def logger_context(log_dir,
name,
run_ID,
log_params=None,
snapshot_mode="none"):
logger.set_snapshot_mode(snapshot_mode)
logger.set_log_tabular_only(False)
abs_log_dir = os.path.abspath(log_dir)
if LOG_DIR != os.path.commonpath([abs_log_dir, LOG_DIR]):
print(
"logger_context received log_dir outside of rllab.config.LOG_DIR: "
"prepending by {}/local/<yyyymmdd>/".format(LOG_DIR))
abs_log_dir = make_log_dir(log_dir)
exp_dir = os.path.join(abs_log_dir, "{}_{}".format(name, run_ID))
tabular_log_file = os.path.join(exp_dir, "progress.csv")
text_log_file = os.path.join(exp_dir, "debug.log")
params_log_file = os.path.join(exp_dir, "params.json")
logger.set_snapshot_dir(exp_dir)
logger.add_text_output(text_log_file)
logger.add_tabular_output(tabular_log_file)
logger.push_prefix("{}_{} ".format(name, run_ID))
if log_params is None:
log_params = dict()
log_params["name"] = name
log_params["run_ID"] = run_ID
with open(params_log_file, "w") as f:
json.dump(log_params, f)
yield
logger.remove_tabular_output(tabular_log_file)
logger.remove_text_output(text_log_file)
logger.pop_prefix()
def __init__(self, algo, args, exp_name):
self.args = args
self.algo = algo
env = algo.env
baseline = algo.baseline
# Logger
default_log_dir = config.LOG_DIR
if args.log_dir is None:
log_dir = osp.join(default_log_dir, 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)
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] " % exp_name)
prev_snapshot_dir = logger.get_snapshot_dir()
prev_mode = logger.get_snapshot_mode()
def eval_mdp_policies(horizon=15, n_traj=100000, log_dir=None):
text_output_file = None if log_dir is None else osp.join(log_dir, "text")
w_env = FrozenLakeEnv(horizon=horizon)
if text_output_file is not None:
logger.add_text_output(text_output_file)
for human_policy in human_mdp_policies.values():
logger.log("-------------------")
logger.log("Evaluating {} for {} timesteps".format(
human_policy.__name__, horizon))
logger.log("-------------------")
test_pi_H = human_policy(w_env)
test_env = HumanCRLWrapper(w_env, test_pi_H)
logger.log("Obtaining Samples...")
rewards = []
for i in pyprind.prog_bar(range(n_traj)):
observation = test_env.reset()
for t in range(horizon):
# _, action = observation
# if action == test_env.nA:
action = test_env.nA - 1
observation, reward, done, info = test_env.step(action)
if done:
rewards.append(info["accumulated rewards"])
break
#feel free to add more data
logger.log("NumTrajs {}".format(n_traj))
logger.log("AverageReturn {}".format(np.mean(rewards)))
logger.log("StdReturn {}".format(np.std(rewards)))
logger.log("MaxReturn {}".format(np.max(rewards)))
logger.log("MinReturn {}".format(np.min(rewards)))
def eval_mab_policies(n_arms=4,
horizon=15,
n_traj=1000,
log_dir=None,
turntaking=False):
text_output_file = None if log_dir is None else osp.join(log_dir, "text")
rag = uniform_bernoulli_iterator()
bandit = BanditEnv(n_arms=n_arms,
reward_dist=bernoulli,
reward_args_generator=rag,
horizon=horizon)
if text_output_file is not None:
logger.add_text_output(text_output_file)
for human_policy in [human_policy_dict['ucl']
]: # human_policy_dict.values():
# for i in range(10000):
# logger.log("Filler")
logger.log("-------------------")
logger.log("Evaluating {} for {} timesteps".format(
human_policy.__name__, horizon))
logger.log("-------------------")
test_pi_H = human_policy(bandit)
if turntaking:
test_env = HumanIterativeWrapper(bandit, test_pi_H)
else:
test_env = HumanCRLWrapper(bandit, test_pi_H, 0)
logger.log("Obtaining Samples...")
# Alas, the rllab samplers don't support hot swapping envs and batch sizes
# TODO: write a new parallel sampler, instead of sampling manually
rewards = []
for i in pyprind.prog_bar(range(n_traj)):
if turntaking:
act_counts = [0 for i in range(bandit.nA)]
observation = test_env.reset()
action = test_env.nA - 1
for t in range(horizon):
observation, reward, done, info = test_env.step(action)
if turntaking:
a_H = observation[1]
if a_H < bandit.nA:
act_counts[a_H] += 1
action = np.argmax(act_counts)
if done:
rewards.append(info["accumulated rewards"])
break
#feel free to add more data
logger.log("NumTrajs {}".format(n_traj))
logger.log("AverageReturn {}".format(np.mean(rewards)))
logger.log("StdReturn {}".format(np.std(rewards)))
logger.log("MaxReturn {}".format(np.max(rewards)))
logger.log("MinReturn {}".format(np.min(rewards)))
if text_output_file is not None:
logger.remove_text_output(text_output_file)
def setup_rllab_logging(vv):
log_dir = trajlogger.get_snapshot_dir()
tabular_log_file = osp.join(log_dir, 'rllprogress.csv')
text_log_file = osp.join(log_dir, 'rlldebug.log')
rllablogger.add_text_output(text_log_file)
rllablogger.add_tabular_output(tabular_log_file)
rllablogger.set_snapshot_dir(log_dir)
rllablogger.set_snapshot_mode("gap")
rllablogger.set_snapshot_gap(10)
rllablogger.set_log_tabular_only(False)
rllablogger.push_prefix("[%s] " % vv['exp_dir'])
return log_dir
def setup_logger(exp_name=''):
# Logging info
now = datetime.datetime.now()
exp_name = 'Exp' + now.strftime("%y%m%d_") + exp_name
n = 0
while osp.exists('./data/' + exp_name + '_' + str(n)):
n = n + 1
exp_name = exp_name + '_' + str(n)
log_dir = './data/' + exp_name
logger.add_text_output(osp.join(log_dir, 'debug.log'))
logger.add_tabular_output(osp.join(log_dir, 'progress.csv'))
logger.push_prefix("[%s] " % exp_name)
return log_dir
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 set_up_experiment(exp_name,
phase,
exp_home='../../data/experiments/',
snapshot_gap=5):
maybe_mkdir(exp_home)
exp_dir = os.path.join(exp_home, exp_name)
maybe_mkdir(exp_dir)
phase_dir = os.path.join(exp_dir, phase)
maybe_mkdir(phase_dir)
log_dir = os.path.join(phase_dir, 'log')
maybe_mkdir(log_dir)
logger.set_snapshot_dir(log_dir)
logger.set_snapshot_mode('gap')
logger.set_snapshot_gap(snapshot_gap)
log_filepath = os.path.join(log_dir, 'log.txt')
logger.add_text_output(log_filepath)
return exp_dir
def setup_logger(
exp_prefix=None,
exp_count=0,
seed=0,
variant=None,
log_dir=None,
text_log_file="debug.log",
variant_log_file="variant.json",
tabular_log_file="progress.csv",
snapshot_mode="last",
log_tabular_only=False,
snapshot_gap=1,
):
"""
Set up logger to have some reasonable default settings.
:param exp_prefix:
:param exp_count:
:param seed: Experiment seed
:param variant:
:param log_dir:
:param text_log_file:
:param variant_log_file:
:param tabular_log_file:
:param snapshot_mode:
:param log_tabular_only:
:param snapshot_gap:
:return:
"""
if log_dir is None:
assert exp_prefix is not None
log_dir = create_log_dir(exp_prefix, exp_count=exp_count, seed=seed)
tabular_log_path = osp.join(log_dir, tabular_log_file)
text_log_path = osp.join(log_dir, text_log_file)
if variant is not None:
variant_log_path = osp.join(log_dir, variant_log_file)
logger.log_variant(variant_log_path, variant)
logger.add_text_output(text_log_path)
logger.add_tabular_output(tabular_log_path)
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)
def find_optimal_epsilon(n_arms=4, horizon=15, n_traj=10000, log_dir=None):
text_output_file = None if log_dir is None else osp.join(log_dir, "text")
rag = uniform_bernoulli_iterator()
bandit = BanditEnv(n_arms=n_arms,
reward_dist=bernoulli,
reward_args_generator=rag,
horizon=horizon)
if text_output_file is not None:
logger.add_text_output(text_output_file)
for epsilon in candidate_epsilons:
# for i in range(10000):
# logger.log("Filler")
logger.log("-------------------")
logger.log("Evaluating epsilon={} for {} timesteps".format(
epsilon, horizon))
logger.log("-------------------")
test_pi_H = EpsGreedyBanditPolicy(bandit, epsilon=epsilon)
test_env = HumanCRLWrapper(bandit, test_pi_H, 0)
logger.log("Obtaining Samples...")
# Alas, the rllab samplers don't support hot swapping envs and batch sizes
# TODO: write a new parallel sampler, instead of sampling manually
rewards = []
for i in pyprind.prog_bar(range(n_traj)):
observation = test_env.reset()
for t in range(horizon):
action = test_env.nA - 1
observation, reward, done, info = test_env.step(action)
if done:
rewards.append(info["accumulated rewards"])
break
#feel free to add more data
logger.log("NumTrajs {}".format(n_traj))
logger.log("AverageReturn {}".format(np.mean(rewards)))
logger.log("StdReturn {}".format(np.std(rewards)))
logger.log("MaxReturn {}".format(np.max(rewards)))
logger.log("MinReturn {}".format(np.min(rewards)))
if text_output_file is not None:
logger.remove_text_output(text_output_file)
def setup_logging(log_dir, algo, env, novice, expert, DR):
tabular_log_file = os.path.join(log_dir, "progress.csv")
text_log_file = os.path.join(log_dir, "debug.log")
params_log_file = os.path.join(log_dir, "params.json")
snapshot_mode = "last"
snapshot_gap = 1
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)
print("Finished setting up logging.")
# log some stuff
logger.log("Created algorithm object of type: %s", type(algo))
logger.log("env of type: %s" % type(env))
logger.log("novice of type: %s" % type(novice))
logger.log("expert of type: %s" % type(expert))
logger.log("decision_rule of type: %s" % type(DR))
logger.log("DAgger beta decay: %s" % DR.beta_decay)
logger.log("numtrajs per epoch/itr: %s" % algo.numtrajs)
logger.log("n_iter: %s" % algo.n_itr)
logger.log("max path length: %s" % algo.max_path_length)
logger.log("Optimizer info - ")
logger.log("Optimizer of type: %s" % type(algo.optimizer))
if type(algo.optimizer) == FirstOrderOptimizer:
logger.log("Optimizer of class: %s" %
type(algo.optimizer._tf_optimizer))
logger.log("optimizer learning rate: %s" %
algo.optimizer._tf_optimizer._lr)
logger.log("optimizer max epochs: %s" % algo.optimizer._max_epochs)
logger.log("optimizer batch size: %s" % algo.optimizer._batch_size)
elif type(algo.optimizer) == PenaltyLbfgsOptimizer:
logger.log("initial_penalty %s" % algo.optimizer._initial_penalty)
logger.log("max_opt_itr %s" % algo.optimizer._max_opt_itr)
logger.log("max_penalty %s" % algo.optimizer._max_penalty)
return True
def setup_output(output_dir, clean=True, final_output_dir=None):
global OUTPUT_DIR
global FINAL_OUTPUT_DIR
if OUTPUT_DIR is not None:
shutdown_output()
output_dir = os.path.abspath(output_dir)
if clean:
ensure_clean_output_dir(output_dir)
else:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
OUTPUT_DIR = output_dir
FINAL_OUTPUT_DIR = final_output_dir
print("** Output set to", OUTPUT_DIR)
if FINAL_OUTPUT_DIR is not None:
print("** Final output set to", FINAL_OUTPUT_DIR)
logger.add_text_output(os.path.join(OUTPUT_DIR, "rllab.txt"))
logger.add_tabular_output(os.path.join(OUTPUT_DIR, "rllab.csv"))
logger.set_snapshot_mode('all') # options: 'none', 'last', or 'all'
logger.set_snapshot_dir(OUTPUT_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)
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("envs", nargs='+', help="The list of environments to train on in order. Eval rollouts will be run on all environments at the end.")
parser.add_argument("--num_epochs", default=100, type=int, help="Number of epochs to run.")
parser.add_argument("--num_final_rollouts", default=20, type=int, help="Number of rollouts to run on final evaluation of environments.")
parser.add_argument("--batch_size", default=25000, type=int, help="Batch_size per epoch (this is the number of (state, action) samples, not the number of rollouts)")
parser.add_argument("--step_size", default=0.01, type=float, help="Step size for TRPO (i.e. the maximum KL bound)")
parser.add_argument("--reg_coeff", default=1e-5, type=float, help="Regularization coefficient for TRPO")
parser.add_argument("--text_log_file", default="./data/debug.log", help="Where text output will go")
parser.add_argument("--tabular_log_file", default="./data/progress.csv", help="Where tabular output will go")
args = parser.parse_args()
# stub(globals())
# ext.set_seed(1)
logger.add_text_output(args.text_log_file)
logger.add_tabular_output(args.tabular_log_file)
logger.set_log_tabular_only(False)
envs = []
for env_name in args.envs:
gymenv = GymEnv(env_name, force_reset=True, record_video=False, record_log=False)
env = TfEnv(normalize(gymenv))
envs.append((env_name, env))
policy = GaussianMLPPolicy(
name="policy",
env_spec=env.spec,
# The neural network policy should have two hidden layers, each with 32 hidden units.
hidden_sizes=(100, 50, 25),
import uuid
import base64
import joblib
default_log_dir = '/home/sliay/Documents/rllab/data/local/experiment'
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)
log_dir = os.path.join(default_log_dir, default_exp_name)
tabular_log_file = os.path.join(log_dir, 'progress.csv')
text_log_file = os.path.join(log_dir, 'debug.log')
params_log_file = os.path.join(log_dir, 'params.json')
logger.add_text_output(text_log_file)
logger.add_tabular_output(tabular_log_file)
logger.set_snapshot_dir(log_dir)
logger.set_snapshot_mode('last')
logger.set_log_tabular_only(False)
logger.push_prefix("[%s] " % default_exp_name)
last_snapshot_dir = '/home/sliay/Documents/rllab/data/local/experiment/experiment_2016_07_07_498itr'
data = joblib.load(os.path.join(last_snapshot_dir, 'params.pkl'))
policy = data['policy']
env = data['env']
baseline = data['baseline']
# env = normalize(GymEnv("VREP-v0", record_video=False))
# policy = GaussianMLPPolicy(
# env_spec=env.spec,
def run_task(v, num_cpu=8, log_dir="./data", ename=None, **kwargs):
from scipy.stats import bernoulli, uniform, beta
import tensorflow as tf
from assistive_bandits.experiments.l2_rnn_baseline import L2RNNBaseline
from assistive_bandits.experiments.tbptt_optimizer import TBPTTOptimizer
from sandbox.rocky.tf.policies.categorical_gru_policy import CategoricalGRUPolicy
from assistive_bandits.experiments.pposgd_clip_ratio import PPOSGD
if not local_test and force_remote:
import rl_algs.logger as rl_algs_logger
log_dir = rl_algs_logger.get_dir()
if log_dir is not None:
log_dir = osp.join(log_dir, str(v["n_episodes"]))
log_dir = osp.join(log_dir, v["human_policy"])
text_output_file = None if log_dir is None else osp.join(log_dir, "text")
tabular_output_file = None if log_dir is None else osp.join(
log_dir, "train_table.csv")
info_theory_tabular_output = None if log_dir is None else osp.join(
log_dir, "info_table.csv")
rag = uniform_bernoulli_iterator()
bandit = BanditEnv(n_arms=v["n_arms"],
reward_dist=bernoulli,
reward_args_generator=rag,
horizon=v["n_episodes"])
pi_H = human_policy_dict[v["human_policy"]](bandit)
h_wrapper = human_wrapper_dict[v["human_wrapper"]]
env = h_wrapper(bandit, pi_H, penalty=v["intervention_penalty"])
if text_output_file is not None:
logger.add_text_output(text_output_file)
logger.add_tabular_output(tabular_output_file)
logger.log("Training against {}".format(v["human_policy"]))
logger.log("Setting seed to {}".format(v["seed"]))
env.seed(v["seed"])
baseline = L2RNNBaseline(
name="vf",
env_spec=env.spec,
log_loss_before=False,
log_loss_after=False,
hidden_nonlinearity=getattr(tf.nn, v["nonlinearity"]),
weight_normalization=v["weight_normalization"],
layer_normalization=v["layer_normalization"],
state_include_action=False,
hidden_dim=v["hidden_dim"],
optimizer=TBPTTOptimizer(
batch_size=v["opt_batch_size"],
n_steps=v["opt_n_steps"],
n_epochs=v["min_epochs"],
),
batch_size=v["opt_batch_size"],
n_steps=v["opt_n_steps"],
)
policy = CategoricalGRUPolicy(env_spec=env.spec,
hidden_nonlinearity=getattr(
tf.nn, v["nonlinearity"]),
hidden_dim=v["hidden_dim"],
state_include_action=True,
name="policy")
n_itr = 3 if local_test else 100
# logger.log('sampler_args {}'.format(dict(n_envs=max(1, min(int(np.ceil(v["batch_size"] / v["n_episodes"])), 100)))))
# parallel_sampler.initialize(6)
# parallel_sampler.set_seed(v["seed"])
algo = PPOSGD(
env=env,
policy=policy,
baseline=baseline,
batch_size=v["batch_size"],
max_path_length=v["n_episodes"],
# 43.65 env time
sampler_args=dict(n_envs=max(
1, min(int(np.ceil(v["batch_size"] / v["n_episodes"])), 100))),
# 100 threads -> 1:36 to sample 187.275
# 6 threads -> 1:31
# force_batch_sampler=True,
n_itr=n_itr,
step_size=v["mean_kl"],
clip_lr=v["clip_lr"],
log_loss_kl_before=False,
log_loss_kl_after=False,
use_kl_penalty=v["use_kl_penalty"],
min_n_epochs=v["min_epochs"],
entropy_bonus_coeff=v["entropy_bonus_coeff"],
optimizer=TBPTTOptimizer(
batch_size=v["opt_batch_size"],
n_steps=v["opt_n_steps"],
n_epochs=v["min_epochs"],
),
discount=v["discount"],
gae_lambda=v["gae_lambda"],
use_line_search=True
# scope=ename
)
sess = tf.Session()
with sess.as_default():
algo.train(sess)
if text_output_file is not None:
logger.remove_tabular_output(tabular_output_file)
logger.add_tabular_output(info_theory_tabular_output)
# Now gather statistics for t-tests and such!
for human_policy_name, human_policy in human_policy_dict.items():
logger.log("-------------------")
logger.log("Evaluating against {}".format(human_policy.__name__))
logger.log("-------------------")
logger.log("Obtaining Samples...")
test_pi_H = human_policy(bandit)
test_env = h_wrapper(bandit, test_pi_H, penalty=0.)
eval_sampler = VectorizedSampler(algo, n_envs=100)
algo.batch_size = v["num_eval_traj"] * v["n_episodes"]
algo.env = test_env
logger.log("algo.env.pi_H has class: {}".format(
algo.env.pi_H.__class__))
eval_sampler.start_worker()
paths = eval_sampler.obtain_samples(-1)
eval_sampler.shutdown_worker()
rewards = []
H_act_seqs = []
R_act_seqs = []
best_arms = []
optimal_a_seqs = []
for p in paths:
a_Rs = env.action_space.unflatten_n(p['actions'])
obs_R = env.observation_space.unflatten_n(p['observations'])
best_arm = np.argmax(p['env_infos']['arm_means'][0])
H_act_seqs.append(obs_R[:, 1])
R_act_seqs.append(a_Rs)
best_arms.append(best_arm)
optimal_a_seqs.append(
[best_arm for _ in range(v["n_episodes"])])
rewards.append(np.sum(p['rewards']))
#feel free to add more data
logger.log("NumTrajs {}".format(v["num_eval_traj"]))
logger.log("AverageReturn {}".format(np.mean(rewards)))
logger.log("StdReturn {}".format(np.std(rewards)))
logger.log("MaxReturn {}".format(np.max(rewards)))
logger.log("MinReturn {}".format(np.min(rewards)))
optimal_a_H_freqs = _frequency_agreement(H_act_seqs,
optimal_a_seqs)
optimal_a_R_freqs = _frequency_agreement(R_act_seqs,
optimal_a_seqs)
for t in range(v["n_episodes"]):
logger.record_tabular("PolicyExecTime", 0)
logger.record_tabular("EnvExecTime", 0)
logger.record_tabular("ProcessExecTime", 0)
logger.record_tabular("Tested Against", human_policy_name)
logger.record_tabular("t", t)
logger.record_tabular("a_H_agreement", optimal_a_H_freqs[t])
logger.record_tabular("a_R_agreement", optimal_a_R_freqs[t])
H_act_seqs_truncated = [a_Hs[0:t] for a_Hs in H_act_seqs]
R_act_seqs_truncated = [a_Rs[0:t] for a_Rs in R_act_seqs]
h_mutual_info = _mutual_info_seqs(H_act_seqs_truncated,
best_arms, v["n_arms"] + 1)
r_mutual_info = _mutual_info_seqs(R_act_seqs_truncated,
best_arms, v["n_arms"] + 1)
logger.record_tabular("h_mutual_info", h_mutual_info)
logger.record_tabular("r_mutual_info", r_mutual_info)
logger.record_tabular("a_H_opt_freq", optimal_a_H_freqs[t])
logger.record_tabular("a_R_opt_freq", optimal_a_R_freqs[t])
logger.dump_tabular()
test_env = h_wrapper(bandit, human_policy(bandit), penalty=0.)
logger.log("Printing Example Trajectories")
for i in range(v["num_display_traj"]):
observation = test_env.reset()
policy.reset()
logger.log("-- Trajectory {} of {}".format(
i + 1, v["num_display_traj"]))
logger.log("t \t obs \t act \t reward \t act_probs")
for t in range(v["n_episodes"]):
action, act_info = policy.get_action(observation)
new_obs, reward, done, info = test_env.step(action)
logger.log("{} \t {} \t {} \t {} \t {}".format(
t, observation, action, reward, act_info['prob']))
observation = new_obs
if done:
logger.log("Total reward: {}".format(
info["accumulated rewards"]))
break
if text_output_file is not None:
logger.remove_text_output(text_output_file)
logger.remove_tabular_output(info_theory_tabular_output)
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()
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 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 setup(self, env, policy, start_itr):
if not self.args.algo == 'thddpg':
# Baseline
if self.args.baseline_type == 'linear':
baseline = LinearFeatureBaseline(env_spec=env.spec)
elif self.args.baseline_type == 'zero':
baseline = ZeroBaseline(env_spec=env.spec)
else:
raise NotImplementedError(self.args.baseline_type)
if self.args.control == 'concurrent':
baseline = [baseline for _ in range(len(env.agents))]
# Logger
default_log_dir = config.LOG_DIR
if self.args.log_dir is None:
log_dir = osp.join(default_log_dir, self.args.exp_name)
else:
log_dir = self.args.log_dir
tabular_log_file = osp.join(log_dir, self.args.tabular_log_file)
text_log_file = osp.join(log_dir, self.args.text_log_file)
params_log_file = osp.join(log_dir, self.args.params_log_file)
logger.log_parameters_lite(params_log_file, self.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(self.args.snapshot_mode)
logger.set_log_tabular_only(self.args.log_tabular_only)
logger.push_prefix("[%s] " % self.args.exp_name)
if self.args.algo == 'tftrpo':
algo = MATRPO(
env=env,
policy_or_policies=policy,
baseline_or_baselines=baseline,
batch_size=self.args.batch_size,
start_itr=start_itr,
max_path_length=self.args.max_path_length,
n_itr=self.args.n_iter,
discount=self.args.discount,
gae_lambda=self.args.gae_lambda,
step_size=self.args.step_size,
optimizer=ConjugateGradientOptimizer(
hvp_approach=FiniteDifferenceHvp(
base_eps=1e-5)) if self.args.recurrent else None,
ma_mode=self.args.control)
elif self.args.algo == 'thddpg':
qfunc = thContinuousMLPQFunction(env_spec=env.spec)
if self.args.exp_strategy == 'ou':
es = OUStrategy(env_spec=env.spec)
elif self.args.exp_strategy == 'gauss':
es = GaussianStrategy(env_spec=env.spec)
else:
raise NotImplementedError()
algo = thDDPG(env=env,
policy=policy,
qf=qfunc,
es=es,
batch_size=self.args.batch_size,
max_path_length=self.args.max_path_length,
epoch_length=self.args.epoch_length,
min_pool_size=self.args.min_pool_size,
replay_pool_size=self.args.replay_pool_size,
n_epochs=self.args.n_iter,
discount=self.args.discount,
scale_reward=0.01,
qf_learning_rate=self.args.qfunc_lr,
policy_learning_rate=self.args.policy_lr,
eval_samples=self.args.eval_samples,
mode=self.args.control)
return algo
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 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 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(
args_data,
variant_data=None,
seed=None,
n_parallel=1,
exp_name=None,
log_dir=None,
snapshot_mode='all',
snapshot_gap=1,
tabular_log_file='progress.csv',
text_log_file='debug.log',
params_log_file='params.json',
variant_log_file='variant.json',
resume_from=None,
plot=False,
log_tabular_only=False,
log_debug_log_only=False,
):
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)
if exp_name is None:
exp_name = default_exp_name
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 plot:
from rllab.plotter import plotter
plotter.init_worker()
if log_dir is None:
log_dir = osp.join(default_log_dir, exp_name)
else:
log_dir = log_dir
tabular_log_file = osp.join(log_dir, tabular_log_file)
text_log_file = osp.join(log_dir, text_log_file)
params_log_file = osp.join(log_dir, params_log_file)
if variant_data is not None:
variant_data = variant_data
variant_log_file = osp.join(log_dir, variant_log_file)
# print(variant_log_file)
# print(variant_data)
logger.log_variant(variant_log_file, variant_data)
else:
variant_data = None
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.set_debug_log_only(log_debug_log_only)
logger.push_prefix("[%s] " % exp_name)
if resume_from is not None:
data = joblib.load(resume_from)
assert 'algo' in data
algo = data['algo']
algo.train()
else:
args_data(variant_data)
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('--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('--control', type=str, default='centralized')
parser.add_argument('--buffer_size', type=int, default=1)
parser.add_argument('--n_good', type=int, default=3)
parser.add_argument('--n_hostage', type=int, default=5)
parser.add_argument('--n_bad', type=int, default=5)
parser.add_argument('--n_coop_save', type=int, default=2)
parser.add_argument('--n_coop_avoid', type=int, default=2)
parser.add_argument('--n_sensors', type=int, default=20)
parser.add_argument('--sensor_range', type=float, default=0.2)
parser.add_argument('--save_reward', type=float, default=3)
parser.add_argument('--hit_reward', type=float, default=-1)
parser.add_argument('--encounter_reward', type=float, default=0.01)
parser.add_argument('--bomb_reward', type=float, default=-10.)
parser.add_argument('--recurrent', action='store_true', default=False)
parser.add_argument('--baseline_type', type=str, default='linear')
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('--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(',')))
assert sensor_range.shape == (args.n_pursuers, )
env = ContinuousHostageWorld(args.n_good,
args.n_hostage,
args.n_bad,
args.n_coop_save,
args.n_coop_avoid,
n_sensors=args.n_sensors,
sensor_range=args.sensor_range,
save_reward=args.save_reward,
hit_reward=args.hit_reward,
encounter_reward=args.encounter_reward,
bomb_reward=args.bomb_reward)
env = RLLabEnv(StandardizedEnv(env), mode=args.control)
if args.buffer_size > 1:
env = ObservationBuffer(env, args.buffer_size)
if args.recurrent:
policy = GaussianGRUPolicy(env_spec=env.spec,
hidden_sizes=args.hidden_sizes)
else:
policy = GaussianMLPPolicy(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,
step_size=args.max_kl,
mode=args.control,
)
algo.train()
def setup(self, env, policy, start_itr):
if not self.args.algo == 'thddpg':
# Baseline
if self.args.baseline_type == 'linear':
baseline = LinearFeatureBaseline(env_spec=env.spec)
elif self.args.baseline_type == 'zero':
baseline = ZeroBaseline(env_spec=env.spec)
else:
raise NotImplementedError(self.args.baseline_type)
if self.args.control == 'concurrent':
baseline = [baseline for _ in range(len(env.agents))]
# Logger
default_log_dir = config.LOG_DIR
if self.args.log_dir is None:
log_dir = osp.join(default_log_dir, self.args.exp_name)
else:
log_dir = self.args.log_dir
tabular_log_file = osp.join(log_dir, self.args.tabular_log_file)
text_log_file = osp.join(log_dir, self.args.text_log_file)
params_log_file = osp.join(log_dir, self.args.params_log_file)
logger.log_parameters_lite(params_log_file, self.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(self.args.snapshot_mode)
logger.set_log_tabular_only(self.args.log_tabular_only)
logger.push_prefix("[%s] " % self.args.exp_name)
if self.args.algo == 'tftrpo':
algo = MATRPO(env=env, policy_or_policies=policy, baseline_or_baselines=baseline,
batch_size=self.args.batch_size, start_itr=start_itr,
max_path_length=self.args.max_path_length, n_itr=self.args.n_iter,
discount=self.args.discount, gae_lambda=self.args.gae_lambda,
step_size=self.args.step_size, optimizer=ConjugateGradientOptimizer(
hvp_approach=FiniteDifferenceHvp(base_eps=1e-5)) if
self.args.recurrent else None, ma_mode=self.args.control)
elif self.args.algo == 'thddpg':
qfunc = thContinuousMLPQFunction(env_spec=env.spec)
if self.args.exp_strategy == 'ou':
es = OUStrategy(env_spec=env.spec)
elif self.args.exp_strategy == 'gauss':
es = GaussianStrategy(env_spec=env.spec)
else:
raise NotImplementedError()
algo = thDDPG(env=env, policy=policy, qf=qfunc, es=es, batch_size=self.args.batch_size,
max_path_length=self.args.max_path_length,
epoch_length=self.args.epoch_length,
min_pool_size=self.args.min_pool_size,
replay_pool_size=self.args.replay_pool_size, n_epochs=self.args.n_iter,
discount=self.args.discount, scale_reward=0.01,
qf_learning_rate=self.args.qfunc_lr,
policy_learning_rate=self.args.policy_lr,
eval_samples=self.args.eval_samples, mode=self.args.control)
return algo
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('--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('--control', type=str, default='centralized')
parser.add_argument('--buffer_size', type=int, default=1)
parser.add_argument('--n_good', type=int, default=3)
parser.add_argument('--n_hostage', type=int, default=5)
parser.add_argument('--n_bad', type=int, default=5)
parser.add_argument('--n_coop_save', type=int, default=2)
parser.add_argument('--n_coop_avoid', type=int, default=2)
parser.add_argument('--n_sensors', type=int, default=20)
parser.add_argument('--sensor_range', type=float, default=0.2)
parser.add_argument('--save_reward', type=float, default=3)
parser.add_argument('--hit_reward', type=float, default=-1)
parser.add_argument('--encounter_reward', type=float, default=0.01)
parser.add_argument('--bomb_reward', type=float, default=-10.)
parser.add_argument('--recurrent', action='store_true', default=False)
parser.add_argument('--baseline_type', type=str, default='linear')
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('--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(',')))
assert sensor_range.shape == (args.n_pursuers,)
env = ContinuousHostageWorld(args.n_good, args.n_hostage, args.n_bad, args.n_coop_save,
args.n_coop_avoid, n_sensors=args.n_sensors,
sensor_range=args.sensor_range, save_reward=args.save_reward,
hit_reward=args.hit_reward, encounter_reward=args.encounter_reward,
bomb_reward=args.bomb_reward)
env = RLLabEnv(StandardizedEnv(env), mode=args.control)
if args.buffer_size > 1:
env = ObservationBuffer(env, args.buffer_size)
if args.recurrent:
policy = GaussianGRUPolicy(env_spec=env.spec, hidden_sizes=args.hidden_sizes)
else:
policy = GaussianMLPPolicy(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,
step_size=args.max_kl,
mode=args.control,)
algo.train()
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()
prev_mode = logger.get_snapshot_mode()
logger.set_snapshot_dir(log_dir)
logger.set_snapshot_mode(snapshot_mode)
logger.set_log_tabular_only(log_tabular_only)
logger.push_prefix("[%s] " % exp_name)
############################################################
## Dumping config
with open(params_all_log_file_fullpath, 'w') as yaml_file:
yaml_file.write(yaml.dump(params, default_flow_style=False))
############################################################
## RUNNING THE EXPERIMENT
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()
eval_samples=
15000, # Number of samples (timesteps) for evaluating the policy.
discount=1.0,
scale_reward=0.1, # The scaling factor applied to the rewards when training
qf_learning_rate=1e-3, # Learning rate for training Q function
policy_learning_rate=1e-4, # Learning rate for training the policy
#qf_weight_decay=0.01,
soft_target_tau=
0.005, # Interpolation parameter for doing the soft target update.
# Uncomment both lines (this and the plot parameter below) to enable plotting
# plot=True,
)
log_dir = os.path.join(os.getcwd(), 'data')
logger.set_snapshot_dir(log_dir)
logger.add_text_output(os.path.join(log_dir, 'debug.log'))
logger.add_tabular_output(os.path.join(log_dir, 'progress.csv'))
logger.set_snapshot_mode('last')
algo.train()
# save parameters
with open(os.path.join(log_dir, 'final_policy.pkl'), 'wb') as output:
trained_policy = algo.policy
pickle.dump(trained_policy, output, pickle.HIGHEST_PROTOCOL)
print('Final policy saved')
def save_large_pickled_object(obj, filepath):
"""
This is a defensive way to write pickle.write, allowing for very large files on all platforms
grid=grid,
b0=b0,
start_state=start_state,
goal_state=goal_state))
env._wrapped_env.generate_grid = False
env._wrapped_env.generate_b0_start_goal = False
env.reset()
log_dir = "./Data/obs_1goal20step0stay_1_gru"
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")
pkl_file = osp.join(log_dir, "params.pkl")
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("gaplast")
logger.set_snapshot_gap(1000)
logger.set_log_tabular_only(False)
logger.push_prefix("[%s] " % "FixMapStartState")
from Algo import parallel_sampler
parallel_sampler.initialize(n_parallel=1)
parallel_sampler.set_seed(0)
policy = QMDPPolicy(env_spec=env.spec,
name="QMDP",
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()
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 setup(self, env, policy, start_itr):
# Baseline
if self.args.baseline_type == 'linear':
baseline = LinearFeatureBaseline(env_spec=env.spec)
elif self.args.baseline_type == 'zero':
baseline = ZeroBaseline(env_spec=env.spec)
else:
raise NotImplementedError(self.args.baseline_type)
# Logger
default_log_dir = config.LOG_DIR
if self.args.log_dir is None:
log_dir = osp.join(default_log_dir, self.args.exp_name)
else:
log_dir = self.args.log_dir
tabular_log_file = osp.join(log_dir, self.args.tabular_log_file)
text_log_file = osp.join(log_dir, self.args.text_log_file)
params_log_file = osp.join(log_dir, self.args.params_log_file)
logger.log_parameters_lite(params_log_file, self.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(self.args.snapshot_mode)
logger.set_log_tabular_only(self.args.log_tabular_only)
logger.push_prefix("[%s] " % self.args.exp_name)
if self.args.algo == 'reinforce':
algo = MAReinforce(env=env,
policy_or_policies=policy,
plot=False,
baseline_or_baselines=baseline,
batch_size=self.args.batch_size,
pause_for_plot=True,
start_itr=start_itr,
max_path_length=self.args.max_path_length,
n_itr=self.args.n_iter,
discount=self.args.discount,
gae_lambda=self.args.gae_lambda,
step_size=self.args.step_size,
ma_mode=self.args.control,
save_param_update=self.args.save_param_update)
elif self.args.algo == 'dqn':
algo = MADQN(env=env,
networks=policy,
plot=False,
batch_size=self.args.batch_size,
pause_for_plot=True,
start_itr=start_itr,
max_path_length=self.args.max_path_length,
n_itr=self.args.n_iter,
discount=self.args.discount,
ma_mode=self.args.control,
pre_trained_size=self.args.replay_pre_trained_size,
target_network_update=self.args.target_network_update,
save_param_update=self.args.save_param_update)
elif self.args.algo == 'a2c':
algo = MAA2C(env=env,
policy_or_policies=policy,
plot=False,
baseline_or_baselines=baseline,
batch_size=self.args.batch_size,
pause_for_plot=True,
start_itr=start_itr,
max_path_length=self.args.max_path_length,
n_itr=self.args.n_iter,
discount=self.args.discount,
ma_mode=self.args.control,
actor_learning_rate=self.args.policy_lr,
critic_learning_rate=self.args.qfunc_lr,
value_coefficient=0.5,
entropy_coefficient=0.01,
clip_grads=0.5,
save_param_update=self.args.save_param_update)
return algo
parser.add_argument("--reg_coeff",
default=1e-5,
type=float,
help="Regularization coefficient for TRPO")
parser.add_argument("--text_log_file",
default="./data/debug.log",
help="Where text output will go")
parser.add_argument("--tabular_log_file",
default="./data/progress.csv",
help="Where tabular output will go")
args = parser.parse_args()
# stub(globals())
# ext.set_seed(1)
logger.add_text_output(args.text_log_file)
logger.add_tabular_output(args.tabular_log_file)
logger.set_log_tabular_only(False)
envs = []
for env_name in args.envs:
gymenv = GymEnv(env_name,
force_reset=True,
record_video=False,
record_log=False)
env = TfEnv(normalize(gymenv))
envs.append((env_name, env))
policy = GaussianMLPPolicy(
name="policy",
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()