PPO.train_eval(
root_dir=sim_dir,
random_seed=seed,
num_epochs=4000,
# Params for train
normalize_observations=True,
normalize_rewards=False,
discount_factor=1.0,
lr=1e-4,
lr_schedule=lambda x: 1e-3 if x < 500 else 1e-4,
num_policy_updates=20,
initial_adaptive_kl_beta=0.0,
kl_cutoff_factor=0,
importance_ratio_clipping=0.1,
value_pred_loss_coef=0.005,
# Params for log, eval, save
eval_interval=50,
save_interval=50,
checkpoint_interval=10000,
summary_interval=10000,
# Params for data collection
train_batch_size=train_batch_size,
eval_batch_size=eval_batch_size,
collect_driver=collect_driver,
eval_driver=eval_driver,
replay_buffer_capacity=7000,
# Policy and value networks
ActorNet=actor_distribution_network_gkp.
ActorDistributionNetworkGKP,
actor_fc_layers=(),
value_fc_layers=(),
use_rnn=True,
actor_lstm_size=(12, ),
value_lstm_size=(12, ))
PPO.train_eval(
root_dir=root_dir,
random_seed=0,
num_epochs=300,
# Params for train
normalize_observations=True,
normalize_rewards=False,
discount_factor=1.0,
lr=1e-3,
lr_schedule=None,
num_policy_updates=20,
initial_adaptive_kl_beta=0.0,
kl_cutoff_factor=0,
importance_ratio_clipping=0.1,
value_pred_loss_coef=0.005,
gradient_clipping=1.0,
entropy_regularization=0,
# Params for log, eval, save
eval_interval=10,
save_interval=10,
checkpoint_interval=10000,
summary_interval=10,
# Params for data collection
train_batch_size=train_batch_size,
eval_batch_size=eval_batch_size,
collect_driver=collect_driver,
eval_driver=eval_driver,
replay_buffer_capacity=15000,
# Policy and value networks
ActorNet=actor_distribution_network.ActorDistributionNetwork,
zero_means_kernel_initializer=True,
actor_fc_layers=(),
value_fc_layers=(),
use_rnn=False,
actor_lstm_size=(12, ),
value_lstm_size=(12, ))
PPO.train_eval(
root_dir=root_dir,
random_seed=0,
# Params for collect
num_iterations=100000,
train_batch_size=100,
replay_buffer_capacity=15000,
# Params for train
normalize_observations=True,
normalize_rewards=False,
discount_factor=1.0,
lr=3e-4,
lr_schedule=None,
num_policy_epochs=20,
initial_adaptive_kl_beta=0.0,
kl_cutoff_factor=0,
importance_ratio_clipping=0.1,
value_pred_loss_coef=0.005,
# Params for log, eval, save
eval_batch_size=1000,
eval_interval=100,
save_interval=500,
checkpoint_interval=5000,
summary_interval=100,
# Params for environment
simulate='Alec_universal_gate_set',
horizon=1,
clock_period=6,
attention_step=1,
train_episode_length=lambda x: 6,
eval_episode_length=6,
init_state='vac',
reward_kwargs=reward_kwargs,
encoding='square',
action_script='Alec_universal_gate_set_6round',
to_learn={
'alpha': True,
'beta': True,
'phi': True
},
# Policy and value networks
ActorNet=actor_distribution_network_gkp.ActorDistributionNetworkGKP,
actor_fc_layers=(),
value_fc_layers=(),
use_rnn=True,
actor_lstm_size=(12, ),
value_lstm_size=(12, ),
**kwargs)
