def run_train_task(vv):
env = TfEnv(normalize(vv['env'](log_scale_limit=vv['log_scale_limit'])))
dynamics_model = MLPDynamicsEnsemble(
name="dyn_model",
env_spec=env.spec,
hidden_sizes=vv['hidden_sizes_model'],
weight_normalization=vv['weight_normalization_model'],
num_models=vv['num_models'],
valid_split_ratio=vv['valid_split_ratio'],
rolling_average_persitency=vv['rolling_average_persitency'])
policy = MPCController(
name="policy",
env=env,
dynamics_model=dynamics_model,
discount=vv['discount'],
n_candidates=vv['n_candidates'],
horizon=vv['horizon'],
)
algo = ModelMPCBatchPolopt(
env=env,
policy=policy,
dynamics_model=dynamics_model,
batch_size_env_samples=vv['batch_size_env_samples'],
initial_random_samples=vv['initial_random_samples'],
dynamic_model_max_epochs=vv['dynamic_model_epochs'],
max_path_length=vv['path_length'],
n_itr=vv['n_itr'],
discount=vv['discount'],
step_size=vv["step_size"],
reinit_model_cycle=vv['reinit_model_cycle'])
algo.train()
def run_train_task(vv):
env = TfEnv(normalize(vv['env']()))
policy = MAMLGaussianMLPPolicy(
name="policy",
env_spec=env.spec,
hidden_sizes=vv['hidden_sizes'],
grad_step_size=vv['fast_lr'],
hidden_nonlinearity=vv['hidden_nonlinearity'],
trainable_step_size=vv['trainable_step_size'],
bias_transform=vv['bias_transform'])
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = MAMLTRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=vv['fast_batch_size'], # number of trajs for grad update
max_path_length=vv['path_length'],
meta_batch_size=vv['meta_batch_size'],
num_grad_updates=vv['num_grad_updates'],
n_itr=vv['n_itr'],
discount=vv['discount'],
step_size=vv["meta_step_size"],
parallel_sampler=vv['parallel_sampler'],
)
algo.train()
def run_train_task(vv):
env = TfEnv(normalize(vv['env']()))
policy = vv['policy'](name="policy",
env_spec=env.spec,
num_tasks=vv['meta_batch_size'],
hidden_sizes=vv['hidden_sizes'],
grad_step_size=vv['fast_lr'],
hidden_nonlinearity=vv['hidden_nonlinearity'],
trainable_step_size=vv['trainable_step_size'],
bias_transform=vv['bias_transform'])
baseline = LinearFeatureBaseline(env_spec=env.spec)
optimizer_args = dict(max_epochs=vv['max_epochs'], )
algo = vv['algo'](
env=env,
policy=policy,
baseline=baseline,
batch_size=vv['fast_batch_size'], # number of trajs for grad update
max_path_length=vv['path_length'],
meta_batch_size=vv['meta_batch_size'],
num_grad_updates=vv['num_grad_updates'],
n_itr=vv['n_itr'],
discount=vv['discount'],
entropy_bonus=vv['entropy_bonus'],
clip_eps=vv['clip_eps'],
target_inner_step=vv['target_inner_step'],
init_kl_penalty=vv['init_kl_penalty'],
optimizer_args=optimizer_args,
)
algo.train()
def run_train_task(vv):
env = TfEnv(normalize(vv['env'](
log_scale_limit=vv['log_scale_limit'],
target_velocity=vv['target_velocity'],
)))
dynamics_model = MLPDynamicsEnsemble(
name="dyn_model",
env_spec=env.spec,
hidden_sizes=vv['hidden_sizes_model'],
weight_normalization=vv['weight_normalization_model'],
num_models=vv['num_models'],
optimizer=vv['optimizer_model'],
valid_split_ratio=vv['valid_split_ratio'],
rolling_average_persitency=vv['rolling_average_persitency']
)
policy = MAMLGaussianMLPPolicy(
name="policy",
env_spec=env.spec,
hidden_sizes=vv['hidden_sizes_policy'],
hidden_nonlinearity=vv['hidden_nonlinearity_policy'],
grad_step_size=vv['fast_lr'],
trainable_step_size=vv['trainable_step_size'],
bias_transform=vv['bias_transform'],
num_tasks=vv['meta_batch_size']
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = ModelMAMLTRPO(
env=env,
policy=policy,
dynamics_model=dynamics_model,
baseline=baseline,
n_itr=vv['n_itr'],
n_iter=vv['n_itr'],
batch_size_env_samples=vv['batch_size_env_samples'],
batch_size_dynamics_samples=vv['batch_size_dynamics_samples'],
meta_batch_size=vv['meta_batch_size'],
initial_random_samples=vv['initial_random_samples'],
num_maml_steps_per_iter=vv['num_maml_steps_per_iter'],
reset_from_env_traj=vv.get('reset_from_env_traj', False),
max_path_length_env=vv['path_length_env'],
max_path_length_dyn=vv.get('path_length_dyn', None),
dynamic_model_max_epochs=vv.get('dynamic_model_max_epochs', (500, 500)),
discount=vv['discount'],
step_size=vv["meta_step_size"],
num_grad_updates=1,
retrain_model_when_reward_decreases=vv['retrain_model_when_reward_decreases'],
reset_policy_std=vv['reset_policy_std'],
reinit_model_cycle=vv['reinit_model_cycle'],
frac_gpu=vv.get('frac_gpu', 0.85),
log_real_performance=True,
clip_obs=vv.get('clip_obs', True),
entropy_bonus=vv['entropy_bonus'],
tailored_exploration=vv['tailored_exploration']
)
algo.train()
def run_eval_task(vv):
# load policy and baseline- Warning: resets the tf graph
# also returns the tensorflow session which must be used in the further code
baseline, env, params_pickle_file = eval.load_baseline_and_env(vv)
tf.reset_default_graph()
# fix the mujoco parameters
env_class = eval.get_env_class(env)
env = TfEnv(
normalize(
env_class(log_scale_limit=vv["log_scale_limit"],
fix_params=True,
random_seed=vv['env_param_seed'])))
step_size = vv['fast_lr']
policy = None
algo = VPG(env=env,
policy=policy,
load_policy=params_pickle_file,
baseline=baseline,
batch_size=20000,
max_path_length=100,
n_itr=5,
optimizer_args={
'init_learning_rate': step_size,
'tf_optimizer_args': {
'learning_rate': step_size
},
'tf_optimizer_cls': tf.train.GradientDescentOptimizer
})
algo.train()
def run_train_task(vv):
import sys
print(vv['exp_prefix'])
sysout_log_path = os.path.join(config.LOG_DIR, 'local', vv['exp_prefix'],
vv['exp_name'], 'stdout.log')
sysout_log_file = open(sysout_log_path, 'w')
sys.stdout = sysout_log_file
env = TfEnv(normalize(vv['env'](log_scale_limit=vv['log_scale_limit'])))
dynamics_model = MLPDynamicsEnsemble(
name="dyn_model",
env_spec=env.spec,
hidden_sizes=vv['hidden_sizes_model'],
weight_normalization=vv['weight_normalization_model'],
num_models=vv['num_models'],
optimizer=vv['optimizer_model'],
valid_split_ratio=vv['valid_split_ratio'],
rolling_average_persitency=vv['rolling_average_persitency'])
policy = MAMLImprovedGaussianMLPPolicy(
name="policy",
env_spec=env.spec,
hidden_sizes=vv['hidden_sizes_policy'],
hidden_nonlinearity=vv['hidden_nonlinearity_policy'],
grad_step_size=vv['fast_lr'],
trainable_step_size=vv['trainable_step_size'],
bias_transform=vv['bias_transform'],
param_noise_std=vv['param_noise_std'])
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = ModelMAMLTRPO(
env=env,
policy=policy,
dynamics_model=dynamics_model,
baseline=baseline,
n_itr=vv['n_itr'],
n_iter=vv['n_itr'],
batch_size_env_samples=vv['batch_size_env_samples'],
batch_size_dynamics_samples=vv['batch_size_dynamics_samples'],
meta_batch_size=vv['meta_batch_size'],
initial_random_samples=vv['initial_random_samples'],
num_maml_steps_per_iter=vv['num_maml_steps_per_iter'],
reset_from_env_traj=vv.get('reset_from_env_traj', False),
max_path_length_env=vv['path_length_env'],
max_path_length_dyn=vv.get('path_length_dyn', None),
discount=vv['discount'],
step_size=vv["meta_step_size"],
num_grad_updates=1,
retrain_model_when_reward_decreases=vv[
'retrain_model_when_reward_decreases'],
reset_policy_std=vv['reset_policy_std'],
reinit_model_cycle=vv['reinit_model_cycle'],
frac_gpu=vv.get('frac_gpu', 0.85),
clip_obs=vv.get('clip_obs', True))
algo.train()
sysout_log_file.close()
def run_train_task(vv):
env = TfEnv(normalize(vv['env'](log_scale_limit=vv['log_scale_limit'])))
dynamics_model = BadDynamicsEnsemble(
name="dyn_model",
env_spec=env.spec,
hidden_sizes=vv['hidden_sizes_model'],
weight_normalization=vv['weight_normalization_model'],
num_models=vv['num_models'],
optimizer=vv['optimizer_model'],
output_bias_range=vv['output_bias_range'],
gaussian_noise_output_std=vv['output_noise_std'],
)
policy = MAMLImprovedGaussianMLPPolicy(
name="policy",
env_spec=env.spec,
hidden_sizes=vv['hidden_sizes_policy'],
hidden_nonlinearity=vv['hidden_nonlinearity_policy'],
grad_step_size=vv['fast_lr'],
trainable_step_size=vv['trainable_step_size'],
bias_transform=vv['bias_transform'],
param_noise_std=vv['param_noise_std'])
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = ModelMAMLTRPO(
env=env,
policy=policy,
dynamics_model=dynamics_model,
baseline=baseline,
n_itr=vv['n_itr'],
n_iter=vv['n_itr'],
batch_size_env_samples=vv['batch_size_env_samples'],
batch_size_dynamics_samples=vv['batch_size_dynamics_samples'],
meta_batch_size=vv['meta_batch_size'],
initial_random_samples=vv['initial_random_samples'],
dynamic_model_epochs=vv['dynamic_model_epochs'],
num_maml_steps_per_iter=vv['num_maml_steps_per_iter'],
reset_from_env_traj=vv['reset_from_env_traj'],
max_path_length_env=vv['path_length_env'],
discount=vv['discount'],
step_size=vv["meta_step_size"],
num_grad_updates=1,
retrain_model_when_reward_decreases=vv[
'retrain_model_when_reward_decreases'],
reset_policy_std=vv['reset_policy_std'],
reinit_model_cycle=vv['reinit_model_cycle'],
frac_gpu=vv.get('frac_gpu', 0.85),
log_real_performance=True,
resample_output_bias=vv['resample_output_bias'])
algo.train()
def run_train_task(vv):
env = TfEnv(
normalize(vv['env_class'](
fix_goal=vv['fix_goal'],
reward_type=vv['reward_type'],
init_puck_low=INIT_PUCK_TARGET - vv['init_slack'],
init_puck_high=INIT_PUCK_TARGET + vv['init_slack'],
puck_goal_low=PUCK_GOAL_TARGET - vv['goal_slack'],
puck_goal_high=PUCK_GOAL_TARGET + vv['goal_slack'],
)))
policy = MAMLGaussianMLPPolicy(
name="policy",
env_spec=env.spec,
num_tasks=vv['meta_batch_size'],
hidden_sizes=vv['hidden_sizes'],
grad_step_size=vv['fast_lr'],
hidden_nonlinearity=vv['hidden_nonlinearity'],
trainable_step_size=vv['trainable_step_size'],
bias_transform=vv['bias_transform'])
baseline = LinearFeatureBaseline(env_spec=env.spec)
optimizer_args = dict(
max_epochs=vv['max_epochs'],
batch_size=vv['num_batches'],
tf_optimizer_args=dict(learning_rate=vv['outer_lr']),
)
algo = MAMLPPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=vv['fast_batch_size'], # number of trajs for grad update
max_path_length=vv['path_length'],
meta_batch_size=vv['meta_batch_size'],
num_grad_updates=vv['num_grad_updates'],
n_itr=vv['n_itr'],
discount=vv['discount'],
entropy_bonus=vv['entropy_bonus'],
clip_eps=vv['clip_eps'],
clip_outer=vv['clip_outer'],
target_outer_step=vv['target_outer_step'],
target_inner_step=vv['target_inner_step'],
init_outer_kl_penalty=vv['init_outer_kl_penalty'],
init_inner_kl_penalty=vv['init_inner_kl_penalty'],
adaptive_outer_kl_penalty=vv['adaptive_outer_kl_penalty'],
adaptive_inner_kl_penalty=vv['adaptive_inner_kl_penalty'],
parallel_sampler=vv['parallel_sampler'],
optimizer_args=optimizer_args,
)
algo.train()
def run_train_task(vv):
import sys
print(vv['exp_prefix'])
sysout_log_path = os.path.join(config.LOG_DIR, 'local', vv['exp_prefix'],
vv['exp_name'], 'stdout.log')
sysout_log_file = open(sysout_log_path, 'w')
sys.stdout = sysout_log_file
env = TfEnv(normalize(vv['env'](log_scale_limit=vv['log_scale_limit'])))
dynamics_model = MLPDynamicsEnsemble(
name="dyn_model",
env_spec=env.spec,
hidden_sizes=vv['hidden_sizes_model'],
weight_normalization=vv['weight_normalization_model'],
num_models=vv['num_models'],
valid_split_ratio=vv['valid_split_ratio'],
rolling_average_persitency=vv['rolling_average_persitency'])
policy = GaussianMLPPolicy(
name="policy",
env_spec=env.spec,
hidden_sizes=vv['hidden_sizes_policy'],
hidden_nonlinearity=vv['hidden_nonlinearity_policy'],
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = ModelTRPO(
env=env,
policy=policy,
dynamics_model=dynamics_model,
baseline=baseline,
batch_size_env_samples=vv['batch_size_env_samples'],
batch_size_dynamics_samples=vv['batch_size_dynamics_samples'],
initial_random_samples=vv['initial_random_samples'],
num_gradient_steps_per_iter=vv['num_gradient_steps_per_iter'],
max_path_length=vv['path_length'],
n_itr=vv['n_itr'],
retrain_model_when_reward_decreases=vv[
'retrain_model_when_reward_decreases'],
discount=vv['discount'],
step_size=vv["step_size"],
reset_policy_std=vv['reset_policy_std'],
reinit_model_cycle=vv['reinit_model_cycle'])
algo.train()
sysout_log_file.close()
def run_train_task(vv):
env = TfEnv(normalize(vv['env'](log_scale_limit=vv['log_scale_limit'])))
dynamics_model = BadDynamicsEnsemble(
name="dyn_model",
env_spec=env.spec,
hidden_sizes=vv['hidden_sizes_model'],
weight_normalization=vv['weight_normalization_model'],
num_models=vv['num_models'],
output_bias_range=vv['output_bias_range'],
gaussian_noise_output_std=vv['output_noise_std'],
)
policy = GaussianMLPPolicy(
name="policy",
env_spec=env.spec,
hidden_sizes=vv['hidden_sizes_policy'],
hidden_nonlinearity=vv['hidden_nonlinearity_policy'],
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = ModelTRPO(
env=env,
policy=policy,
dynamics_model=dynamics_model,
baseline=baseline,
batch_size_env_samples=vv['batch_size_env_samples'],
batch_size_dynamics_samples=vv['batch_size_dynamics_samples'],
initial_random_samples=vv['initial_random_samples'],
dynamic_model_epochs=vv['dynamic_model_epochs'],
num_gradient_steps_per_iter=vv['num_gradient_steps_per_iter'],
retrain_model_when_reward_decreases=vv[
'retrain_model_when_reward_decreases'],
max_path_length=vv['path_length'],
n_itr=vv['n_itr'],
discount=vv['discount'],
step_size=vv["step_size"],
reset_policy_std=vv['reset_policy_std'],
reinit_model_cycle=vv['reinit_model_cycle'],
resample_output_bias=vv['resample_output_bias'])
algo.train()
def run_train_task(vv):
env = TfEnv(normalize(vv['env']()))
policy = MAMLGaussianMLPPolicy(
name="policy",
env_spec=env.spec,
num_tasks=vv['meta_batch_size'],
hidden_sizes=vv['hidden_sizes'],
grad_step_size=vv['fast_lr'],
hidden_nonlinearity=vv['hidden_nonlinearity'],
trainable_step_size=vv['trainable_step_size'],
bias_transform=vv['bias_transform'])
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = MAMLPPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=vv['fast_batch_size'], # number of trajs for grad update
max_path_length=vv['path_length'],
meta_batch_size=vv['meta_batch_size'],
num_grad_updates=vv['num_grad_updates'],
n_itr=vv['n_itr'],
discount=vv['discount'],
entropy_bonus=vv['entropy_bonus'],
clip_eps=vv['clip_eps'],
clip_outer=vv['clip_outer'],
target_outer_step=vv['target_outer_step'],
target_inner_step=vv['target_inner_step'],
init_outer_kl_penalty=vv['init_outer_kl_penalty'],
init_inner_kl_penalty=vv['init_inner_kl_penalty'],
adaptive_outer_kl_penalty=vv['adaptive_outer_kl_penalty'],
adaptive_inner_kl_penalty=vv['adaptive_inner_kl_penalty'],
max_epochs=vv['max_epochs'],
num_batches=vv['num_batches'],
tf_optimizer_args=dict(learning_rate=vv['outer_lr']),
parallel_sampler=vv['parallel_sampler'],
multi_adam=vv['multi_adam'],
)
algo.train()
def run_train_task(vv):
env = TfEnv(
normalize(vv['env'](init_sampling_boundaries=vv['point_env_setup']
['init_sampling_boundaries'],
goal=vv['point_env_setup']['goal'])))
dynamics_model = PointEnvFakeModelEnsemble(
env_spec=env.spec,
num_models=vv['num_models'],
error_range_around_goal=vv['fake_model_setup']
['error_range_around_goal'],
bias_range=vv['fake_model_setup']['bias_range'],
error_std=vv['fake_model_setup']['error_std'],
goal=vv['point_env_setup']['goal'],
error_at_goal=vv['fake_model_setup']['error_at_goal'],
smooth_error=vv['smooth_error'],
)
policy = MAMLGaussianMLPPolicy(
name="policy",
env_spec=env.spec,
hidden_sizes=vv['hidden_sizes_policy'],
hidden_nonlinearity=vv['hidden_nonlinearity_policy'],
grad_step_size=vv['fast_lr'],
trainable_step_size=vv['trainable_step_size'],
bias_transform=vv['bias_transform'],
num_tasks=vv['meta_batch_size'])
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = ModelMAMLTRPO(
env=env,
policy=policy,
dynamics_model=dynamics_model,
baseline=baseline,
n_itr=vv['n_itr'],
n_iter=vv['n_itr'],
batch_size_env_samples=vv['batch_size_env_samples'],
batch_size_dynamics_samples=vv['batch_size_dynamics_samples'],
meta_batch_size=vv['meta_batch_size'],
initial_random_samples=vv['initial_random_samples'],
num_maml_steps_per_iter=vv['num_maml_steps_per_iter'],
reset_from_env_traj=vv.get('reset_from_env_traj', False),
max_path_length_env=vv['path_length_env'],
max_path_length_dyn=vv.get('path_length_dyn', None),
dynamic_model_max_epochs=vv.get('dynamic_model_max_epochs',
(500, 500)),
discount=vv['discount'],
step_size=vv["meta_step_size"],
num_grad_updates=1,
retrain_model_when_reward_decreases=vv[
'retrain_model_when_reward_decreases'],
reset_policy_std=vv['reset_policy_std'],
reinit_model_cycle=vv['reinit_model_cycle'],
frac_gpu=vv.get('frac_gpu', 0.85),
log_real_performance=True,
clip_obs=vv.get('clip_obs', True),
entropy_bonus=vv['entropy_bonus'],
)
algo.train()