env=mdp,
policy=policy,
baseline=baseline,
batch_size=batch_size,
whole_paths=True,
max_path_length=200,
n_itr=2000,
step_size=0.01,
subsample_factor=1.0,
optimizer_args={'num_slices': 10},
)
algorithm = ICM(
mdp,
algo,
"/home/fred/box3d/trpo_box3d_state_v10_tf_icm_cos_%d" % seed,
feature_dim=mdp.spec.observation_space.flat_dim,
forward_weight=0.1,
external_reward_weight=0.0,
inverse_tanh=True,
init_learning_rate=1e-4,
)
run_experiment_lite(algorithm.train(),
exp_prefix='trpo_box3d_state_v10_tf_icm_cos',
n_parallel=1,
snapshot_mode="gap",
snapshot_gap=200,
seed=seed,
mode="local")
batch_size = 2400
idle = Idle(
env=env,
policy=policy,
baseline=baseline,
)
icm = ICM(
env,
idle,
'/home/fshentu/box3d/trpo_box3d_pixel_v8_tf_idle_normalize_fw_%d' %
seed,
feature_dim=256,
forward_weight=0.2,
inverse_tanh=True,
init_learning_rate=1e-4,
icm_batch_size=128,
replay_pool_size=1000000,
n_updates_per_iter=200,
obs_dtype='uint8',
normalize_input=True,
)
run_experiment_lite(icm.train(),
exp_prefix='trpo_box3d_pixel_v8_tf_idle_normalize',
n_parallel=1,
snapshot_mode="gap",
snapshot_gap=200,
seed=seed,
mode="local")
def main():
parser = argparse.ArgumentParser()
# Hyperparameters
parser.add_argument('--fw_ratio', type=float, default=0.1)
parser.add_argument('--init_lr', type=float, default=5e-4)
parser.add_argument('--tfboard_path', type=str, default='/tmp/tfboard')
parser.add_argument('--gpu_ratio', type=float, default=0.99)
args = parser.parse_args()
# Param ranges
seeds = range(2)
for seed in seeds:
mdp = TfEnv(normalize(env=GymEnv('Box3dReach-v17',record_video=False, \
log_dir='/tmp/gym_test',record_log=False), normalize_obs=True))
name = 'trpo-state-v17-tf-icm-fw{}-initlr-{}-norm'.format(
args.fw_ratio, args.init_lr)
policy = GaussianMLPPolicy(
"mlp_policy",
env_spec=mdp.spec,
hidden_sizes=(64, 64, 32),
output_nonlinearity=tf.nn.tanh,
clip_action=False,
)
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=1000,
n_itr=1000,
step_size=0.01,
subsample_factor=1.0,
sampler_cls=BatchSampler,
)
algorithm = ICM(
mdp,
algo,
args.tfboard_path + "/%s_%d" % (name, seed),
feature_dim=mdp.spec.observation_space.flat_dim,
forward_weight=args.fw_ratio,
external_reward_weight=0.0,
replay_pool_size=1000000,
init_learning_rate=args.init_lr,
n_updates_per_iter=1000,
)
run_experiment_lite(algorithm.train(),
exp_prefix=name,
n_parallel=8,
snapshot_mode="gap",
snapshot_gap=200,
seed=seed,
mode="local")
)
batch_size = 2400
idle = Idle(
env=env,
policy=policy,
baseline=baseline,
)
icm = ICM(
env,
idle,
'/home/fred/box3d/trpo_box3d_pixel_v8_tf_idle_%d'%seed,
feature_dim=256,
forward_weight=0.2,
inverse_tanh=True,
init_learning_rate=1e-4,
icm_batch_size=128,
replay_pool_size=1000000,
n_updates_per_iter=200,
obs_dtype='uint8',
)
run_experiment_lite(
icm.train(),
exp_prefix='trpo_box3d_pixel_v8_tf_idle',
n_parallel=1,
snapshot_mode="gap",
snapshot_gap=200,
seed=seed,
mode="local"
)
baseline = LinearFeatureBaseline(mdp.spec, )
batch_size = 50000
idle = Idle(
env=mdp,
policy=policy,
baseline=baseline,
n_itr=2000,
)
algorithm = ICM(
mdp,
idle,
"/x/mujoco/tfboard_box3d/trpo_box3d_state_v10_tf_icm_idle_%d" % seed,
feature_dim=mdp.spec.observation_space.flat_dim,
forward_weight=0.1,
external_reward_weight=0.0,
init_learning_rate=1e-4,
n_updates_per_iter=500,
)
run_experiment_lite(algorithm.train(),
exp_prefix='trpo_box3d_state_v10_tf_icm_idle',
n_parallel=8,
snapshot_mode="gap",
snapshot_gap=200,
seed=seed,
mode="local")
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_v10_tf_icm_cos_new_ext0.95_%d" %
seed,
feature_dim=mdp.spec.observation_space.flat_dim,
forward_weight=0.1,
external_reward_weight=0.95,
inverse_tanh=True,
init_learning_rate=1e-4,
n_updates_per_iter=500)
run_experiment_lite(
algorithm.train(),
exp_prefix='trpo_box3d_state_v10_tf_icm_cos_new_ext0.95',
n_parallel=8,
snapshot_mode="gap",
snapshot_gap=200,
seed=seed,
mode="local")
step_size=0.01,
subsample_factor=0.2,
sampler_cls=BatchSampler,
optimizer_args={
'num_slices': 4,
})
icm = ICM(
env,
algo,
'/z/dianchen/box3d/trpo_box3d_pixel_v11_tf_icm_pretrained_cnn_norew_fw0.01_%d'
% seed,
forward_weight=0.01,
external_reward_weight=0.0,
init_learning_rate=1e-4,
forward_cos=True,
replay_pool_size=100000,
n_updates_per_iter=1000,
normalize_input=True,
obs_dtype='uint8',
pretrained_icm=True,
pretrained_icm_path=
'/z/dianchen/tfmodel_box3d/icm_supervised_box3dpixel_v11_box_dense_2e3_fw_0.01_lr_5e-4.pkl',
)
run_experiment_lite(
icm.train(),
exp_prefix='trpo_box3d_pixel_v11_tf_icm_pretrained_cnn_norew_fw0.01',
n_parallel=12,
snapshot_mode="gap",
snapshot_gap=200,
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,
)
algorithm = ICM(
mdp,
algo,
"/media/4tb/box3d/trpo_box3d_state_v12_tf_icm_frozen_fw0.1_frozen_%d" %
seed,
feature_dim=mdp.spec.observation_space.flat_dim,
pretrained_icm=True,
pretrained_icm_path=
"/home/fred/rllab/data/local/trpo-state-v12-tf-icm-fw0.1-initlr-0.001/trpo-state-v12-tf-icm-fw0.1-initlr-0.001_2017_07_16_22_12_20_0001/itr_1000.pkl",
freeze_icm=True,
external_reward_weight=0.0,
)
run_experiment_lite(algorithm.train(),
exp_prefix='trpo_box3d_state_v12_tf_icm_frozen_fw0.1',
n_parallel=8,
snapshot_mode="gap",
snapshot_gap=200,
seed=seed,
mode="local")
baseline=baseline,
batch_size=batch_size,
whole_paths=True,
max_path_length=1000,
n_itr=10000,
step_size=0.01,
subsample_factor=1.0,
sampler_cls=BatchSampler,
)
algorithm = ICM(
mdp,
algo,
"/x/mujoco/tfboard_box3d/trpo_box3d_state_v12_tf_icm_fw0.3_5e-4_%d"%seed,
feature_dim=mdp.spec.observation_space.flat_dim,
forward_weight=0.3,
external_reward_weight=0.0,
replay_pool_size=100000000,
init_learning_rate=5e-4,
n_updates_per_iter=2000,
)
run_experiment_lite(
algorithm.train(),
exp_prefix='trpo_box3d_state_v12_tf_icm_fw0.3_5e-4',
n_parallel=8,
snapshot_mode="gap",
snapshot_gap=200,
seed=seed,
mode="local"
)
env=mdp,
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,
sampler_cls=BatchSampler,
)
algorithm = ICM(
mdp,
algo,
"/z/dianchen/box3d/trpo_box3d_state_v4_tf_icm_ext0.9995_%d" % seed,
feature_dim=mdp.spec.observation_space.flat_dim,
forward_weight=0.1,
external_reward_weight=0.9995,
replay_pool_size=500000,
init_learning_rate=5e-4,
n_updates_per_iter=1000)
run_experiment_lite(algorithm.train(),
exp_prefix='trpo_box3d_state_v4_tf_icm_ext0.9995',
n_parallel=6,
snapshot_mode="gap",
snapshot_gap=100,
seed=seed,
mode="local")
env=mdp,
policy=policy,
baseline=baseline,
batch_size=batch_size,
whole_paths=True,
max_path_length=500,
n_itr=1000,
step_size=0.01,
subsample_factor=1.0,
)
algorithm = ICM(
mdp,
algo,
"/data0/dianchen/box3d/trpo_box3d_state_v4_tf_icm",
feature_dim=mdp.spec.observation_space.flat_dim,
forward_weight=0.2,
external_reward_weight=0.0,
inverse_tanh=True,
init_learning_rate=1e-4,
)
run_experiment_lite(
algorithm.train(),
exp_prefix='trpo_box3d_state_v4_tf_icm',
n_parallel=1,
snapshot_mode="gap",
snapshot_gap=100,
seed=seed,
mode="local"
)
mdp.spec,
)
batch_size = 5000
algo = Idle(
env=mdp,
policy=policy,
baseline=baseline,
)
algorithm = ICM(
mdp,
algo,
"/home/dianchen/box3d/trpo_box3d_state_v4_tf_icm_idle",
no_encoder=False,
feature_dim=mdp.spec.observation_space.flat_dim,
forward_weight=0.2,
external_reward_weight=0.99,
inverse_tanh=True,
init_learning_rate=1e-3,
)
run_experiment_lite(
algorithm.train(),
exp_prefix='trpo_box3d_state_v4_tf_icm_idle',
n_parallel=1,
snapshot_mode="gap",
snapshot_gap=100,
seed=seed,
mode="local"
)
policy=policy,
baseline=baseline,
batch_size=batch_size,
whole_paths=True,
max_path_length=200,
n_itr=2000,
step_size=0.01,
subsample_factor=1.0,
)
icm = ICM(
env,
trpo,
'/home/fred/box3d/trpo_box3d_pixel_v7_tf_icm_%d' % seed,
feature_dim=256,
forward_weight=0.2,
inverse_tanh=True,
init_learning_rate=1e-4,
icm_batch_size=128,
replay_pool_size=1000000,
n_updates_per_iter=200,
)
run_experiment_lite(icm.train(),
exp_prefix='trpo_box3d_pixel_v7_tf_icm',
n_parallel=1,
snapshot_mode="gap",
snapshot_gap=200,
seed=seed,
mode="local")
baseline=baseline,
batch_size=batch_size,
whole_paths=True,
max_path_length=200,
n_itr=1000,
step_size=0.01,
subsample_factor=1.0,
sampler_cls=BatchSampler,
)
algorithm = ICM(
mdp,
algo,
"/home/fred/box3d/trpo_box3d_state_v11_tf_icm_cos_ext0.995_%d"%seed,
feature_dim=mdp.spec.observation_space.flat_dim,
forward_weight=0.1,
external_reward_weight=0.995,
init_learning_rate=1e-4,
replay_pool_size=500000,
n_updates_per_iter=500
)
run_experiment_lite(
algorithm.train(),
exp_prefix='trpo_box3d_state_v11_tf_icm_cos_ext0.995',
n_parallel=8,
snapshot_mode="gap",
snapshot_gap=200,
seed=seed,
mode="local"
)
