file (in csv).')
parser.add_argument('--text_log_file',
type=str,
default='debug.log',
help='Name of the text log file (in pure text).')
parser.add_argument('--params_log_file',
type=str,
default='params.json',
help='Name of the parameter log file (in json).')
parser.add_argument('--seed', type=int, help='Random seed for numpy')
parser.add_argument('--args_data',
type=str,
help='Pickled data for stub objects')
parser.add_argument('--snapshot_mode',
type=str,
default='all',
help='Mode to save the snapshot. Can be either "all" '
'(all iterations will be saved), "last" (only '
'the last iteration will be saved), or "none" '
'(do not save snapshots)')
parser.add_argument(
'--log_tabular_only',
type=ast.literal_eval,
default=False,
help=
'Whether to only print the tabular log information (in a horizontal format)'
)
args = parser.parse_args()
parallel_sampler.initialize(n_parallel=args.sampler_workers)
if args.seed is not None:
set_seed(args.seed)
parallel_sampler.set_seed(args.seed)
args.hidden_sizes = tuple(map(int, args.policy_hidden_sizes.split(',')))
if args.checkpoint:
with tf.Session() as sess:
data = joblib.load(args.checkpoint)
policy = data['policy']
env = data['env']
else:
if args.sample_maps:
map_pool = np.load(args.map_file)
else:
if args.map_type == 'rectangle':
env_map = TwoDMaps.rectangle_map(
*map(int, args.rectangle.split(',')))
elif args.map_type == 'complex':
env_map = TwoDMaps.complex_map(
*map(int, args.rectangle.split(',')))
else:
raise NotImplementedError()
map_pool = [env_map]
env = PursuitEvade(map_pool,
n_evaders=args.n_evaders,
n_pursuers=args.n_pursuers,
obs_range=args.obs_range,
n_catch=args.n_catch,
train_pursuit=args.train_pursuit,
urgency_reward=args.urgency,
surround=args.surround,
sample_maps=args.sample_maps,
constraint_window=args.constraint_window,
flatten=args.flatten,
reward_mech=args.reward_mech,
catchr=args.catchr,
term_pursuit=args.term_pursuit)
env = TfEnv(
RLLabEnv(StandardizedEnv(env,
scale_reward=args.reward_scale,
enable_obsnorm=False),
mode=args.control))
if args.recurrent:
if args.conv:
feature_network = ConvNetwork(
name='feature_net',
input_shape=emv.spec.observation_space.shape,
output_dim=5,
conv_filters=(16, 32, 32),
conv_filter_sizes=(3, 3, 3),
conv_strides=(1, 1, 1),
conv_pads=('VALID', 'VALID', 'VALID'),
hidden_sizes=(64, ),
hidden_nonlinearity=tf.nn.relu,
output_nonlinearity=tf.nn.softmax)
else:
feature_network = MLP(
name='feature_net',
input_shape=(env.spec.observation_space.flat_dim +
env.spec.action_space.flat_dim, ),
output_dim=5,
hidden_sizes=(256, 128, 64),
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None)
if args.recurrent == 'gru':
policy = CategoricalGRUPolicy(env_spec=env.spec,
feature_network=feature_network,
hidden_dim=int(
args.policy_hidden_sizes),
name='policy')
elif args.recurrent == 'lstm':
policy = CategoricalLSTMPolicy(env_spec=env.spec,
feature_network=feature_network,
hidden_dim=int(
args.policy_hidden_sizes),
name='policy')
elif args.conv:
feature_network = ConvNetwork(
name='feature_net',
input_shape=env.spec.observation_space.shape,
output_dim=5,
conv_filters=(8, 16),
conv_filter_sizes=(3, 3),
conv_strides=(2, 1),
conv_pads=('VALID', 'VALID'),
hidden_sizes=(32, ),
hidden_nonlinearity=tf.nn.relu,
output_nonlinearity=tf.nn.softmax)
policy = CategoricalMLPPolicy(name='policy',
env_spec=env.spec,
prob_network=feature_network)
else:
policy = CategoricalMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=args.hidden_sizes)
if args.baseline_type == 'linear':
baseline = LinearFeatureBaseline(env_spec=env.spec)
else:
baseline = ZeroBaseline(env_spec=env.spec)
# logger
default_log_dir = config.LOG_DIR
if args.log_dir is None:
log_dir = osp.join(default_log_dir, args.exp_name)
else:
log_dir = args.log_dir
tabular_log_file = osp.join(log_dir, args.tabular_log_file)
text_log_file = osp.join(log_dir, args.text_log_file)
params_log_file = osp.join(log_dir, args.params_log_file)
logger.log_parameters_lite(params_log_file, args)
logger.add_text_output(text_log_file)
logger.add_tabular_output(tabular_log_file)
prev_snapshot_dir = logger.get_snapshot_dir()
prev_mode = logger.get_snapshot_mode()
logger.set_snapshot_dir(log_dir)
logger.set_snapshot_mode(args.snapshot_mode)
logger.set_log_tabular_only(args.log_tabular_only)
logger.push_prefix("[%s] " % args.exp_name)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=args.n_timesteps,
max_path_length=args.max_traj_len,
n_itr=args.n_iter,
discount=args.discount,
gae_lambda=args.gae_lambda,
step_size=args.max_kl,
optimizer=ConjugateGradientOptimizer(hvp_approach=FiniteDifferenceHvp(
base_eps=1e-5)) if args.recurrent else None,
mode=args.control,
)
algo.train()
def run_experiment(argv):
default_log_dir = config.LOG_DIR
now = datetime.datetime.now(dateutil.tz.tzlocal())
# avoid name clashes when running distributed jobs
rand_id = str(uuid.uuid4())[:5]
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S_%f_%Z')
default_exp_name = 'experiment_%s_%s' % (timestamp, rand_id)
parser = argparse.ArgumentParser()
parser.add_argument('--n_parallel', type=int, default=1,
help='Number of parallel workers to perform rollouts. 0 => don\'t start any workers')
parser.add_argument(
'--exp_name', type=str, default=default_exp_name, help='Name of the experiment.')
parser.add_argument('--log_dir', type=str, default=None,
help='Path to save the log and iteration snapshot.')
parser.add_argument('--snapshot_mode', type=str, default='all',
help='Mode to save the snapshot. Can be either "all" '
'(all iterations will be saved), "last" (only '
'the last iteration will be saved), "gap" (every'
'`snapshot_gap` iterations are saved), or "none" '
'(do not save snapshots)')
parser.add_argument('--snapshot_gap', type=int, default=1,
help='Gap between snapshot iterations.')
parser.add_argument('--tabular_log_file', type=str, default='progress.csv',
help='Name of the tabular log file (in csv).')
parser.add_argument('--text_log_file', type=str, default='debug.log',
help='Name of the text log file (in pure text).')
parser.add_argument('--params_log_file', type=str, default='params.json',
help='Name of the parameter log file (in json).')
parser.add_argument('--variant_log_file', type=str, default='variant.json',
help='Name of the variant log file (in json).')
parser.add_argument('--resume_from', type=str, default=None,
help='Name of the pickle file to resume experiment from.')
parser.add_argument('--plot', type=ast.literal_eval, default=False,
help='Whether to plot the iteration results')
parser.add_argument('--log_tabular_only', type=ast.literal_eval, default=False,
help='Whether to only print the tabular log information (in a horizontal format)')
parser.add_argument('--seed', type=int,
help='Random seed for numpy')
parser.add_argument('--args_data', type=str,
help='Pickled data for stub objects')
parser.add_argument('--variant_data', type=str,
help='Pickled data for variant configuration')
parser.add_argument('--use_cloudpickle', type=ast.literal_eval, default=False)
args = parser.parse_args(argv[1:])
if args.seed is not None:
set_seed(args.seed)
if args.n_parallel > 0:
from rllab.sampler import parallel_sampler
parallel_sampler.initialize(n_parallel=args.n_parallel)
if args.seed is not None:
parallel_sampler.set_seed(args.seed)
if args.plot:
from rllab.plotter import plotter
plotter.init_worker()
if args.log_dir is None:
log_dir = osp.join(default_log_dir, args.exp_name)
else:
log_dir = args.log_dir
tabular_log_file = osp.join(log_dir, args.tabular_log_file)
text_log_file = osp.join(log_dir, args.text_log_file)
params_log_file = osp.join(log_dir, args.params_log_file)
if args.variant_data is not None:
variant_data = pickle.loads(base64.b64decode(args.variant_data))
variant_log_file = osp.join(log_dir, args.variant_log_file)
logger.log_variant(variant_log_file, variant_data)
else:
variant_data = None
if not args.use_cloudpickle:
logger.log_parameters_lite(params_log_file, args)
logger.add_text_output(text_log_file)
logger.add_tabular_output(tabular_log_file)
prev_snapshot_dir = logger.get_snapshot_dir()
prev_mode = logger.get_snapshot_mode()
logger.set_snapshot_dir(log_dir)
logger.set_snapshot_mode(args.snapshot_mode)
logger.set_snapshot_gap(args.snapshot_gap)
logger.set_log_tabular_only(args.log_tabular_only)
logger.push_prefix("[%s] " % args.exp_name)
if args.resume_from is not None:
data = joblib.load(args.resume_from)
assert 'algo' in data
algo = data['algo']
algo.train()
else:
# read from stdin
if args.use_cloudpickle:
import cloudpickle
method_call = cloudpickle.loads(base64.b64decode(args.args_data))
method_call(variant_data)
else:
data = pickle.loads(base64.b64decode(args.args_data))
maybe_iter = concretize(data)
if is_iterable(maybe_iter):
for _ in maybe_iter:
pass
logger.set_snapshot_mode(prev_mode)
logger.set_snapshot_dir(prev_snapshot_dir)
logger.remove_tabular_output(tabular_log_file)
logger.remove_text_output(text_log_file)
logger.pop_prefix()
def main():
now = datetime.datetime.now(dateutil.tz.tzlocal())
rand_id = str(uuid.uuid4())[:5]
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S_%f_%Z')
default_exp_name = 'experiment_%s_%s' % (timestamp, rand_id)
parser = argparse.ArgumentParser()
parser.add_argument('--exp_name',
type=str,
default=default_exp_name,
help='Name of the experiment.')
parser.add_argument('--discount', type=float, default=0.95)
parser.add_argument('--gae_lambda', type=float, default=0.99)
parser.add_argument('--reward_scale', type=float, default=1.0)
parser.add_argument('--enable_obsnorm', action='store_true', default=False)
parser.add_argument('--chunked', action='store_true', default=False)
parser.add_argument('--n_iter', type=int, default=250)
parser.add_argument('--sampler_workers', type=int, default=1)
parser.add_argument('--max_traj_len', type=int, default=250)
parser.add_argument('--update_curriculum',
action='store_true',
default=False)
parser.add_argument('--anneal_step_size', type=int, default=0)
parser.add_argument('--n_timesteps', type=int, default=8000)
parser.add_argument('--control', type=str, default='centralized')
parser.add_argument('--buffer_size', type=int, default=1)
parser.add_argument('--radius', type=float, default=0.015)
parser.add_argument('--n_evaders', type=int, default=10)
parser.add_argument('--n_pursuers', type=int, default=8)
parser.add_argument('--n_poison', type=int, default=10)
parser.add_argument('--n_coop', type=int, default=4)
parser.add_argument('--n_sensors', type=int, default=30)
parser.add_argument('--sensor_range', type=str, default='0.2')
parser.add_argument('--food_reward', type=float, default=5)
parser.add_argument('--poison_reward', type=float, default=-1)
parser.add_argument('--encounter_reward', type=float, default=0.05)
parser.add_argument('--reward_mech', type=str, default='local')
parser.add_argument('--recurrent', type=str, default=None)
parser.add_argument('--baseline_type', type=str, default='linear')
parser.add_argument('--policy_hidden_sizes', type=str, default='128,128')
parser.add_argument('--baseline_hidden_sizes', type=str, default='128,128')
parser.add_argument('--max_kl', type=float, default=0.01)
parser.add_argument('--log_dir', type=str, required=False)
parser.add_argument('--tabular_log_file',
type=str,
default='progress.csv',
help='Name of the tabular log file (in csv).')
parser.add_argument('--text_log_file',
type=str,
default='debug.log',
help='Name of the text log file (in pure text).')
parser.add_argument('--params_log_file',
type=str,
default='params.json',
help='Name of the parameter log file (in json).')
parser.add_argument('--seed', type=int, help='Random seed for numpy')
parser.add_argument('--args_data',
type=str,
help='Pickled data for stub objects')
parser.add_argument('--snapshot_mode',
type=str,
default='all',
help='Mode to save the snapshot. Can be either "all" '
'(all iterations will be saved), "last" (only '
'the last iteration will be saved), or "none" '
'(do not save snapshots)')
parser.add_argument(
'--log_tabular_only',
type=ast.literal_eval,
default=False,
help=
'Whether to only print the tabular log information (in a horizontal format)'
)
args = parser.parse_args()
parallel_sampler.initialize(n_parallel=args.sampler_workers)
if args.seed is not None:
set_seed(args.seed)
parallel_sampler.set_seed(args.seed)
args.hidden_sizes = tuple(map(int, args.policy_hidden_sizes.split(',')))
centralized = True if args.control == 'centralized' else False
sensor_range = np.array(map(float, args.sensor_range.split(',')))
if len(sensor_range) == 1:
sensor_range = sensor_range[0]
else:
assert sensor_range.shape == (args.n_pursuers, )
env = MAWaterWorld(args.n_pursuers,
args.n_evaders,
args.n_coop,
args.n_poison,
radius=args.radius,
n_sensors=args.n_sensors,
food_reward=args.food_reward,
poison_reward=args.poison_reward,
encounter_reward=args.encounter_reward,
reward_mech=args.reward_mech,
sensor_range=sensor_range,
obstacle_loc=None)
env = TfEnv(
RLLabEnv(StandardizedEnv(env,
scale_reward=args.reward_scale,
enable_obsnorm=args.enable_obsnorm),
mode=args.control))
if args.buffer_size > 1:
env = ObservationBuffer(env, args.buffer_size)
if args.recurrent:
feature_network = MLP(
name='feature_net',
input_shape=(env.spec.observation_space.flat_dim +
env.spec.action_space.flat_dim, ),
output_dim=16,
hidden_sizes=(128, 64, 32),
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None)
if args.recurrent == 'gru':
policy = GaussianGRUPolicy(env_spec=env.spec,
feature_network=feature_network,
hidden_dim=int(
args.policy_hidden_sizes),
name='policy')
elif args.recurrent == 'lstm':
policy = GaussianLSTMPolicy(env_spec=env.spec,
feature_network=feature_network,
hidden_dim=int(
args.policy_hidden_sizes),
name='policy')
else:
policy = GaussianMLPPolicy(
name='policy',
env_spec=env.spec,
hidden_sizes=tuple(map(int, args.policy_hidden_sizes.split(','))),
min_std=10e-5)
if args.baseline_type == 'linear':
baseline = LinearFeatureBaseline(env_spec=env.spec)
elif args.baseline_type == 'mlp':
raise NotImplementedError()
# baseline = GaussianMLPBaseline(
# env_spec=env.spec, hidden_sizes=tuple(map(int, args.baseline_hidden_sizes.split(','))))
else:
baseline = ZeroBaseline(env_spec=env.spec)
# logger
default_log_dir = config.LOG_DIR
if args.log_dir is None:
log_dir = osp.join(default_log_dir, args.exp_name)
else:
log_dir = args.log_dir
tabular_log_file = osp.join(log_dir, args.tabular_log_file)
text_log_file = osp.join(log_dir, args.text_log_file)
params_log_file = osp.join(log_dir, args.params_log_file)
logger.log_parameters_lite(params_log_file, args)
logger.add_text_output(text_log_file)
logger.add_tabular_output(tabular_log_file)
prev_snapshot_dir = logger.get_snapshot_dir()
prev_mode = logger.get_snapshot_mode()
logger.set_snapshot_dir(log_dir)
logger.set_snapshot_mode(args.snapshot_mode)
logger.set_log_tabular_only(args.log_tabular_only)
logger.push_prefix("[%s] " % args.exp_name)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=args.n_timesteps,
max_path_length=args.max_traj_len,
#max_path_length_limit=args.max_path_length_limit,
update_max_path_length=args.update_curriculum,
anneal_step_size=args.anneal_step_size,
n_itr=args.n_iter,
discount=args.discount,
gae_lambda=args.gae_lambda,
step_size=args.max_kl,
optimizer=ConjugateGradientOptimizer(hvp_approach=FiniteDifferenceHvp(
base_eps=1e-5)) if args.recurrent else None,
mode=args.control
if not args.chunked else 'chunk_{}'.format(args.control),
)
algo.train()
def run_experiment(argv):
default_log_dir = config.LOG_DIR
now = datetime.datetime.now(dateutil.tz.tzlocal())
# avoid name clashes when running distributed jobs
rand_id = str(uuid.uuid4())[:5]
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S_%f_%Z')
default_exp_name = 'experiment_%s_%s' % (timestamp, rand_id)
parser = argparse.ArgumentParser()
parser.add_argument(
'--n_parallel',
type=int,
default=1,
help=
'Number of parallel workers to perform rollouts. 0 => don\'t start any workers'
)
parser.add_argument('--exp_name',
type=str,
default=default_exp_name,
help='Name of the experiment.')
parser.add_argument('--log_dir',
type=str,
default=None,
help='Path to save the log and iteration snapshot.')
parser.add_argument('--snapshot_mode',
type=str,
default='all',
help='Mode to save the snapshot. Can be either "all" '
'(all iterations will be saved), "last" (only '
'the last iteration will be saved), "gap" (every'
'`snapshot_gap` iterations are saved), or "none" '
'(do not save snapshots)')
parser.add_argument('--snapshot_gap',
type=int,
default=1,
help='Gap between snapshot iterations.')
parser.add_argument('--tabular_log_file',
type=str,
default='progress.csv',
help='Name of the tabular log file (in csv).')
parser.add_argument('--text_log_file',
type=str,
default='debug.log',
help='Name of the text log file (in pure text).')
parser.add_argument('--tensorboard_log_dir',
type=str,
default='tb',
help='Name of the folder for tensorboard_summary.')
parser.add_argument(
'--tensorboard_step_key',
type=str,
default=None,
help=
'Name of the step key in log data which shows the step in tensorboard_summary.'
)
parser.add_argument('--params_log_file',
type=str,
default='params.json',
help='Name of the parameter log file (in json).')
parser.add_argument('--variant_log_file',
type=str,
default='variant.json',
help='Name of the variant log file (in json).')
parser.add_argument(
'--resume_from',
type=str,
default=None,
help='Name of the pickle file to resume experiment from.')
parser.add_argument('--plot',
type=ast.literal_eval,
default=False,
help='Whether to plot the iteration results')
parser.add_argument(
'--log_tabular_only',
type=ast.literal_eval,
default=False,
help=
'Whether to only print the tabular log information (in a horizontal format)'
)
parser.add_argument('--seed', type=int, help='Random seed for numpy')
parser.add_argument('--args_data',
type=str,
help='Pickled data for stub objects')
parser.add_argument('--variant_data',
type=str,
help='Pickled data for variant configuration')
parser.add_argument('--use_cloudpickle',
type=ast.literal_eval,
default=False)
parser.add_argument('--checkpoint_dir',
type=str,
default='checkpoint',
help='Name of the folder for checkpoints.')
parser.add_argument('--obs_dir',
type=str,
default='obs',
help='Name of the folder for original observations.')
args = parser.parse_args(argv[1:])
if args.seed is not None:
set_seed(args.seed)
if args.n_parallel > 0:
from rllab.sampler import parallel_sampler
parallel_sampler.initialize(n_parallel=args.n_parallel)
if args.seed is not None:
parallel_sampler.set_seed(args.seed)
if args.plot:
from rllab.plotter import plotter
plotter.init_worker()
if args.log_dir is None:
log_dir = osp.join(default_log_dir, args.exp_name)
else:
log_dir = args.log_dir
tabular_log_file = osp.join(log_dir, args.tabular_log_file)
text_log_file = osp.join(log_dir, args.text_log_file)
params_log_file = osp.join(log_dir, args.params_log_file)
tensorboard_log_dir = osp.join(log_dir, args.tensorboard_log_dir)
checkpoint_dir = osp.join(log_dir, args.checkpoint_dir)
obs_dir = osp.join(log_dir, args.obs_dir)
if args.variant_data is not None:
variant_data = pickle.loads(base64.b64decode(args.variant_data))
variant_log_file = osp.join(log_dir, args.variant_log_file)
logger.log_variant(variant_log_file, variant_data)
else:
variant_data = None
if not args.use_cloudpickle:
logger.log_parameters_lite(params_log_file, args)
logger.add_text_output(text_log_file)
logger.add_tabular_output(tabular_log_file)
logger.set_tensorboard_dir(tensorboard_log_dir)
logger.set_checkpoint_dir(checkpoint_dir)
logger.set_obs_dir(obs_dir)
prev_snapshot_dir = logger.get_snapshot_dir()
prev_mode = logger.get_snapshot_mode()
logger.set_snapshot_dir(log_dir)
logger.set_snapshot_mode(args.snapshot_mode)
logger.set_snapshot_gap(args.snapshot_gap)
logger.set_log_tabular_only(args.log_tabular_only)
logger.set_tensorboard_step_key(args.tensorboard_step_key)
logger.push_prefix("[%s] " % args.exp_name)
git_commit = get_git_commit_hash()
logger.log('Git commit: {}'.format(git_commit))
git_diff_file_path = osp.join(log_dir,
'git_diff_{}.patch'.format(git_commit))
save_git_diff_to_file(git_diff_file_path)
logger.log('hostname: {}, pid: {}, tmux session: {}'.format(
socket.gethostname(), os.getpid(), get_tmux_session_name()))
if args.resume_from is not None:
data = joblib.load(args.resume_from)
assert 'algo' in data
algo = data['algo']
algo.train()
else:
# read from stdin
if args.use_cloudpickle:
import cloudpickle
method_call = cloudpickle.loads(base64.b64decode(args.args_data))
method_call(variant_data)
else:
data = pickle.loads(base64.b64decode(args.args_data))
maybe_iter = concretize(data)
if is_iterable(maybe_iter):
for _ in maybe_iter:
pass
logger.set_snapshot_mode(prev_mode)
logger.set_snapshot_dir(prev_snapshot_dir)
logger.remove_tabular_output(tabular_log_file)
logger.remove_text_output(text_log_file)
logger.pop_prefix()
def _worker_set_seed(_, seed):
ext.set_seed(seed)
if pickled_mode:
run_experiment_lite(
algo.train_seek(),
exp_prefix="trpo-expl",
n_parallel=2,
snapshot_mode="last",
seed=seed,
mode="local",
script="rllab/run_experiment_lite.py",
)
else:
from sandbox.vime.sampler import parallel_sampler_expl as parallel_sampler
parallel_sampler.initialize(n_parallel=n_parallel)
if seed is not None:
set_seed(seed)
parallel_sampler.set_seed(seed)
if plot:
from rllab.plotter import plotter
plotter.init_worker()
tabular_log_file_fullpath = osp.join(log_dir, tabular_log_file)
text_log_file_fullpath = osp.join(log_dir, text_log_file)
# params_log_file_fullpath = osp.join(log_dir, params_log_file)
params_all_log_file_fullpath = osp.join(log_dir, params_all_log_file)
# logger.log_parameters_lite(params_log_file, args)
logger.add_text_output(text_log_file_fullpath)
logger.add_tabular_output(tabular_log_file_fullpath)
prev_snapshot_dir = logger.get_snapshot_dir()
def __init__(self, env, args):
self.args = args
# Parallel setup
parallel_sampler.initialize(n_parallel=args.n_parallel)
if args.seed is not None:
set_seed(args.seed)
parallel_sampler.set_seed(args.seed)
env, policy = rllab_envpolicy_parser(env, args)
if not args.algo == 'thddpg':
# Baseline
if args.baseline_type == 'linear':
baseline = LinearFeatureBaseline(env_spec=env.spec)
elif args.baseline_type == 'zero':
baseline = ZeroBaseline(env_spec=env.spec)
else:
raise NotImplementedError(args.baseline_type)
# Logger
default_log_dir = config.LOG_DIR
if args.log_dir is None:
log_dir = osp.join(default_log_dir, args.exp_name)
else:
log_dir = args.log_dir
tabular_log_file = osp.join(log_dir, args.tabular_log_file)
text_log_file = osp.join(log_dir, args.text_log_file)
params_log_file = osp.join(log_dir, args.params_log_file)
logger.log_parameters_lite(params_log_file, args)
logger.add_text_output(text_log_file)
logger.add_tabular_output(tabular_log_file)
prev_snapshot_dir = logger.get_snapshot_dir()
prev_mode = logger.get_snapshot_mode()
logger.set_snapshot_dir(log_dir)
logger.set_snapshot_mode(args.snapshot_mode)
logger.set_log_tabular_only(args.log_tabular_only)
logger.push_prefix("[%s] " % args.exp_name)
if args.algo == 'tftrpo':
self.algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=args.batch_size,
max_path_length=args.max_path_length,
n_itr=args.n_iter,
discount=args.discount,
gae_lambda=args.gae_lambda,
step_size=args.step_size,
optimizer=ConjugateGradientOptimizer(
hvp_approach=FiniteDifferenceHvp(
base_eps=1e-5)) if args.recurrent else None,
mode=args.control)
elif args.algo == 'thddpg':
qfunc = thContinuousMLPQFunction(env_spec=env.spec)
if args.exp_strategy == 'ou':
es = OUStrategy(env_spec=env.spec)
elif args.exp_strategy == 'gauss':
es = GaussianStrategy(env_spec=env.spec)
else:
raise NotImplementedError()
self.algo = thDDPG(env=env,
policy=policy,
qf=qfunc,
es=es,
batch_size=args.batch_size,
max_path_length=args.max_path_length,
epoch_length=args.epoch_length,
min_pool_size=args.min_pool_size,
replay_pool_size=args.replay_pool_size,
n_epochs=args.n_iter,
discount=args.discount,
scale_reward=0.01,
qf_learning_rate=args.qfunc_lr,
policy_learning_rate=args.policy_lr,
eval_samples=args.eval_samples,
mode=args.control)
def main():
now = datetime.datetime.now(dateutil.tz.tzlocal())
rand_id = str(uuid.uuid4())[:5]
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S_%f_%Z')
default_exp_name = 'experiment_%s_%s' % (timestamp, rand_id)
parser = argparse.ArgumentParser()
parser.add_argument(
'--exp_name', type=str, default=default_exp_name, help='Name of the experiment.')
parser.add_argument('--discount', type=float, default=0.99)
parser.add_argument('--gae_lambda', type=float, default=1.0)
parser.add_argument('--reward_scale', type=float, default=1.0)
parser.add_argument('--n_iter', type=int, default=250)
parser.add_argument('--sampler_workers', type=int, default=1)
parser.add_argument('--max_traj_len', type=int, default=250)
parser.add_argument('--update_curriculum', action='store_true', default=False)
parser.add_argument('--n_timesteps', type=int, default=8000)
parser.add_argument('--control', type=str, default='centralized')
parser.add_argument('--rectangle', type=str, default='10,10')
parser.add_argument('--map_type', type=str, default='rectangle')
parser.add_argument('--n_evaders', type=int, default=5)
parser.add_argument('--n_pursuers', type=int, default=2)
parser.add_argument('--obs_range', type=int, default=3)
parser.add_argument('--n_catch', type=int, default=2)
parser.add_argument('--urgency', type=float, default=0.0)
parser.add_argument('--pursuit', dest='train_pursuit', action='store_true')
parser.add_argument('--evade', dest='train_pursuit', action='store_false')
parser.set_defaults(train_pursuit=True)
parser.add_argument('--surround', action='store_true', default=False)
parser.add_argument('--constraint_window', type=float, default=1.0)
parser.add_argument('--sample_maps', action='store_true', default=False)
parser.add_argument('--map_file', type=str, default='../maps/map_pool.npy')
parser.add_argument('--flatten', action='store_true', default=False)
parser.add_argument('--reward_mech', type=str, default='global')
parser.add_argument('--catchr', type=float, default=0.1)
parser.add_argument('--term_pursuit', type=float, default=5.0)
parser.add_argument('--recurrent', type=str, default=None)
parser.add_argument('--policy_hidden_sizes', type=str, default='128,128')
parser.add_argument('--baselin_hidden_sizes', type=str, default='128,128')
parser.add_argument('--baseline_type', type=str, default='linear')
parser.add_argument('--conv', action='store_true', default=False)
parser.add_argument('--max_kl', type=float, default=0.01)
parser.add_argument('--log_dir', type=str, required=False)
parser.add_argument('--tabular_log_file', type=str, default='progress.csv',
help='Name of the tabular log file (in csv).')
parser.add_argument('--text_log_file', type=str, default='debug.log',
help='Name of the text log file (in pure text).')
parser.add_argument('--params_log_file', type=str, default='params.json',
help='Name of the parameter log file (in json).')
parser.add_argument('--seed', type=int,
help='Random seed for numpy')
parser.add_argument('--args_data', type=str,
help='Pickled data for stub objects')
parser.add_argument('--snapshot_mode', type=str, default='all',
help='Mode to save the snapshot. Can be either "all" '
'(all iterations will be saved), "last" (only '
'the last iteration will be saved), or "none" '
'(do not save snapshots)')
parser.add_argument('--log_tabular_only', type=ast.literal_eval, default=False,
help='Whether to only print the tabular log information (in a horizontal format)')
args = parser.parse_args()
parallel_sampler.initialize(n_parallel=args.sampler_workers)
if args.seed is not None:
set_seed(args.seed)
parallel_sampler.set_seed(args.seed)
args.hidden_sizes = tuple(map(int, args.policy_hidden_sizes.split(',')))
if args.sample_maps:
map_pool = np.load(args.map_file)
else:
if args.map_type == 'rectangle':
env_map = TwoDMaps.rectangle_map(*map(int, args.rectangle.split(',')))
elif args.map_type == 'complex':
env_map = TwoDMaps.complex_map(*map(int, args.rectangle.split(',')))
else:
raise NotImplementedError()
map_pool = [env_map]
env = PursuitEvade(map_pool, n_evaders=args.n_evaders, n_pursuers=args.n_pursuers,
obs_range=args.obs_range, n_catch=args.n_catch,
train_pursuit=args.train_pursuit, urgency_reward=args.urgency,
surround=args.surround, sample_maps=args.sample_maps,
constraint_window=args.constraint_window,
flatten=args.flatten,
reward_mech=args.reward_mech,
catchr=args.catchr,
term_pursuit=args.term_pursuit)
env = RLLabEnv(
StandardizedEnv(env, scale_reward=args.reward_scale, enable_obsnorm=False),
mode=args.control)
if args.recurrent:
if args.conv:
feature_network = ConvNetwork(
input_shape=emv.spec.observation_space.shape,
output_dim=5,
conv_filters=(8,16,16),
conv_filter_sizes=(3,3,3),
conv_strides=(1,1,1),
conv_pads=('VALID','VALID','VALID'),
hidden_sizes=(64,),
hidden_nonlinearity=NL.rectify,
output_nonlinearity=NL.softmax)
else:
feature_network = MLP(
input_shape=(env.spec.observation_space.flat_dim + env.spec.action_space.flat_dim,),
output_dim=5, hidden_sizes=(128,128,128), hidden_nonlinearity=NL.tanh,
output_nonlinearity=None)
if args.recurrent == 'gru':
policy = CategoricalGRUPolicy(env_spec=env.spec, feature_network=feature_network,
hidden_dim=int(args.policy_hidden_sizes))
elif args.conv:
feature_network = ConvNetwork(
input_shape=env.spec.observation_space.shape,
output_dim=5,
conv_filters=(8,16,16),
conv_filter_sizes=(3,3,3),
conv_strides=(1,1,1),
conv_pads=('valid','valid','valid'),
hidden_sizes=(64,),
hidden_nonlinearity=NL.rectify,
output_nonlinearity=NL.softmax)
policy = CategoricalMLPPolicy(env_spec=env.spec, prob_network=feature_network)
else:
policy = CategoricalMLPPolicy(env_spec=env.spec, hidden_sizes=args.hidden_sizes)
if args.baseline_type == 'linear':
baseline = LinearFeatureBaseline(env_spec=env.spec)
else:
baseline = ZeroBaseline(obsfeat_space)
# logger
default_log_dir = config.LOG_DIR
if args.log_dir is None:
log_dir = osp.join(default_log_dir, args.exp_name)
else:
log_dir = args.log_dir
tabular_log_file = osp.join(log_dir, args.tabular_log_file)
text_log_file = osp.join(log_dir, args.text_log_file)
params_log_file = osp.join(log_dir, args.params_log_file)
logger.log_parameters_lite(params_log_file, args)
logger.add_text_output(text_log_file)
logger.add_tabular_output(tabular_log_file)
prev_snapshot_dir = logger.get_snapshot_dir()
prev_mode = logger.get_snapshot_mode()
logger.set_snapshot_dir(log_dir)
logger.set_snapshot_mode(args.snapshot_mode)
logger.set_log_tabular_only(args.log_tabular_only)
logger.push_prefix("[%s] " % args.exp_name)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=args.n_timesteps,
max_path_length=args.max_traj_len,
n_itr=args.n_iter,
discount=args.discount,
gae_lambda=args.gae_lambda,
step_size=args.max_kl,
mode=args.control,)
algo.train()
from sandbox.rocky.tf.policies.deterministic_mlp_policy import \
DeterministicMLPPolicy
from sandbox.rocky.tf.q_functions.continuous_mlp_q_function import \
ContinuousMLPQFunction
parser = argparse.ArgumentParser()
parser.add_argument("env", help="The environment name from OpenAIGym environments")
parser.add_argument("--num_epochs", default=250, type=int)
parser.add_argument("--data_dir", default="./data_ddpg/")
parser.add_argument("--reward_scale", default=1.0, type=float)
parser.add_argument("--use_ec2", action="store_true", help="Use your ec2 instances if configured")
parser.add_argument("--dont_terminate_machine", action="store_false", help="Whether to terminate your spot instance or not. Be careful.")
args = parser.parse_args()
stub(globals())
ext.set_seed(1)
gymenv = GymEnv(args.env, force_reset=True, record_video=True, record_log=True)
env = TfEnv(normalize(gymenv))
policy = DeterministicMLPPolicy(
env_spec=env.spec,
name="policy",
# The neural network policy should have two hidden layers, each with 32 hidden units.
hidden_sizes=(100, 50, 25),
hidden_nonlinearity=tf.nn.relu,
)
es = OUStrategy(env_spec=env.spec)
def run_experiment(argv):
default_log_dir = config.LOG_DIR
now = datetime.datetime.now(dateutil.tz.tzlocal())
# avoid name clashes when running distributed jobs
rand_id = str(uuid.uuid4())[:5]
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S_%f_%Z')
default_exp_name = 'experiment_%s_%s' % (timestamp, rand_id)
parser = argparse.ArgumentParser()
parser.add_argument('--n_parallel',
type=int,
default=1,
help='Number of parallel workers to perform rollouts.')
parser.add_argument('--exp_name',
type=str,
default=default_exp_name,
help='Name of the experiment.')
parser.add_argument('--log_dir',
type=str,
default=default_log_dir,
help='Path to save the log and iteration snapshot.')
parser.add_argument('--snapshot_mode',
type=str,
default='all',
help='Mode to save the snapshot. Can be either "all" '
'(all iterations will be saved), "last" (only '
'the last iteration will be saved), or "none" '
'(do not save snapshots)')
parser.add_argument('--snapshot_gap',
type=int,
default=1,
help='Gap between snapshot iterations.')
parser.add_argument('--tabular_log_file',
type=str,
default='progress.csv',
help='Name of the tabular log file (in csv).')
parser.add_argument('--text_log_file',
type=str,
default='debug.log',
help='Name of the text log file (in pure text).')
parser.add_argument('--params_log_file',
type=str,
default='params.json',
help='Name of the parameter log file (in json).')
parser.add_argument('--plot',
type=ast.literal_eval,
default=False,
help='Whether to plot the iteration results')
parser.add_argument(
'--log_tabular_only',
type=ast.literal_eval,
default=False,
help=
'Whether to only print the tabular log information (in a horizontal format)'
)
parser.add_argument('--seed', type=int, help='Random seed for numpy')
parser.add_argument('--args_data',
type=str,
help='Pickled data for stub objects')
parser.add_argument('--use_cloudpickle',
type=ast.literal_eval,
default=False,
help='Whether to plot the iteration results')
args = parser.parse_args(argv[1:])
if args.seed is not None:
set_seed(args.seed)
if args.n_parallel > 0:
from sandbox.vase.sampler import parallel_sampler_expl as parallel_sampler
parallel_sampler.initialize(n_parallel=args.n_parallel)
if args.seed is not None:
set_seed(args.seed)
parallel_sampler.set_seed(args.seed)
if args.plot:
from rllab.plotter import plotter
plotter.init_worker()
# read from stdin
data = pickle.loads(base64.b64decode(args.args_data))
log_dir = args.log_dir
# exp_dir = osp.join(log_dir, args.exp_name)
tabular_log_file = osp.join(log_dir, args.tabular_log_file)
text_log_file = osp.join(log_dir, args.text_log_file)
params_log_file = osp.join(log_dir, args.params_log_file)
logger.log_parameters_lite(params_log_file, args)
logger.add_text_output(text_log_file)
logger.add_tabular_output(tabular_log_file)
prev_snapshot_dir = logger.get_snapshot_dir()
prev_mode = logger.get_snapshot_mode()
logger.set_snapshot_gap(args.snapshot_gap)
logger.set_snapshot_dir(log_dir)
logger.set_snapshot_mode(args.snapshot_mode)
logger.set_log_tabular_only(args.log_tabular_only)
logger.push_prefix("[%s] " % args.exp_name)
maybe_iter = concretize(data)
if is_iterable(maybe_iter):
for _ in maybe_iter:
pass
logger.set_snapshot_mode(prev_mode)
logger.set_snapshot_dir(prev_snapshot_dir)
logger.remove_tabular_output(tabular_log_file)
logger.remove_text_output(text_log_file)
logger.pop_prefix()
def main():
now = datetime.datetime.now(dateutil.tz.tzlocal())
rand_id = str(uuid.uuid4())[:5]
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S_%f_%Z')
default_exp_name = 'experiment_%s_%s' % (timestamp, rand_id)
parser = argparse.ArgumentParser()
parser.add_argument('--exp_name', type=str, default=default_exp_name,
help='Name of the experiment.')
parser.add_argument('--discount', type=float, default=0.95)
parser.add_argument('--gae_lambda', type=float, default=0.99)
parser.add_argument('--reward_scale', type=float, default=1.0)
parser.add_argument('--enable_obsnorm', action='store_true', default=False)
parser.add_argument('--chunked', action='store_true', default=False)
parser.add_argument('--n_iter', type=int, default=250)
parser.add_argument('--sampler_workers', type=int, default=1)
parser.add_argument('--max_traj_len', type=int, default=250)
parser.add_argument('--update_curriculum', action='store_true', default=False)
parser.add_argument('--anneal_step_size', type=int, default=0)
parser.add_argument('--n_timesteps', type=int, default=8000)
parser.add_argument('--control', type=str, default='centralized')
parser.add_argument('--buffer_size', type=int, default=1)
parser.add_argument('--radius', type=float, default=0.015)
parser.add_argument('--n_evaders', type=int, default=10)
parser.add_argument('--n_pursuers', type=int, default=8)
parser.add_argument('--n_poison', type=int, default=10)
parser.add_argument('--n_coop', type=int, default=4)
parser.add_argument('--n_sensors', type=int, default=30)
parser.add_argument('--sensor_range', type=str, default='0.2')
parser.add_argument('--food_reward', type=float, default=5)
parser.add_argument('--poison_reward', type=float, default=-1)
parser.add_argument('--encounter_reward', type=float, default=0.05)
parser.add_argument('--reward_mech', type=str, default='local')
parser.add_argument('--recurrent', type=str, default=None)
parser.add_argument('--baseline_type', type=str, default='linear')
parser.add_argument('--policy_hidden_sizes', type=str, default='128,128')
parser.add_argument('--baseline_hidden_sizes', type=str, default='128,128')
parser.add_argument('--max_kl', type=float, default=0.01)
parser.add_argument('--log_dir', type=str, required=False)
parser.add_argument('--tabular_log_file', type=str, default='progress.csv',
help='Name of the tabular log file (in csv).')
parser.add_argument('--text_log_file', type=str, default='debug.log',
help='Name of the text log file (in pure text).')
parser.add_argument('--params_log_file', type=str, default='params.json',
help='Name of the parameter log file (in json).')
parser.add_argument('--seed', type=int, help='Random seed for numpy')
parser.add_argument('--args_data', type=str, help='Pickled data for stub objects')
parser.add_argument('--snapshot_mode', type=str, default='all',
help='Mode to save the snapshot. Can be either "all" '
'(all iterations will be saved), "last" (only '
'the last iteration will be saved), or "none" '
'(do not save snapshots)')
parser.add_argument(
'--log_tabular_only', type=ast.literal_eval, default=False,
help='Whether to only print the tabular log information (in a horizontal format)')
args = parser.parse_args()
parallel_sampler.initialize(n_parallel=args.sampler_workers)
if args.seed is not None:
set_seed(args.seed)
parallel_sampler.set_seed(args.seed)
args.hidden_sizes = tuple(map(int, args.policy_hidden_sizes.split(',')))
centralized = True if args.control == 'centralized' else False
sensor_range = np.array(map(float, args.sensor_range.split(',')))
if len(sensor_range) == 1:
sensor_range = sensor_range[0]
else:
assert sensor_range.shape == (args.n_pursuers,)
env = MAWaterWorld(args.n_pursuers, args.n_evaders, args.n_coop, args.n_poison,
radius=args.radius, n_sensors=args.n_sensors, food_reward=args.food_reward,
poison_reward=args.poison_reward, encounter_reward=args.encounter_reward,
reward_mech=args.reward_mech, sensor_range=sensor_range, obstacle_loc=None)
env = TfEnv(
RLLabEnv(
StandardizedEnv(env, scale_reward=args.reward_scale,
enable_obsnorm=args.enable_obsnorm), mode=args.control))
if args.buffer_size > 1:
env = ObservationBuffer(env, args.buffer_size)
if args.recurrent:
feature_network = MLP(
name='feature_net',
input_shape=(env.spec.observation_space.flat_dim + env.spec.action_space.flat_dim,),
output_dim=16, hidden_sizes=(128, 64, 32), hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None)
if args.recurrent == 'gru':
policy = GaussianGRUPolicy(env_spec=env.spec, feature_network=feature_network,
hidden_dim=int(args.policy_hidden_sizes), name='policy')
elif args.recurrent == 'lstm':
policy = GaussianLSTMPolicy(env_spec=env.spec, feature_network=feature_network,
hidden_dim=int(args.policy_hidden_sizes), name='policy')
else:
policy = GaussianMLPPolicy(
name='policy', env_spec=env.spec,
hidden_sizes=tuple(map(int, args.policy_hidden_sizes.split(','))), min_std=10e-5)
if args.baseline_type == 'linear':
baseline = LinearFeatureBaseline(env_spec=env.spec)
elif args.baseline_type == 'mlp':
raise NotImplementedError()
# baseline = GaussianMLPBaseline(
# env_spec=env.spec, hidden_sizes=tuple(map(int, args.baseline_hidden_sizes.split(','))))
else:
baseline = ZeroBaseline(env_spec=env.spec)
# logger
default_log_dir = config.LOG_DIR
if args.log_dir is None:
log_dir = osp.join(default_log_dir, args.exp_name)
else:
log_dir = args.log_dir
tabular_log_file = osp.join(log_dir, args.tabular_log_file)
text_log_file = osp.join(log_dir, args.text_log_file)
params_log_file = osp.join(log_dir, args.params_log_file)
logger.log_parameters_lite(params_log_file, args)
logger.add_text_output(text_log_file)
logger.add_tabular_output(tabular_log_file)
prev_snapshot_dir = logger.get_snapshot_dir()
prev_mode = logger.get_snapshot_mode()
logger.set_snapshot_dir(log_dir)
logger.set_snapshot_mode(args.snapshot_mode)
logger.set_log_tabular_only(args.log_tabular_only)
logger.push_prefix("[%s] " % args.exp_name)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=args.n_timesteps,
max_path_length=args.max_traj_len,
#max_path_length_limit=args.max_path_length_limit,
update_max_path_length=args.update_curriculum,
anneal_step_size=args.anneal_step_size,
n_itr=args.n_iter,
discount=args.discount,
gae_lambda=args.gae_lambda,
step_size=args.max_kl,
optimizer=ConjugateGradientOptimizer(hvp_approach=FiniteDifferenceHvp(base_eps=1e-5)) if
args.recurrent else None,
mode=args.control if not args.chunked else 'chunk_{}'.format(args.control),)
algo.train()
def run_experiment(argv):
default_log_dir = config.LOG_DIR
now = datetime.datetime.now(dateutil.tz.tzlocal())
# avoid name clashes when running distributed jobs
rand_id = str(uuid.uuid4())[:5]
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S_%f_%Z')
default_exp_name = 'experiment_%s_%s' % (timestamp, rand_id)
parser = argparse.ArgumentParser()
parser.add_argument('--n_parallel', type=int, default=1,
help='Number of parallel workers to perform rollouts.')
parser.add_argument(
'--exp_name', type=str, default=default_exp_name, help='Name of the experiment.')
parser.add_argument('--log_dir', type=str, default=default_log_dir,
help='Path to save the log and iteration snapshot.')
parser.add_argument('--snapshot_mode', type=str, default='all',
help='Mode to save the snapshot. Can be either "all" '
'(all iterations will be saved), "last" (only '
'the last iteration will be saved), or "none" '
'(do not save snapshots)')
parser.add_argument('--tabular_log_file', type=str, default='progress.csv',
help='Name of the tabular log file (in csv).')
parser.add_argument('--text_log_file', type=str, default='debug.log',
help='Name of the text log file (in pure text).')
parser.add_argument('--params_log_file', type=str, default='params.json',
help='Name of the parameter log file (in json).')
parser.add_argument('--plot', type=ast.literal_eval, default=False,
help='Whether to plot the iteration results')
parser.add_argument('--log_tabular_only', type=ast.literal_eval, default=False,
help='Whether to only print the tabular log information (in a horizontal format)')
parser.add_argument('--seed', type=int,
help='Random seed for numpy')
parser.add_argument('--args_data', type=str,
help='Pickled data for stub objects')
args = parser.parse_args(argv[1:])
from sandbox.vime.sampler import parallel_sampler_expl as parallel_sampler
parallel_sampler.initialize(n_parallel=args.n_parallel)
if args.seed is not None:
set_seed(args.seed)
parallel_sampler.set_seed(args.seed)
if args.plot:
from rllab.plotter import plotter
plotter.init_worker()
# read from stdin
data = pickle.loads(base64.b64decode(args.args_data))
log_dir = args.log_dir
# exp_dir = osp.join(log_dir, args.exp_name)
tabular_log_file = osp.join(log_dir, args.tabular_log_file)
text_log_file = osp.join(log_dir, args.text_log_file)
params_log_file = osp.join(log_dir, args.params_log_file)
logger.log_parameters_lite(params_log_file, args)
logger.add_text_output(text_log_file)
logger.add_tabular_output(tabular_log_file)
prev_snapshot_dir = logger.get_snapshot_dir()
prev_mode = logger.get_snapshot_mode()
logger.set_snapshot_dir(log_dir)
logger.set_snapshot_mode(args.snapshot_mode)
logger.set_log_tabular_only(args.log_tabular_only)
logger.push_prefix("[%s] " % args.exp_name)
maybe_iter = concretize(data)
if is_iterable(maybe_iter):
for _ in maybe_iter:
pass
logger.set_snapshot_mode(prev_mode)
logger.set_snapshot_dir(prev_snapshot_dir)
logger.remove_tabular_output(tabular_log_file)
logger.remove_text_output(text_log_file)
logger.pop_prefix()
def run_experiment(argv):
default_log_dir = config.LOG_DIR
now = datetime.datetime.now(dateutil.tz.tzlocal())
# avoid name clashes when running distributed jobs
rand_id = str(uuid.uuid4())[:5]
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S_%f_%Z')
default_exp_name = 'experiment_%s_%s' % (timestamp, rand_id)
parser = argparse.ArgumentParser()
parser.add_argument(
'--n_parallel',
type=int,
default=1,
help=
'Number of parallel workers to perform rollouts. 0 => don\'t start any workers'
)
parser.add_argument('--exp_name',
type=str,
default=default_exp_name,
help='Name of the experiment.')
parser.add_argument('--log_dir',
type=str,
default=None,
help='Path to save the log and iteration snapshot.')
parser.add_argument('--snapshot_mode',
type=str,
default='all',
help='Mode to save the snapshot. Can be either "all" '
'(all iterations will be saved), "last" (only '
'the last iteration will be saved), "gap" (every'
'`snapshot_gap` iterations are saved), or "none" '
'(do not save snapshots)')
parser.add_argument('--snapshot_gap',
type=int,
default=1,
help='Gap between snapshot iterations.')
parser.add_argument('--tabular_log_file',
type=str,
default='progress.csv',
help='Name of the tabular log file (in csv).')
parser.add_argument('--text_log_file',
type=str,
default='debug.log',
help='Name of the text log file (in pure text).')
parser.add_argument('--params_log_file',
type=str,
default='params.json',
help='Name of the parameter log file (in json).')
parser.add_argument('--variant_log_file',
type=str,
default='variant.json',
help='Name of the variant log file (in json).')
parser.add_argument(
'--resume_from',
type=str,
default=None,
help='Name of the pickle file to resume experiment from.')
parser.add_argument('--plot',
type=ast.literal_eval,
default=False,
help='Whether to plot the iteration results')
parser.add_argument(
'--log_tabular_only',
type=ast.literal_eval,
default=False,
help=
'Whether to only print the tabular log information (in a horizontal format)'
)
parser.add_argument('--seed', type=int, help='Random seed for numpy')
parser.add_argument('--args_data',
type=str,
help='Pickled data for stub objects')
parser.add_argument('--variant_data',
type=str,
help='Pickled data for variant configuration')
parser.add_argument('--use_cloudpickle',
type=ast.literal_eval,
default=False)
args = parser.parse_args(argv[1:])
if args.seed is not None:
set_seed(args.seed)
if args.n_parallel > 0:
from rllab.sampler import parallel_sampler
parallel_sampler.initialize(n_parallel=args.n_parallel)
if args.seed is not None:
parallel_sampler.set_seed(args.seed)
if args.plot:
from rllab.plotter import plotter
plotter.init_worker()
if args.log_dir is None:
log_dir = osp.join(default_log_dir, args.exp_name)
else:
log_dir = args.log_dir
tabular_log_file = osp.join(log_dir, args.tabular_log_file)
text_log_file = osp.join(log_dir, args.text_log_file)
params_log_file = osp.join(log_dir, args.params_log_file)
if args.variant_data is not None:
variant_data = pickle.loads(base64.b64decode(args.variant_data))
variant_log_file = osp.join(log_dir, args.variant_log_file)
logger.log_variant(variant_log_file, variant_data)
else:
variant_data = None
if not args.use_cloudpickle:
logger.log_parameters_lite(params_log_file, args)
logger.add_text_output(text_log_file)
logger.add_tabular_output(tabular_log_file)
prev_snapshot_dir = logger.get_snapshot_dir()
prev_mode = logger.get_snapshot_mode()
logger.set_snapshot_dir(log_dir)
logger.set_snapshot_mode(args.snapshot_mode)
logger.set_snapshot_gap(args.snapshot_gap)
logger.set_log_tabular_only(args.log_tabular_only)
logger.push_prefix("[%s] " % args.exp_name)
#variant_data is the variant dictionary sent from trpoTests_ExpLite
if (args.resume_from is not None) and (
'&|&' in args.resume_from
): #separate string on &|& to get iters and file location
vals = args.resume_from.split(
'&|&') #dirRes | numItrs to go | new batchSize
dirRes = vals[0]
numItrs = int(vals[1])
if (len(vals) > 2):
batchSize = int(vals[2])
print("resuming from :{}".format(dirRes))
data = joblib.load(dirRes)
#data is dict : 'baseline', 'algo', 'itr', 'policy', 'env'
assert 'algo' in data
algo = data['algo']
assert 'policy' in data
pol = data['policy']
bl = data['baseline']
oldBatchSize = algo.batch_size
algo.n_itr = numItrs
if (len(vals) > 2):
algo.batch_size = batchSize
print(
'algo iters : {} cur iter :{} oldBatchSize : {} newBatchSize : {}'
.format(algo.n_itr, algo.current_itr, oldBatchSize,
algo.batch_size))
else:
print('algo iters : {} cur iter :{} '.format(
algo.n_itr, algo.current_itr))
algo.train()
else:
print('Not resuming - building new exp')
# read from stdin
if args.use_cloudpickle: #set to use cloudpickle
import cloudpickle
method_call = cloudpickle.loads(base64.b64decode(args.args_data))
method_call(variant_data)
else:
print('not use cloud pickle')
data = pickle.loads(base64.b64decode(args.args_data))
maybe_iter = concretize(data)
if is_iterable(maybe_iter):
for _ in maybe_iter:
pass
logger.set_snapshot_mode(prev_mode)
logger.set_snapshot_dir(prev_snapshot_dir)
logger.remove_tabular_output(tabular_log_file)
logger.remove_text_output(text_log_file)
logger.pop_prefix()