)
critic = GAE(vfcn, **critic_hparam)
# Subroutine
algo_hparam = dict(
max_iter=200 if policy.name == FNNPolicy.name else 75,
eps_clip=0.12648736789309026,
min_steps=30 * env.max_steps,
num_epoch=7,
batch_size=500,
std_init=0.7573286998997557,
lr=6.999956625305722e-04,
max_grad_norm=1.0,
num_workers=8,
lr_scheduler=lr_scheduler.ExponentialLR,
lr_scheduler_hparam=dict(gamma=0.999),
)
algo = PPO(ex_dir, env, policy, critic, **algo_hparam)
# Save the hyper-parameters
save_dicts_to_yaml(
dict(env=env_hparams, seed=args.seed),
dict(policy=policy_hparam),
dict(critic=critic_hparam, vfcn=vfcn_hparam),
dict(algo=algo_hparam, algo_name=algo.name),
save_dir=ex_dir,
)
# Jeeeha
algo.train(snapshot_mode="latest", seed=args.seed)
standardizer=None,
max_grad_norm=1.0,
lr=5e-4,
)
critic = GAE(vfcn, **critic_hparam)
# Algorithm
algo_hparam = dict(
max_iter=500,
min_steps=20 * env.max_steps,
num_epoch=10,
eps_clip=0.15,
batch_size=512,
max_grad_norm=1.0,
lr=3e-4,
num_workers=12,
)
algo = PPO(ex_dir, env, policy, critic, **algo_hparam)
# Save the hyper-parameters
save_dicts_to_yaml(
dict(env=env_hparams, seed=args.seed),
dict(policy=policy_hparam),
dict(critic=critic_hparam, vfcn=vfcn_hparam),
dict(algo=algo_hparam, algo_name=algo.name),
save_dir=ex_dir,
)
# Jeeeha
algo.train(seed=args.seed, snapshot_mode="best")
def train_and_eval(trial: optuna.Trial, study_dir: str, seed: int):
"""
Objective function for the Optuna `Study` to maximize.
.. note::
Optuna expects only the `trial` argument, thus we use `functools.partial` to sneak in custom arguments.
:param trial: Optuna Trial object for hyper-parameter optimization
:param study_dir: the parent directory for all trials in this study
:param seed: seed value for the random number generators, pass `None` for no seeding
:return: objective function value
"""
# Synchronize seeds between Optuna trials
pyrado.set_seed(seed)
# Environment
env = QBallBalancerSim(dt=1 / 250.0, max_steps=1500)
env = ActNormWrapper(env)
# Learning rate scheduler
lrs_gamma = trial.suggest_categorical("exp_lr_scheduler_gamma",
[None, 0.99, 0.995, 0.999])
if lrs_gamma is not None:
lr_sched = lr_scheduler.ExponentialLR
lr_sched_hparam = dict(gamma=lrs_gamma)
else:
lr_sched, lr_sched_hparam = None, dict()
# Policy
policy = FNNPolicy(
spec=env.spec,
hidden_sizes=trial.suggest_categorical("hidden_sizes_policy",
[(16, 16), (32, 32), (64, 64)]),
hidden_nonlin=fcn_from_str(
trial.suggest_categorical("hidden_nonlin_policy",
["to_tanh", "to_relu"])),
)
# Critic
vfcn = FNN(
input_size=env.obs_space.flat_dim,
output_size=1,
hidden_sizes=trial.suggest_categorical("hidden_sizes_critic",
[(16, 16), (32, 32), (64, 64)]),
hidden_nonlin=fcn_from_str(
trial.suggest_categorical("hidden_nonlin_critic",
["to_tanh", "to_relu"])),
)
critic_hparam = dict(
batch_size=250,
gamma=trial.suggest_uniform("gamma_critic", 0.99, 1.0),
lamda=trial.suggest_uniform("lamda_critic", 0.95, 1.0),
num_epoch=trial.suggest_int("num_epoch_critic", 1, 10),
lr=trial.suggest_loguniform("lr_critic", 1e-5, 1e-3),
standardize_adv=trial.suggest_categorical("standardize_adv_critic",
[True, False]),
max_grad_norm=trial.suggest_categorical("max_grad_norm_critic",
[None, 1.0, 5.0]),
lr_scheduler=lr_sched,
lr_scheduler_hparam=lr_sched_hparam,
)
critic = GAE(vfcn, **critic_hparam)
# Algorithm
algo_hparam = dict(
num_workers=1, # parallelize via optuna n_jobs
max_iter=300,
batch_size=250,
min_steps=trial.suggest_int("num_rollouts_algo", 10, 30) *
env.max_steps,
num_epoch=trial.suggest_int("num_epoch_algo", 1, 10),
eps_clip=trial.suggest_uniform("eps_clip_algo", 0.05, 0.2),
std_init=trial.suggest_uniform("std_init_algo", 0.5, 1.0),
lr=trial.suggest_loguniform("lr_algo", 1e-5, 1e-3),
max_grad_norm=trial.suggest_categorical("max_grad_norm_algo",
[None, 1.0, 5.0]),
lr_scheduler=lr_sched,
lr_scheduler_hparam=lr_sched_hparam,
)
algo = PPO(osp.join(study_dir, f"trial_{trial.number}"), env, policy,
critic, **algo_hparam)
# Train without saving the results
algo.train(snapshot_mode="latest", seed=seed)
# Evaluate
min_rollouts = 1000
sampler = ParallelRolloutSampler(env,
policy,
num_workers=1,
min_rollouts=min_rollouts)
ros = sampler.sample()
mean_ret = sum([r.undiscounted_return() for r in ros]) / min_rollouts
return mean_ret
env = DomainRandWrapperLive(env, randomizer)
# Policy
policy = to.load(osp.join(ref_ex_dir, 'policy.pt'))
policy.init_param()
# Critic
vfcn = to.load(osp.join(ref_ex_dir, 'valuefcn.pt'))
vfcn.init_param()
critic = GAE(vfcn, **hparams['critic'])
# Algorithm
algo_hparam = hparams['subrtn']
algo_hparam.update({'num_workers': 1}) # should be equivalent to the number of cores per job
# algo_hparam.update({'max_iter': 300})
# algo_hparam.update({'max_iter': 600})
# algo_hparam.update({'min_steps': 3*algo_hparam['min_steps']})
algo = PPO(ex_dir, env, policy, critic, **algo_hparam)
# Save the hyper-parameters
save_list_of_dicts_to_yaml([
dict(env=env_hparams, seed=args.seed),
dict(policy=hparams['policy']),
dict(critic=hparams['critic']),
dict(algo=algo_hparam, algo_name=algo.name)],
ex_dir
)
# Jeeeha
algo.train(seed=args.seed, snapshot_mode='latest')
lr=7.058326426522811e-4,
max_grad_norm=6.,
lr_scheduler=lr_scheduler.ExponentialLR,
lr_scheduler_hparam=dict(gamma=0.999))
critic = GAE(vfcn, **critic_hparam)
# Subroutine
algo_hparam = dict(max_iter=200,
eps_clip=0.12648736789309026,
min_steps=30 * env.max_steps,
num_epoch=7,
batch_size=500,
std_init=0.7573286998997557,
lr=6.999956625305722e-04,
max_grad_norm=1.,
num_workers=8,
lr_scheduler=lr_scheduler.ExponentialLR,
lr_scheduler_hparam=dict(gamma=0.999))
algo = PPO(ex_dir, env, policy, critic, **algo_hparam)
# Save the hyper-parameters
save_list_of_dicts_to_yaml([
dict(env=env_hparams, seed=args.seed),
dict(policy=policy_hparam),
dict(critic=critic_hparam, vfcn=vfcn_hparam),
dict(algo=algo_hparam, algo_name=algo.name)
], ex_dir)
# Jeeeha
algo.train(snapshot_mode='latest', seed=args.seed)
def train_and_eval(trial: optuna.Trial, study_dir: str, seed: int):
"""
Objective function for the Optuna `Study` to maximize.
.. note::
Optuna expects only the `trial` argument, thus we use `functools.partial` to sneak in custom arguments.
:param trial: Optuna Trial object for hyper-parameter optimization
:param study_dir: the parent directory for all trials in this study
:param seed: seed value for the random number generators, pass `None` for no seeding
:return: objective function value
"""
# Synchronize seeds between Optuna trials
pyrado.set_seed(seed)
# Environment
env_hparams = dict(dt=1/100., max_steps=600)
env = QQubeSwingUpSim(**env_hparams)
env = ActNormWrapper(env)
# Learning rate scheduler
lrs_gamma = trial.suggest_categorical('exp_lr_scheduler_gamma', [None, 0.995, 0.999])
if lrs_gamma is not None:
lr_sched = lr_scheduler.ExponentialLR
lr_sched_hparam = dict(gamma=lrs_gamma)
else:
lr_sched, lr_sched_hparam = None, dict()
# Policy
policy_hparam = dict(
hidden_sizes=trial.suggest_categorical('hidden_sizes_policy', [(16, 16), (32, 32), (64, 64)]),
hidden_nonlin=fcn_from_str(trial.suggest_categorical('hidden_nonlin_policy', ['to_tanh', 'to_relu'])),
) # FNN
# policy_hparam = dict(
# hidden_size=trial.suggest_categorical('hidden_size_policy', [16, 32, 64]),
# num_recurrent_layers=trial.suggest_categorical('num_recurrent_layers_policy', [1, 2]),
# ) # LSTM & GRU
policy = FNNPolicy(spec=env.spec, **policy_hparam)
# policy = GRUPolicy(spec=env.spec, **policy_hparam)
# Critic
vfcn_hparam = dict(
hidden_sizes=trial.suggest_categorical('hidden_sizes_critic', [(16, 16), (32, 32), (64, 64)]),
hidden_nonlin=fcn_from_str(trial.suggest_categorical('hidden_nonlin_critic', ['to_tanh', 'to_relu'])),
)
# vfcn_hparam = dict(
# hidden_size=trial.suggest_categorical('hidden_size_critic', [16, 32, 64]),
# num_recurrent_layers=trial.suggest_categorical('num_recurrent_layers_critic', [1, 2]),
# ) # LSTM & GRU
vfcn = FNNPolicy(spec=EnvSpec(env.obs_space, ValueFunctionSpace), **vfcn_hparam)
# vfcn = GRUPolicy(spec=EnvSpec(env.obs_space, ValueFunctionSpace), **vfcn_hparam)
critic_hparam = dict(
batch_size=500,
gamma=trial.suggest_uniform('gamma_critic', 0.98, 1.),
lamda=trial.suggest_uniform('lamda_critic', 0.95, 1.),
num_epoch=trial.suggest_int('num_epoch_critic', 1, 10),
lr=trial.suggest_loguniform('lr_critic', 1e-5, 1e-3),
standardize_adv=trial.suggest_categorical('standardize_adv_critic', [False]),
max_grad_norm=trial.suggest_categorical('max_grad_norm_critic', [None, 1., 5.]),
lr_scheduler=lr_sched,
lr_scheduler_hparam=lr_sched_hparam
)
critic = GAE(vfcn, **critic_hparam)
# Algorithm
algo_hparam = dict(
num_workers=1, # parallelize via optuna n_jobs
max_iter=250,
batch_size=500,
min_steps=trial.suggest_int('num_rollouts_algo', 10, 30)*env.max_steps,
num_epoch=trial.suggest_int('num_epoch_algo', 1, 10),
eps_clip=trial.suggest_uniform('eps_clip_algo', 0.05, 0.2),
std_init=trial.suggest_uniform('std_init_algo', 0.5, 1.0),
lr=trial.suggest_loguniform('lr_algo', 1e-5, 1e-3),
max_grad_norm=trial.suggest_categorical('max_grad_norm_algo', [None, 1., 5.]),
lr_scheduler=lr_sched,
lr_scheduler_hparam=lr_sched_hparam
)
csv_logger = create_csv_step_logger(osp.join(study_dir, f'trial_{trial.number}'))
algo = PPO(osp.join(study_dir, f'trial_{trial.number}'), env, policy, critic, **algo_hparam, logger=csv_logger)
# Train without saving the results
algo.train(snapshot_mode='latest', seed=seed)
# Evaluate
min_rollouts = 1000
sampler = ParallelRolloutSampler(env, policy, num_workers=1,
min_rollouts=min_rollouts) # parallelize via optuna n_jobs
ros = sampler.sample()
mean_ret = sum([r.undiscounted_return() for r in ros])/min_rollouts
return mean_ret