def run_sql_experiment(main, mode, include_folders=None, log_dir=None,
exp_prefix="experiment", exp_name=None, **kwargs):
if exp_name is None:
exp_name = timestamp()
if log_dir is None:
log_dir = os.path.join(
DEFAULT_LOG_DIR,
"local",
exp_prefix.replace("_", "-"),
exp_name)
if include_folders is None:
include_folders = list()
if mode == 'ec2':
include_folders.append('softqlearning')
all_symlinks = list()
for folder in include_folders:
all_symlinks.append(_create_symlink(folder))
kwargs.update(added_project_directories=all_symlinks)
run_experiment_lite(
stub_method_call=main,
mode=mode,
exp_prefix=exp_prefix,
exp_name=exp_name,
log_dir=log_dir,
**kwargs,
)
def _launch_ec2(func, exp_prefix, exp_name, params, run_experiment_kwargs):
print("Launching task", exp_name)
kwargs = dict(
n_parallel=1,
snapshot_mode="last",
seed=params.get("seed",None),
mode="ec2"
)
kwargs.update(run_experiment_kwargs)
kwargs.update(dict(
exp_prefix=exp_prefix,
exp_name=exp_name,
variant=params,
confirm_remote=False))
run_experiment_lite(func,**kwargs)
def run_experiment(**params):
base_params = copy.copy(DEFAULTS)
base_params.update(params)
params = base_params
pprint(params)
grid_world = SlaveGridWorldEnv("walled_chain",
max_traj_length=DEFAULTS["max_path_length"],
goal_reward=params["goal_reward"])
agent = GridWorldMasterAgent(grid_world, match_reward=params["match_reward"])
env = normalize(SituatedConversationEnvironment(env=grid_world, b_agent=agent))
baseline = LinearFeatureBaseline(env)
policy = RecurrentCategoricalPolicy(
name="policy",
env_spec=env.spec,
hidden_dims=params["policy_hidden_dims"],
feature_network=MLPNetworkWithEmbeddings(
"feature_network", env.observation_space.flat_dim,
params["feature_dim"], params["feature_hidden_dims"],
tf.tanh, tf.tanh, agent.vocab_size, params["embedding_dim"]),
state_include_action=False,
)
optimizer = ConjugateGradientOptimizer(hvp_approach=FiniteDifferenceHvp(base_eps=1e-5))
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=params["batch_size"],
max_path_length=params["max_path_length"],
n_itr=params["n_itr"],
discount=0.99,
step_size=params["step_size"],
optimizer=optimizer,
)
run_experiment_lite(
algo.train(),
n_parallel=15,
snapshot_mode="last",
exp_prefix="grid_world_sweep3",
variant=params,
)
def run_experiment(params):
params_base = copy.copy(DEFAULTS)
params_base.update(params)
params = params_base
policy = RecurrentCategoricalPolicy(
name="policy",
env_spec=env.spec,
hidden_dims=params["policy_hidden_dims"],
feature_network=MLPNetworkWithEmbeddings(
"embeddings", len(VOCAB), params["feature_dim"],
params["feature_hidden_dims"], tf.tanh, tf.tanh, len(VOCAB),
params["embedding_dim"], has_other_input=False),
state_include_action=False,
)
baseline = LinearFeatureBaseline(env.spec)
optimizer = ConjugateGradientOptimizer(hvp_approach=FiniteDifferenceHvp(base_eps=1e-5))
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=params["batch_size"],
max_path_length=LENGTH,
n_itr=params["n_itr"],
discount=0.99,
step_size=params["step_size"],
optimizer=optimizer,
)
run_experiment_lite(
algo.train(),
n_parallel=5,
snapshot_mode="last",
exp_prefix="autoenc_unnorm_reward",
variant=params,
)
def run_experiment(**params):
base_params = copy.copy(DEFAULTS)
base_params.update(params)
params = base_params
grid_world = SlaveGridWorldEnv("3x3", goal_reward=params["goal_reward"])
env = normalize(grid_world)
baseline = LinearFeatureBaseline(env)
policy = CategoricalMLPPolicy(
name="policy",
env_spec=env.spec,
hidden_sizes=params["policy_hidden_dims"],
)
optimizer = ConjugateGradientOptimizer(hvp_approach=FiniteDifferenceHvp(base_eps=1e-5))
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=params["batch_size"],
max_path_length=5,
n_itr=params["n_itr"],
discount=0.99,
step_size=params["step_size"],
optimizer=optimizer,
)
run_experiment_lite(
algo.train(),
n_parallel=5,
snapshot_mode="last",
exp_prefix="grid_world_silent",
variant=params,
)
from sandbox.rocky.tf.envs.base import TfEnv
stub(globals())
#env = TfEnv(normalize(PointEnv()))
env = TfEnv(normalize(PointEnvRandGoal()))
policy = GaussianMLPPolicy(
name="policy",
env_spec=env.spec,
)
#baseline = LinearFeatureBaseline(env_spec=env.spec)
baseline = ZeroBaseline(env_spec=env.spec)
algo = VPG(
env=env,
policy=policy,
baseline=baseline,
#batch_size=20,
max_path_length=5,
n_itr=100,
#plot=True,
)
run_experiment_lite(
algo.train(),
n_parallel=1,
snapshot_mode="last",
seed=1,
exp_prefix='deleteme',
exp_name='deleteme',
#plot=True,
)
batch_size=64,
max_path_length=env.horizon,
epoch_length=1000,
min_pool_size=10000,
n_epochs=args.num_epochs,
discount=0.99,
scale_reward=args.reward_scale,
qf_learning_rate=1e-3,
policy_learning_rate=1e-4,
plot=False
)
run_experiment_lite(
algo.train(),
log_dir=None if args.use_ec2 else args.data_dir,
# Number of parallel workers for sampling
n_parallel=1,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="last",
# Specifies the seed for the experiment. If this is not provided, a random seed
# will be used
exp_prefix="DDPG_" + args.env,
seed=1,
mode="ec2" if args.use_ec2 else "local",
plot=False,
# dry=True,
terminate_machine=args.dont_terminate_machine,
added_project_directories=[osp.abspath(osp.join(osp.dirname(__file__), '.'))]
)
)
else:
policy = GaussianMLPPolicy(env_spec=mdp.spec,
hidden_sizes=(32, 32),
init_std=10)
baseline = LinearFeatureBaseline(mdp.spec, )
batch_size = 50 * 250
algo = TRPO(env=mdp,
policy=policy,
baseline=baseline,
batch_size=batch_size,
whole_paths=True,
max_path_length=50,
n_itr=200,
step_size=0.01,
subsample_factor=1.0,
**copyparams)
run_experiment_lite(
algo.train(),
exp_prefix="r_push_new_ours-quad1",
n_parallel=4,
# dry=True,
snapshot_mode="all",
seed=seed,
mode="ec2_mujoco",
# terminate_machine=False
)
env=env,
policy=policy,
es=es,
qf=qf,
batch_size=size_of_batch,
max_path_length=100,
epoch_length=1000,
min_pool_size=10000,
n_epochs=number_of_episodes,
discount=discount_factor,
scale_reward=reward_scaling[r],
qf_learning_rate=critic_learning_rate[c],
policy_learning_rate=actor_learning_rate[c],
# Uncomment both lines (this and the plot parameter below) to enable plotting
# plot=True,
)
algo.train()
run_experiment_lite(
run_task,
# Number of parallel workers for sampling
n_parallel=1,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="last",
# Specifies the seed for the experiment. If this is not provided, a random seed
# will be used
exp_name="DDPG_HalfCheetah/" + "HalfCheetah",
seed=1,
# plot=True,
)
)
if bas == 'zero':
baseline = ZeroBaseline(env_spec=env.spec)
elif 'linear' in bas:
baseline = LinearFeatureBaseline(env_spec=env.spec)
else:
baseline = GaussianMLPBaseline(env_spec=env.spec)
algo = MAMLTRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=fast_batch_size, # number of trajs for grad update
max_path_length=max_path_length,
meta_batch_size=meta_batch_size,
num_grad_updates=num_grad_updates,
n_itr=100,
use_maml=use_maml,
step_size=meta_step_size,
plot=False,
)
run_experiment_lite(
algo.train(),
n_parallel=1,
snapshot_mode="last",
python_command='python3',
seed=1,
exp_prefix='vpg_maml_point100',
exp_name='trpomaml'+str(int(use_maml))+'_fbs'+str(fast_batch_size)+'_mbs'+str(meta_batch_size)+'_flr_' + str(fast_learning_rate) + 'metalr_' + str(meta_step_size) +'_step1'+str(num_grad_updates),
plot=False,
)
run_experiment_lite(
# use_cloudpickle=False,
stub_method_call=run_task,
variant=vv,
mode=mode,
# Number of parallel workers for sampling
n_parallel=n_parallel,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="last",
seed=vv['seed'],
# plot=True,
exp_prefix=exp_prefix,
# exp_name=exp_name,
sync_s3_pkl=True,
# for sync the pkl file also during the training
sync_s3_png=True,
sync_s3_html=True,
# # use this ONLY with ec2 or local_docker!!!
pre_commands=[
'export MPLBACKEND=Agg',
'pip install --upgrade pip',
'pip install --upgrade -I tensorflow',
'pip install git+https://github.com/tflearn/tflearn.git',
'pip install dominate',
'pip install multiprocessing_on_dill',
'pip install scikit-image',
'conda install numpy -n rllab3 -y',
],
)
if mode == 'local_docker':
policy=policy,
es=es,
qf=qf,
batch_size=batch_size_values[b],
max_path_length=env.horizon,
epoch_length=1000,
min_pool_size=10000,
n_epochs=args.num_epochs,
discount=0.99,
scale_reward=1.0,
qf_learning_rate=1e-3,
policy_learning_rate=1e-4,
# Uncomment both lines (this and the plot parameter below) to enable plotting
plot=args.plot,
)
run_experiment_lite(
algo.train(),
# log_dir=args.data_dir,
# Number of parallel workers for sampling
n_parallel=1,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="last",
# Specifies the seed for the experiment. If this is not provided, a random seed
# will be used
exp_name="reproducibility_ML/" + "DDPG/" + "HalfCheetah/" + "Batch_Size_Tune/" + "Batch_Size_" + str(batch_size_values[b]) + "_Experiment_" + str(e),
seed=1,
plot=args.plot,
)
from rllab.policies.gaussian_gru_policy import GaussianGRUPolicy
from rllab.optimizers.conjugate_gradient_optimizer import ConjugateGradientOptimizer, FiniteDifferenceHvp
from rllab.misc.instrument import stub, run_experiment_lite
stub(globals())
env = normalize(CartpoleEnv())
policy = GaussianGRUPolicy(
env_spec=env.spec,
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=100,
n_itr=10,
discount=0.99,
step_size=0.01,
optimizer=ConjugateGradientOptimizer(hvp_approach=FiniteDifferenceHvp(base_eps=1e-5))
)
run_experiment_lite(
algo.train(),
n_parallel=1,
seed=1,
)
if param.name == hrl_param.name:
param.set_value(hrl_param.get_value(borrow=True))
for param in hrl_pol_param:
for hrl_param in llc_param:
if param.name == hrl_param.name:
param.set_value(hrl_param.get_value(borrow=True))
for i in range(100):
algo1.current_itr = 0
algo2.current_itr = 0
algo2.train(continue_learning=(i > 0))
sep2int()
algo1.train(continue_learning=(i > 0))
int2sep()
run_experiment_lite(
run_task,
# Number of parallel workers for sampling
n_parallel=0,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="last",
# Specifies the seed for the experiment. If this is not provided, a random seed
# will be used
seed=1,
exp_prefix='Walker3d_async_hrl'
# plot=True
)
baseline=baseline,
batch_size=4000, # 2x
max_path_length=200,
n_itr=n_itr,
reset_arg=goal,
optimizer_args={'init_learning_rate': step_sizes[step_i], 'tf_optimizer_args': {'learning_rate': 0.5*step_sizes[step_i]}, 'tf_optimizer_cls': tf.train.GradientDescentOptimizer}
)
run_experiment_lite(
algo.train(),
# Number of parallel workers for sampling
n_parallel=4,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="all",
# Specifies the seed for the experiment. If this is not provided, a random seed
# will be used
seed=1,
exp_prefix='ant_test_posticml',
exp_name='test' + str(run_id),
#plot=True,
)
# get return from the experiment
with open('data/local/ant-test-posticml/test'+str(run_id)+'/progress.csv', 'r') as f:
reader = csv.reader(f, delimiter=',')
i = 0
row = None
returns = []
quantization_tunings = [1, 5, 15, 20]
discounts = [0.99]
participation_rates = [1]
agents_numbers = [5]
average_periods = [10]
for quantization_tuning in quantization_tunings:
for discount in discounts:
for participation_rate in participation_rates:
for agents_number in agents_numbers:
for average_period in average_periods:
run_experiment_lite(
run_task,
exp_prefix="test_quantized",
# Number of parallel workers for sampling
n_parallel=1,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="last",
# Specifies the seed for the experiment. If this is not provided, a random seed
# will be used
mode="local",
variant=dict(quantization_tuning=quantization_tuning,
discount=discount,
participation_rate=participation_rate,
agents_number=agents_number,
average_period=average_period)
# plot=True,
# terminate_machine=False,
)
assert 'render_every' in params['rollout_params']
params['rollout_params']['render_every'] = None
count = 1
for i in range(options.n):
l_bfgs_exception(params)
aws_config = get_aws_config(count)
run_experiment_lite(train,
exp_prefix=exp_prefix,
mode=mode,
variant=dict(mode=mode,
params=params,
use_gpu=True,
seed=i),
dry=False,
aws_config=aws_config,
sync_s3_pkl=True,
sync_s3_png=True,
sync_s3_log=True,
pre_commands=[
"pip install --upgrade pip",
"pip install mpi4py", "pip install plotly",
"pip install pandas", "pip install seaborn"
],
use_gpu=True
# terminate_machine=False
)
print(count)
count += 1
else:
mode = "local"
l_bfgs_exception(params)
import colored_traceback.always
# exp_prefix = "test"
now = datetime.datetime.now(dateutil.tz.tzlocal())
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S')
exp_name = 'TRPO_scratch__{}batch_{}length_{}id_{}_parallel{}'.format(
# time_step_agg,
int(batch_size),
int(max_path_length),
maze_id,
timestamp,
n_parallel)
run_experiment_lite(
stub_method_call=algo.train(),
mode=mode,
use_cloudpickle=False,
pre_commands=[
'pip install --upgrade pip',
'pip install --upgrade theano',
],
# Number of parallel workers for sampling
n_parallel=n_parallel,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="last",
seed=s,
# Save to data/local/exp_prefix/exp_name/
exp_prefix=exp_prefix,
exp_name=exp_name,
use_gpu=False,
)
whole_paths=True,
max_path_length=500,
n_itr=10000,
step_size=0.01,
eta=eta,
snn_n_samples=10,
subsample_factor=1.0,
use_replay_pool=True,
use_kl_ratio=True,
use_kl_ratio_q=True,
n_itr_update=1,
kl_batch_size=1,
normalize_reward=False,
replay_pool_size=1000000,
n_updates_per_sample=5000,
second_order_update=True,
unn_n_hidden=[32],
unn_layers_type=[1, 1],
unn_learning_rate=0.0001
)
run_experiment_lite(
algo.train(),
exp_prefix="trpo-expl",
n_parallel=4,
snapshot_mode="last",
seed=seed,
mode="local",
script="sandbox/vime/experiments/run_experiment_lite.py",
)
max_path_length=1500,
n_itr=4500,
step_size=0.01,
eta=eta,
snn_n_samples=10,
subsample_factor=1.0,
use_replay_pool=True,
use_kl_ratio=True,
use_kl_ratio_q=True,
n_itr_update=1,
kl_batch_size=1,
normalize_reward=False,
replay_pool_size=1000000,
n_updates_per_sample=5000,
second_order_update=True,
unn_n_hidden=[64],
unn_layers_type=[1, 1],
unn_learning_rate=0.0001
)
run_experiment_lite(
algo.train(),
exp_prefix="trpo-expl",
n_parallel=1,
snapshot_mode="last",
seed=seed,
args_data="/home/ubuntu/work/rllab/data/local/trpo-expl/trpo-expl_2016_06_29_01_56_06_0001/params.pkl",
mode="local",
script="sandbox/vime/experiments/run_experiment_lite.py",
)
stub(globals())
# Param ranges
seeds = range(2)
# SwimmerGather hierarchical task
mdp_classes = [SwimmerGatherEnv]
mdps = [NormalizedEnv(env=mdp_class()) for mdp_class in mdp_classes]
param_cart_product = itertools.product(mdps, seeds)
for mdp, seed in param_cart_product:
policy = GaussianMLPPolicy(env_spec=mdp.spec, hidden_sizes=(64, 32))
baseline = LinearFeatureBaseline(mdp.spec)
batch_size = 50000
algo = TRPO(
env=mdp,
policy=policy,
baseline=baseline,
batch_size=batch_size,
whole_paths=True,
max_path_length=500,
n_itr=10000,
step_size=0.01,
subsample_factor=1.0,
)
run_experiment_lite(algo.train(), exp_prefix="trpo", n_parallel=4, snapshot_mode="last", seed=seed, mode="local")
from examples.point_env_randgoal import PointEnvRandGoal
from rllab.envs.normalized_env import normalize
from rllab.misc.instrument import stub, run_experiment_lite
#from rllab.policies.gaussian_mlp_policy import GaussianMLPPolicy
#from sandbox.rocky.tf.policies.gaussian_mlp_policy import GaussianMLPPolicy
from sandbox.rocky.tf.policies.minimal_gauss_mlp_policy import GaussianMLPPolicy
from sandbox.rocky.tf.envs.base import TfEnv
stub(globals())
env = TfEnv(normalize(PointEnv()))
#env = TfEnv(normalize(PointEnvRandGoal()))
policy = GaussianMLPPolicy(
name="policy",
env_spec=env.spec,
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = VPG(
env=env,
policy=policy,
baseline=baseline,
#plot=True,
)
run_experiment_lite(
algo.train(),
n_parallel=1,
snapshot_mode="last",
seed=1,
#plot=True,
)
# see https://github.com/openai/rllab/issues/87#issuecomment-282519288
env = TfEnv(normalize(GymEnv("CartPole-v0", force_reset=True)))
policy = CategoricalMLPPolicy(
name="policy",
env_spec=env.spec,
# The neural network policy should have two hidden layers, each with 32 hidden units.
hidden_sizes=(32, 32)
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=200,
n_itr=120,
discount=0.99,
step_size=0.01,
# optimizer=ConjugateGradientOptimizer(hvp_approach=FiniteDifferenceHvp(base_eps=1e-5))
)
run_experiment_lite(
algo.train(),
n_parallel=1,
snapshot_mode="last",
seed=1
)
def run_evaluation(argv):
# -------------------- Parse Arguments -----------------------------------
parser = argparse.ArgumentParser()
parser.add_argument(
'exp_prefix_dir',
type=str,
help='path to dump dir which contains folders with '
'the train results i.e. params.pkl and variant.json file')
parser.add_argument(
'--mode',
type=str,
default='local',
help='Mode for running the experiments - local: runs on local machine, '
'ec2: runs on AWS ec2 cluster (requires a proper configuration file)')
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('--num_sampled_envs',
type=int,
default=5,
help='number or environments with samples parameters')
args = parser.parse_args(argv[1:])
# ----------------------- EVALUATION ---------------------------------------
exp_prefix = os.path.basename(args.exp_prefix_dir)
eval_exp_prefix = exp_prefix + '-eval'
evaluation_runs = eval.prepare_evaluation_runs(
args.exp_prefix_dir,
EXP_PREFIX,
num_sampled_envs=args.num_sampled_envs)
# ----------------------- AWS conficuration ---------------------------------
if args.mode == 'ec2':
subnets = cheapest_subnets(ec2_instance, num_subnets=3)
info = config.INSTANCE_TYPE_INFO[ec2_instance]
config.AWS_INSTANCE_TYPE = ec2_instance
config.AWS_SPOT_PRICE = str(info["price"])
print("\n" + "**********" * 10 +
"\nexp_prefix: {}\nvariants: {}".format('TRPO',
len(evaluation_runs)))
print(
'Running on type {}, with price {}, on the subnets: '.format(
config.AWS_INSTANCE_TYPE,
config.AWS_SPOT_PRICE,
), str(subnets))
for eval_exp_name, v in evaluation_runs:
if args.mode == 'ec2':
subnet = random.choice(subnets)
config.AWS_REGION_NAME = subnet[:-1]
config.AWS_KEY_NAME = config.ALL_REGION_AWS_KEY_NAMES[
config.AWS_REGION_NAME]
config.AWS_IMAGE_ID = config.ALL_REGION_AWS_IMAGE_IDS[
config.AWS_REGION_NAME]
config.AWS_SECURITY_GROUP_IDS = \
config.ALL_REGION_AWS_SECURITY_GROUP_IDS[
config.AWS_REGION_NAME]
run_experiment_lite(
run_eval_task,
exp_prefix=eval_exp_prefix,
exp_name=eval_exp_name,
# Number of parallel workers for sampling
n_parallel=args.n_parallel,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="last",
# Specifies the seed for the experiment. If this is not provided, a random seed
# will be used
seed=v["seed"],
python_command='python3',
pre_commands=[
"yes | pip install --upgrade pip",
"yes | pip install tensorflow=='1.6.0'",
"yes | pip install --upgrade cloudpickle"
],
mode=args.mode,
use_cloudpickle=True,
periodic_sync=True,
variant=v,
# plot=True,
# terminate_machine=False,
)
policy = GaussianMLPPolicy(
env_spec=env.spec,
# The neural network policy should have two hidden layers, each with 32 hidden units.
hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
whole_paths=True,
max_path_length=100,
n_itr=20,
discount=0.99,
step_size=0.01,
plot=False)
run_experiment_lite(
algo.train(),
# Number of parallel workers for sampling
n_parallel=1,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="last",
log_dir="./results",
# Specifies the seed for the experiment. If this is not provided, a random seed
# will be used
seed=1,
plot=True,
)
policy = RecurrentCategoricalPolicy(
name="policy",
env_spec=env.spec,
hidden_dim=128,
state_include_action=False,
#temperature=2,
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=50000,
max_path_length=5,
n_itr=50,
discount=0.99,
step_size=0.01,
optimizer=ConjugateGradientOptimizer(hvp_approach=FiniteDifferenceHvp(base_eps=1e-5))
)
run_experiment_lite(
algo.train(),
n_parallel=5,
snapshot_mode="last",
log_dir="./log",
)
INPUT_FEED)
run_experiment_lite(
algo.train(
),
n_parallel=
1,
snapshot_mode
="all",
python_command
='python3',
seed=seed,
exp_prefix=
str('PU_IL_'
+ time.
strftime(
"%D"
).
replace(
"/",
"")
[0:4]),
exp_name=
exp_name,
plot=False,
sync_s3_pkl
=True,
mode=mode,
terminate_machine
=True,
)
env_spec=env.spec,
# The neural network policy should have two hidden layers, each with 32 hidden units.
hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=100,
n_itr=1000,
discount=0.99,
step_size=0.01,
)
algo.train()
run_experiment_lite(
run_task,
# Number of parallel workers for sampling
n_parallel=1,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="last",
exp_name="TRPO_Trial_Results/" + "Trial_GridWorld/",
# Specifies the seed for the experiment. If this is not provided, a random seed
# will be used
seed=1,
# plot=True,
)
def run_experiment_old(
task,
exp_prefix='default',
seed=None,
variant=None,
time_it=True,
save_profile=False,
profile_file='time_log.prof',
mode='here',
exp_id=0,
unique_id=None,
prepend_date_to_exp_prefix=True,
use_gpu=False,
snapshot_mode='last',
snapshot_gap=1,
n_parallel=0,
base_log_dir=None,
**run_experiment_lite_kwargs
):
"""
Run a task via the rllab interface, i.e. serialize it and then run it via
the run_experiment_lite script.
This will soon be deprecated.
:param task:
:param exp_prefix:
:param seed:
:param variant:
:param time_it: Add a "time" command to the python command?
:param save_profile: Create a cProfile log?
:param profile_file: Where to save the cProfile log.
:param mode: 'here' will run the code in line, without any serialization
Other options include 'local', 'local_docker', and 'ec2'. See
run_experiment_lite documentation to learn what those modes do.
:param exp_id: Experiment ID. Should be unique across all
experiments. Note that one experiment may correspond to multiple seeds.
:param unique_id: Unique ID should be unique across all runs--even different
seeds!
:param prepend_date_to_exp_prefix: If True, prefix "month-day_" to
exp_prefix
:param run_experiment_lite_kwargs: kwargs to be passed to
`run_experiment_lite`
:return:
"""
if seed is None:
seed = random.randint(0, 100000)
if variant is None:
variant = {}
if unique_id is None:
unique_id = str(uuid.uuid4())
if prepend_date_to_exp_prefix:
exp_prefix = time.strftime("%m-%d") + "_" + exp_prefix
variant['seed'] = str(seed)
variant['exp_id'] = str(exp_id)
variant['unique_id'] = str(unique_id)
logger.log("Variant:")
logger.log(json.dumps(ppp.dict_to_safe_json(variant), indent=2))
command_words = []
if time_it:
command_words.append('time')
command_words.append('python')
if save_profile:
command_words += ['-m cProfile -o', profile_file]
repo = git.Repo(os.getcwd())
diff_string = repo.git.diff(None)
commit_hash = repo.head.commit.hexsha
script_name = "tmp"
if mode == 'here':
log_dir, exp_name = create_log_dir(exp_prefix, exp_id, seed,
base_log_dir)
data = dict(
log_dir=log_dir,
exp_name=exp_name,
mode=mode,
variant=variant,
exp_id=exp_id,
exp_prefix=exp_prefix,
seed=seed,
use_gpu=use_gpu,
snapshot_mode=snapshot_mode,
snapshot_gap=snapshot_gap,
diff_string=diff_string,
commit_hash=commit_hash,
n_parallel=n_parallel,
base_log_dir=base_log_dir,
script_name=script_name,
)
save_experiment_data(data, log_dir)
if mode == 'here':
run_experiment_here(
task,
exp_prefix=exp_prefix,
variant=variant,
exp_id=exp_id,
seed=seed,
use_gpu=use_gpu,
snapshot_mode=snapshot_mode,
snapshot_gap=snapshot_gap,
code_diff=diff_string,
commit_hash=commit_hash,
script_name=script_name,
n_parallel=n_parallel,
base_log_dir=base_log_dir,
)
else:
if mode == "ec2" and use_gpu:
if not query_yes_no(
"EC2 is more expensive with GPUs. Confirm?"
):
sys.exit(1)
code_diff = (
base64.b64encode(cloudpickle.dumps(diff_string)).decode("utf-8")
)
run_experiment_lite(
task,
snapshot_mode=snapshot_mode,
snapshot_gap=snapshot_gap,
exp_prefix=exp_prefix,
variant=variant,
seed=seed,
use_cloudpickle=True,
python_command=' '.join(command_words),
mode=mode,
use_gpu=use_gpu,
script="railrl/scripts/run_experiment_lite.py",
code_diff=code_diff,
commit_hash=commit_hash,
script_name=script_name,
n_parallel=n_parallel,
**run_experiment_lite_kwargs
)
def run_trpo_vase(env,nRuns = 20,seed_base=0, sigma_c=0.5, ablation_mode=False):
now = datetime.datetime.now(dateutil.tz.tzlocal())
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S')
for seed in range(seed_base,nRuns):
if env == 'mountaincar':
mdp = MountainCarEnvX()
n_itr = 50
max_path_length = 500
type = 'classic'
elif env == 'cartpole':
mdp = NormalizedEnv(env=CartpoleSwingupEnvX())
n_itr = 400
max_path_length = 500
type = 'classic'
elif env == 'doublependulum':
mdp = NormalizedEnv(env=DoublePendulumEnvX())
n_itr = 400
max_path_length = 500
type = 'classic'
elif env == 'halfcheetah':
mdp = NormalizedEnv(env=HalfCheetahEnvX())
n_itr = 600
max_path_length = 500
type = 'locomotion'
elif env == 'ant':
mdp = NormalizedEnv(env=AntEnv())
n_itr = 600
max_path_length = 500
type = 'locomotion'
elif env == 'lunarlander':
mdp = NormalizedEnv(env=LunarLanderContinuous())
n_itr = 100
max_path_length = 1000
type = 'classic'
else:
sys.stderr.write("Error! Environment '%s' not recognised\n" % env)
sys.exit(-1)
if type == 'classic':
step_size = 0.01
replay_pool_size = 100000
policy_hidden_sizes = (32,)
unn_n_hidden = [32]
unn_layers_type=[1, 1]
baseline = GaussianMLPBaseline(
env_spec=mdp.spec,
regressor_args={
'hidden_sizes': (32,),
'learn_std': False,
'hidden_nonlinearity': NL.rectify,
'optimizer': ConjugateGradientOptimizer(subsample_factor=1.0)
}
)
else:
step_size = 0.05
replay_pool_size = 5000000
policy_hidden_sizes = (64, 32)
unn_n_hidden = [64, 64]
unn_layers_type=[1, 1, 1]
baseline = LinearFeatureBaseline(
mdp.spec,
)
policy = GaussianMLPPolicy(
env_spec=mdp.spec,
hidden_sizes=policy_hidden_sizes,
hidden_nonlinearity=NL.tanh
)
algo = TRPO(
env=mdp,
policy=policy,
baseline=baseline,
n_itr=n_itr,
batch_size=5000,
max_path_length = max_path_length,
discount = 0.995,
gae_lambda = 0.95,
whole_paths=True,
step_size=step_size,
eta=1e-4,
snn_n_samples=10,
prior_sd=0.5,
likelihood_sd=sigma_c,
subsample_factor=1.0,
use_replay_pool=True,
replay_pool_size=replay_pool_size,
n_updates_per_sample=500,
unn_n_hidden=unn_n_hidden,
unn_layers_type=unn_layers_type,
unn_learning_rate=0.001
)
exp_name = "trpo-vase_%s_%04d" % (timestamp, seed + 1)
if ablation_mode:
cwd = os.getcwd()
log_dir = cwd + "/data/local/sigmas/" + env + ("/%.3f/" % sigma_c) + exp_name
else:
log_dir = config.LOG_DIR + "/local/" + env + "/" + exp_name
run_experiment_lite(
algo.train(),
exp_name = exp_name,
log_dir= log_dir,
n_parallel=0,
snapshot_mode="last",
seed=seed,
mode="local",
script="sandbox/vase/experiments/run_experiment_lite.py"
)
es=es,
qf=qf,
batch_size=35,
max_path_length=100,
epoch_length=5000,
min_pool_size=10000,
n_epochs=100,
discount=0.99,
scale_reward=variant["scale_reward"],
soft_target_tau=1e-3,
qf_learning_rate=variant["qf_learning_rate"],
policy_learning_rate=variant["policy_learning_rate"],
#Uncomment both lines (this and the plot parameter below) to enable plotting
plot=True,
eval_samples=5000,
)
run_experiment_lite(
algo.train(),
# Number of parallel workers for sampling
n_parallel=1,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="last",
exp_prefix="dpg_search",
# Specifies the seed for the experiment. If this is not provided, a random seed
# will be used
exp_name=str(num),
seed=2,
plot=True,
)
AssociatePublicIpAddress=True,
)
]
run_experiment_lite(
stub_method_call=algo.train(),
mode='ec2',
use_cloudpickle=False,
# Number of parallel workers for sampling
n_parallel=n_parallel,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="last",
seed=s,
# plot=True,
exp_prefix=exp_prefix,
exp_name=exp_name,
sync_s3_pkl=True,
# for sync the pkl file also during the training
sync_s3_png=True,
# # use this ONLY with ec2 or local_docker!!!
pre_commands=[
"which conda",
"which python",
"conda list -n rllab3",
"conda install -f numpy -n rllab3 -y",
],
)
else:
run_experiment_lite(
stub_method_call=algo.train(),
mode='local',
from rllab.policies.gaussian_mlp_policy import GaussianMLPPolicy
def run_task(*_):
env = normalize(GymEnv("Pendulum-v0"))
policy = GaussianMLPPolicy(env_spec=env.spec, hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=env.horizon,
n_itr=50,
discount=0.99,
step_size=0.01,
plot=True,
)
algo.train()
run_experiment_lite(
run_task,
n_parallel=1,
snapshot_mode="last",
plot=True,
)
normalize(GymEnv("Walker2d-v1", record_video=False, force_reset=True)))
policy = GaussianMLPPolicy(
name="policy",
env_spec=env.spec,
# The neural network policy should have two hidden layers, each with 64 hidden units.
hidden_sizes=(64, 64),
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=50000,
max_path_length=env.horizon,
n_itr=10,
discount=0.995,
step_size=0.01,
)
run_experiment_lite(
algo.train(),
# Only keep the snapshot parameters for the last iteration
snapshot_mode="last",
n_parallel=4,
seed=0,
# plot=True,
)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=HORIZON * N_ROLLOUTS,
max_path_length=HORIZON,
n_itr=1000,
# whole_paths=True,
discount=0.999,
)
algo.train(),
exp_tag = "stabilizing_highway_%.3f" % RL_PENETRATION
for seed in [5]: # , 20, 68, 72, 125]:
run_experiment_lite(
run_task,
# Number of parallel workers for sampling
n_parallel=N_CPUS,
# Keeps the snapshot parameters for all iterations
snapshot_mode="all",
# Specifies the seed for the experiment. If this is not provided, a
# random seed will be used
seed=seed,
mode="local",
exp_prefix=exp_tag,
# plot=True,
sync_s3_pkl=True,
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
#
algo = PPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=env.horizon,
n_itr=100, #000,
discount=0.99,
step_size=0.0075, # 0.01
# Uncomment both lines (this and the plot parameter below) to enable plotting
# plot=True,
)
algo.train()
run_experiment_lite(
run_task,
# Number of parallel workers for sampling
n_parallel=5,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="last",
exp_name=
"testing", #relu_small_network_ppo_capped_action_simpler_dense_layer_xW_learn_std_smaller_learning_rate",
# Specifies the seed for the experiment. If this is not provided, a random seed
# will be used
seed=0,
plot=True,
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=env.horizon,
n_itr=500,
discount=0.99,
step_size=0.01,
# Uncomment both lines (this and the plot parameter below) to enable plotting
# plot=True,
)
run_experiment_lite(
algo.train(),
# Number of parallel workers for sampling
n_parallel=1,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="last",
# Specifies the seed for the experiment. If this is not provided, a random seed
# will be used
seed=1,
use_gpu=True,
# plot=True,
)
init_lr=0.001,
n_itr=5,
train_feature_network=True,
)
batch_size = 10000
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=batch_size,
whole_paths=True,
max_path_length=1000,
n_itr=1000,
step_size=0.01,
subsample_factor=1.0,
sampler_cls=BatchSampler,
optimizer_args={
'num_slices' : 10,
}
)
run_experiment_lite(
algo.train(),
exp_prefix='trpo_box3d_pixel_v11_tf',
n_parallel=12,
snapshot_mode="gap",
snapshot_gap=200,
seed=seed,
mode="local"
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
#algo = VPG(
env=env,
policy=policy,
baseline=baseline,
batch_size=
1000, # was 4k # 500 for path lenght of 5, 1000 for path length of 100
max_path_length=100,
n_itr=100,
discount=0.99,
step_size=0.01,
#plot=True,
)
#algo.train()
run_experiment_lite(
algo.train(),
# Number of parallel workers for sampling
n_parallel=4,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="last",
# Specifies the seed for the experiment. If this is not provided, a random seed
# will be used
seed=1,
exp_prefix='vpg_sensitive_point100',
exp_name='oracleenv2',
#plot=True,
)
algo = SensitiveTRPO(
#algo = SensitiveVPG(
env=env,
policy=policy,
baseline=baseline,
batch_size=fast_batch_size, # number of trajs for grad update
max_path_length=max_path_length,
meta_batch_size=meta_batch_size,
num_grad_updates=num_grad_updates,
n_itr=400,
use_sensitive=use_sensitive,
#optimizer_args={'tf_optimizer_args':{'learning_rate': learning_rate}},
plot=False,
)
run_experiment_lite(
algo.train(),
n_parallel=0,
snapshot_mode="last",
seed=1,
#exp_prefix='deleteme',
#exp_name='deleteme'
#exp_prefix='sensitive1dT5_2017_01_19',
#exp_prefix='bugfix_sensitive0d_8tasks_T'+str(max_path_length)+'_2017_02_05',
exp_prefix='trpo_sensitive_cheetah' + str(max_path_length),
exp_name='sens' + str(int(use_sensitive)) + '_fbs' +
str(fast_batch_size) + '_mbs' + str(meta_batch_size) +
'_flr_' + str(fast_learning_rate) + '_lr_' +
str(learning_rate) + '_step1' + str(num_grad_updates),
plot=False,
)
hidden_sizes=(100,100),
)
if bas == 'zero':
baseline = ZeroBaseline(env_spec=env.spec)
elif 'linear' in bas:
baseline = LinearFeatureBaseline(env_spec=env.spec)
else:
baseline = GaussianMLPBaseline(env_spec=env.spec)
algo = MAMLTRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=fast_batch_size, # number of trajs for grad update
max_path_length=max_path_length,
meta_batch_size=meta_batch_size,
num_grad_updates=num_grad_updates,
n_itr=800,
use_maml=use_maml,
step_size=meta_step_size,
plot=False,
)
run_experiment_lite(
algo.train(),
n_parallel=4,
snapshot_mode="last",
seed=1,
exp_prefix='trpo_maml_4state',
exp_name='trpo_maml'+str(int(use_maml))+'_fbs'+str(fast_batch_size)+'_mbs'+str(meta_batch_size)+'_flr_' + str(fast_learning_rate) + 'metalr_' + str(meta_step_size) +'_step1'+str(num_grad_updates),
plot=False,
)
parser.add_argument("env_name", type=str, help='available env_name:')
parser.add_argument("random_seed", type=int)
parser.add_argument("num_of_agents", type=int)
parser.add_argument("temperature", type=float)
parser.add_argument("batch_size", type=int, default=5000)
args = parser.parse_args()
env_name = env_map[args.env_name]
prefix = prefix_map[args.env_name]
n_epochs = n_epochs_map[args.env_name]
random_seed = int(args.random_seed)
run_function = function_map[args.algo]
n_itr = n_epochs_map[args.env_name]
num_of_agents = int(args.num_of_agents)
temperature = float(args.temperature)
learning_rate = learning_rate_map[args.env_name]
batch_size = int(args.batch_size)
if args.algo == "multi_REINFORCE_stein" or args.algo == "multi_REINFORCE_stein_anneal" or args.algo == 'multi_REINOFRCE_stein_reg' or args.algo == "multi_REINFORCE_stein_no_critic" or args.algo == 'multi_REINFORCE_baseline_no_critic' or args.algo == 'multi_REINFORCE_stein_evolution':
args.algo = "{:}#{:}_temp={:}".format(args.algo, num_of_agents,
args.temperature)
run_experiment_lite(
run_function,
n_parallel=4,
snapshot_mode="last",
seed=random_seed,
log_dir="./../exp_log/{:}_seed={:}_iter=500_env={:}_{:}".format(
args.algo, random_seed, prefix, get_date()),
)
max_path_length=max_path_length,
meta_batch_size=v['meta_batch_size'],
num_grad_updates=num_grad_updates,
n_itr=800,
use_maml=use_maml,
step_size=v['meta_step_size'],
plot=False,
)
direc = 'direc' if direc else ''
run_experiment_lite(
algo.train(),
exp_prefix='trpo_maml_cheetah' + direc + str(max_path_length),
exp_name='maml'+str(int(use_maml))+'_fbs'+str(v['fast_batch_size'])+'_mbs'+str(v['meta_batch_size'])+'_flr_' + str(v['fast_lr']) + '_mlr' + str(v['meta_step_size']),
# Number of parallel workers for sampling
n_parallel=8,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="gap",
snapshot_gap=25,
sync_s3_pkl=True,
python_command='python3',
# Specifies the seed for the experiment. If this is not provided, a random seed
# will be used
seed=v["seed"],
mode="local",
#mode="ec2",
variant=v,
# plot=True,
# terminate_machine=False,
)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=64 * 3 * horizon,
max_path_length=horizon,
# whole_paths=True,
n_itr=1000,
discount=0.999,
# step_size=0.01,
)
algo.train()
exp_tag = "cooperative_merge_example" # experiment prefix
for seed in [1]: # , 5, 10, 56, 73]:
run_experiment_lite(
run_task,
# Number of parallel workers for sampling
n_parallel=1,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="all",
# Specifies the seed for the experiment. If this is not provided, a
# random seed will be used
seed=seed,
mode="local", # "ec2"
exp_prefix=exp_tag,
# plot=True,
)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=100,
n_itr=40,
discount=0.99,
step_size=v["step_size"],
# Uncomment both lines (this and the plot parameter below) to enable plotting
# plot=True,
)
run_experiment_lite(
algo.train(),
exp_prefix="first_exp",
# Number of parallel workers for sampling
n_parallel=1,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="last",
# Specifies the seed for the experiment. If this is not provided, a random seed
# will be used
seed=v["seed"],
# mode="local",
mode="ec2",
variant=v,
# plot=True,
# terminate_machine=False,
)
sys.exit()
policy=policy,
baseline=baseline,
batch_size=batch_size,
whole_paths=True,
max_path_length=200,
n_itr=1000,
step_size=0.01,
subsample_factor=1.0,
optimizer_args={'num_slices': 10},
sampler_cls=BatchSampler,
)
algorithm = ICM(
mdp,
algo,
"/home/fred/box3d/trpo_box3d_state_v11_tf_icm_cos_ext0.9_%d" % seed,
feature_dim=mdp.spec.observation_space.flat_dim,
forward_weight=0.1,
external_reward_weight=0.9,
inverse_tanh=True,
init_learning_rate=1e-4,
n_updates_per_iter=500)
run_experiment_lite(algorithm.train(),
exp_prefix='trpo_box3d_state_v11_tf_icm_cos_ext0.9',
n_parallel=8,
snapshot_mode="gap",
snapshot_gap=200,
seed=seed,
mode="local")
load_policy=initial_params_file,
baseline=baseline,
batch_size=8000,
max_path_length=200,
n_itr=n_itr,
reset_arg=goal,
optimizer_args={'init_learning_rate': step_sizes[step_i], 'tf_optimizer_args': {'learning_rate': 0.01*step_sizes[step_i]}, 'tf_optimizer_cls': tf.train.GradientDescentOptimizer}
)
run_experiment_lite(
algo.train(),
# Number of parallel workers for sampling
n_parallel=4,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="all",
# Specifies the seed for the experiment. If this is not provided, a random seed
# will be used
seed=goal_i,
exp_prefix='antdirec_test',
exp_name='test' + str(run_id),
plot=True,
)
# get return from the experiment
with open('data/local/antdirec-test/test'+str(run_id)+'/progress.csv', 'r') as f:
reader = csv.reader(f, delimiter=',')
i = 0
row = None
returns = []
for row in reader:
i+=1
baseline = LinearFeatureBaseline(env_spec=env.spec)
# max_path_length = env.horizon
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=300,
n_itr=10000,
discount=0.99,
step_size=0.02,
# truncate_local_is_ratio = 0.2
# Uncomment both lines (this and the plot parameter below) to enable plotting
# plot=True,
)
algo.train()
run_experiment_lite(
run_task,
# Number of parallel workers for sampling
log_dir='./log/trpo_mntcar_cont',
n_parallel=1,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="last",
# Specifies the seed for the experiment. If this is not provided, a random seed
# will be used
seed=1,
# plot=True,
)
def run_task(*_):
env = normalize(GymEnv("Pendulum-v0"))
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(32, 32)
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=env.horizon,
n_itr=50,
discount=0.99,
step_size=0.01,
plot=True,
)
algo.train()
run_experiment_lite(
run_task,
n_parallel=1,
snapshot_mode="last",
plot=True,
)
step_size=v['meta_step_size'],
plot=False,
)
exp_name = 'Cellrobot_BigDog2trpo_maml' + task_var + '_' + str(
max_path_length) + '_EXP' + str(exp_id)
run_experiment_lite(
algo.train(),
exp_prefix=exp_name,
exp_name='maml' + str(int(use_maml)) + '_fbs' +
str(v['fast_batch_size']) + '_mbs' + str(v['meta_batch_size']) +
'_flr_' + str(v['fast_lr']) + '_mlr' + str(v['meta_step_size']),
# Number of parallel workers for sampling
n_parallel=16,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="gap",
snapshot_gap=2,
sync_s3_pkl=True,
# Specifies the seed for the experiment. If this is not provided, a random seed
# will be used
seed=v["seed"],
mode="local",
# mode="ec2",
variant=v,
# plot=True,
# terminate_machine=False,
)
if ssh_FLAG:
local_dir = os.path.abspath('data/local/' + exp_name + '/')
remote_dir = '/home/drl/PycharmProjects/maml_rl-master/data/AWS_data/' + exp_name + '/'
ssh.upload(local_dir,
load_policy=initial_params_file,
baseline=baseline,
batch_size=4000, # 2x
max_path_length=100,
n_itr=n_itr,
optimizer_args={'init_learning_rate': step_sizes[step_i], 'tf_optimizer_args': {'learning_rate': 0.5*step_sizes[step_i]}, 'tf_optimizer_cls': tf.train.GradientDescentOptimizer}
)
run_experiment_lite(
algo.train(),
# Number of parallel workers for sampling
n_parallel=4,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="last",
# Specifies the seed for the experiment. If this is not provided, a random seed
# will be used
seed=4,
exp_prefix='trpopoint2d_test',
exp_name='test',
#plot=True,
)
import pdb; pdb.set_trace()
# get return from the experiment
with open('data/local/trpopoint2d-test/test/progress.csv', 'r') as f:
reader = csv.reader(f, delimiter=',')
i = 0
row = None
returns = []
for row in reader:
i+=1
else:
env = TfEnv(normalize(SwimmerEnv()))
batch_size = 20
policy = GaussianMLPPolicy(
name="policy",
env_spec=env.spec,
hidden_sizes=(100,100),
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
#baseline = ZeroBaseline(env_spec=env.spec)
algo = VPG(
env=env,
policy=policy,
baseline=baseline,
batch_size=500*batch_size,
max_path_length=500,
n_itr=500,
#plot=True,
optimizer_args={'tf_optimizer_args':{'learning_rate': 1e-3}},
)
run_experiment_lite(
algo.train(),
n_parallel=1, # try increasing this to make it faster??? (Maybe need to modify code for this)
snapshot_mode="last",
seed=1,
exp_prefix='vpgswimmer',
#exp_name='basic',
exp_name='randomenv',
#plot=True,
)