def __init__(
self,
conv_filters, conv_filter_sizes, conv_strides, conv_pads,
conv_nonlinearity=LN.rectify,
hidden_sizes=[],
hidden_nonlinearity=LN.tanh,
output_pi_nonlinearity=LN.softmax,
pixel_scale=255.,
alternating_sampler=False,
**kwargs
):
"""
The policy consists of several convolution layers followed by recurrent
layers and softmax
:param env_spec: A spec for the mdp.
:param conv_filters, conv_filter_sizes, conv_strides, conv_pads: specify the convolutional layers. See rllab.core.network.ConvNetwork for details.
:param hidden_sizes: list of sizes for the fully connected hidden layers
:param hidden_nonlinearity: nonlinearity used for each hidden layer
:param prob_network: manually specified network for this policy, other network params
are ignored
:param feature_layer_index: index of the feature layer. Default -2 means the last layer before fc-softmax
:param eps: mixture weight on uniform distribution; useful to force exploration
:return:
"""
save_args(vars())
super().__init__(**kwargs)
def __init__(
self,
algo,
policy,
sampler,
seed,
affinities,
rank,
n_runners,
use_gpu=True,
):
save_args(vars(), underscore=False)
def __init__(
self,
rank,
envs,
sync,
segs_buf,
step_buf,
horizon,
max_path_length,
discount,
):
save_args(vars(), underscore=False)
self.n_envs = len(envs)
def __init__(
self,
conv_filters, conv_filter_sizes, conv_strides, conv_pads,
hidden_sizes=[],
hidden_nonlinearity=LN.rectify,
pixel_scale=255.,
epsilon=1,
dueling=False,
shared_last_bias=False,
**kwargs
):
save_args(vars())
super().__init__(**kwargs)
def __init__(
self,
discount,
gae_lambda,
v_loss_coeff=1,
ent_loss_coeff=0.01,
standardize_adv=False,
lr_schedule=None,
):
if lr_schedule is not None and lr_schedule not in LR_SCHEDULES:
raise ValueError("Unrecognized lr_schedule: {}, should be None "
"(for constant) or in: {}".format(
lr_schedule, LR_SCHEDULES))
save_args(vars(), underscore=False)
self.need_extra_obs = True # (signal sent to the sampler)
def __init__(
self,
discount=0.99,
batch_size=32,
min_steps_learn=int(5e4),
delta_clip=1,
replay_size=int(1e6),
training_intensity=8, # avg number of training uses per datum
target_update_steps=int(1e4),
reward_horizon=1,
OptimizerCls=None,
optimizer_args=None,
eps_greedy_args=None,
double_dqn=False,
dueling_dqn=False, # Just a shortcut for optimizer args
prioritized_replay=False,
priority_args=None,
):
save_args(vars(), underscore=False)
opt_args, eps_args, pri_args = self._get_default_sub_args()
if optimizer_args is not None:
opt_args.update(optimizer_args)
if OptimizerCls is None:
OptimizerCls = DqnOptimizer
self.optimizer = OptimizerCls(**opt_args)
if eps_greedy_args is not None:
eps_args.update(eps_greedy_args)
self._eps_initial = eps_args["initial"]
self._eps_final = eps_args["final"]
self._eps_eval = eps_args["eval"]
self._eps_anneal_steps = eps_args["anneal_steps"]
if prioritized_replay:
if priority_args is not None:
pri_args.update(priority_args)
self._priority_beta_initial = pri_args["beta_initial"]
self._priority_beta_final = pri_args["beta_final"]
self._priority_beta_anneal_steps = pri_args["beta_anneal_steps"]
self._priority_args = dict(
alpha=pri_args["alpha"],
beta_initial=pri_args["beta_initial"],
default_priority=pri_args["default_priority"],
)
self.need_extra_obs = False # (for the sampler; should clean this up)
def __init__(
self,
learning_rate,
update_method_name,
update_method_args,
grad_norm_clip=None,
scale_conv_grads=False,
n_update_chunks=3,
):
assert update_method_name in ["rmsprop", "adam"]
save_args(vars(), underscore=True)
self.n_update_chunks = n_update_chunks
if n_update_chunks == 1:
self._push_update = self._single_lock_push
else:
self._push_update = self._cycle_locks_push
def __init__(
self,
conv_filters, conv_filter_sizes, conv_strides, conv_pads,
hidden_sizes=[],
hidden_nonlinearity=LN.rectify,
pixel_scale=255.,
epsilon=1,
# shared_last_bias=False,
factorized=True,
common_noise=False,
sigma_0=0.4,
use_mu_init=True,
**kwargs
):
save_args(vars())
super().__init__(**kwargs)
def __init__(
self,
EnvCls,
env_args,
horizon,
n_parallel=1,
envs_per=1,
max_path_length=np.inf,
mid_batch_reset=True,
max_decorrelation_steps=2000,
profile_pathname=None,
):
save_args(vars(), underscore=False)
self.common_kwargs = vars(self).copy()
self.common_kwargs.pop("n_parallel")
def __init__(
self,
algo,
policy,
sampler,
n_steps,
seed=None,
affinities=None,
use_gpu=True,
):
n_steps = int(n_steps)
save_args(vars(), underscore=False)
if affinities is None:
self.affinities = dict()
if algo.optimizer.parallelism_tag != self.parallelism_tag:
raise TypeError("Had mismatched parallelism between Runner ({}) "
"and Optimizer: {}".format(
self.parallelism_tag,
algo.optimizer.parallelism_tag))