本文整理匯總了Python中chainer.optimizers.RMSpropGraves方法的典型用法代碼示例。如果您正苦於以下問題：Python optimizers.RMSpropGraves方法的具體用法？Python optimizers.RMSpropGraves怎麽用？Python optimizers.RMSpropGraves使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類chainer.optimizers的用法示例。
在下文中一共展示了optimizers.RMSpropGraves方法的8個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: __init__
# 需要導入模塊: from chainer import optimizers [as 別名]
# 或者: from chainer.optimizers import RMSpropGraves [as 別名]
def __init__(self, n_history, n_act):
print("Initializing DQN...")
self.step = 0 # number of steps that DQN is updated
self.n_act = n_act
self.n_history = n_history # Number of obervations used to construct the single state
print("Model Building")
self.model = ActionValue(n_history, n_act).to_gpu()
self.model_target = copy.deepcopy(self.model)
print("Initizlizing Optimizer")
self.optimizer = optimizers.RMSpropGraves(lr=0.00025, alpha=0.95, momentum=0.95, eps=0.01)
self.optimizer.setup(self.model)
# History Data : D=[s, a, r, s_dash, end_episode_flag]
hs = self.n_history
ims = self.img_size
self.replay_buffer = [np.zeros((self.data_size, hs, ims, ims), dtype=np.uint8),
np.zeros(self.data_size, dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.float32),
np.zeros((self.data_size, hs, ims, ims), dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.bool)] 示例2: __init__
# 需要導入模塊: from chainer import optimizers [as 別名]
# 或者: from chainer.optimizers import RMSpropGraves [as 別名]
def __init__(self, n_history, n_act):
print("Initializing DQN...")
self.step = 0 # number of steps that DQN is updated
self.n_act = n_act
self.n_history = n_history # Number of obervations used to construct the single state
print("Model Building")
self.model = ActionValue(n_history, n_act)
self.model_target = copy.deepcopy(self.model)
print("Initizlizing Optimizer")
self.optimizer = optimizers.RMSpropGraves(lr=0.00025, alpha=0.95, momentum=0.95, eps=0.01)
self.optimizer.setup(self.model)
# History Data : D=[s, a, r, s_dash, end_episode_flag]
hs = self.n_history
ims = self.img_size
self.replay_buffer = [np.zeros((self.data_size, hs, ims, ims), dtype=np.uint8),
np.zeros(self.data_size, dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.float32),
np.zeros((self.data_size, hs, ims, ims), dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.bool)] 示例3: _test_load_dqn
# 需要導入模塊: from chainer import optimizers [as 別名]
# 或者: from chainer.optimizers import RMSpropGraves [as 別名]
def _test_load_dqn(self, gpu):
q_func = links.Sequence(
links.NatureDQNHead(),
L.Linear(512, 4),
DiscreteActionValue)
opt = optimizers.RMSpropGraves(
lr=2.5e-4, alpha=0.95, momentum=0.0, eps=1e-2)
opt.setup(q_func)
rbuf = replay_buffer.ReplayBuffer(100)
explorer = explorers.LinearDecayEpsilonGreedy(
start_epsilon=1.0, end_epsilon=0.1,
decay_steps=10 ** 6,
random_action_func=lambda: np.random.randint(4))
agent = agents.DQN(q_func, opt, rbuf, gpu=gpu, gamma=0.99,
explorer=explorer, replay_start_size=50,
target_update_interval=10 ** 4,
clip_delta=True,
update_interval=4,
batch_accumulator='sum',
phi=lambda x: x)
model, exists = download_model("DQN", "BreakoutNoFrameskip-v4",
model_type=self.pretrained_type)
agent.load(model)
if os.environ.get('CHAINERRL_ASSERT_DOWNLOADED_MODEL_IS_CACHED'):
assert exists 示例4: create
# 需要導入模塊: from chainer import optimizers [as 別名]
# 或者: from chainer.optimizers import RMSpropGraves [as 別名]
def create(self):
return optimizers.RMSpropGraves(0.1) 示例5: __init__
# 需要導入模塊: from chainer import optimizers [as 別名]
# 或者: from chainer.optimizers import RMSpropGraves [as 別名]
def __init__(self, enable_controller=[0, 3, 4]):
self.num_of_actions = len(enable_controller)
self.enable_controller = enable_controller # Default setting : "Pong"
print "Initializing DQN..."
# Initialization for Chainer 1.1.0 or older.
# print "CUDA init"
# cuda.init()
print "Model Building"
self.model = FunctionSet(
l1=F.Convolution2D(4, 16, ksize=8, stride=4, wscale=np.sqrt(2)),
l2=F.Convolution2D(16, 32, ksize=4, stride=2, wscale=np.sqrt(2)),
l3=F.Linear(2592, 256),
q_value=F.Linear(256, self.num_of_actions,
initialW=np.zeros((self.num_of_actions, 256),
dtype=np.float32))
).to_gpu()
print "Initizlizing Optimizer"
self.optimizer = optimizers.RMSpropGraves(lr=0.0002, alpha=0.3, momentum=0.2)
self.optimizer.setup(self.model.collect_parameters())
# History Data : D=[s, a, r, s_dash, end_episode_flag]
self.D = [np.zeros((self.data_size, 4, 84, 84), dtype=np.uint8),
np.zeros(self.data_size, dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.int8),
np.zeros((self.data_size, 4, 84, 84), dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.bool)] 示例6: __init__
# 需要導入模塊: from chainer import optimizers [as 別名]
# 或者: from chainer.optimizers import RMSpropGraves [as 別名]
def __init__(self, enable_controller=[0, 3, 4]):
self.num_of_actions = len(enable_controller)
self.enable_controller = enable_controller # Default setting : "Pong"
print "Initializing DQN..."
# Initialization of Chainer 1.1.0 or older.
# print "CUDA init"
# cuda.init()
print "Model Building"
self.model = FunctionSet(
l1=F.Convolution2D(4, 32, ksize=8, stride=4, nobias=False, wscale=np.sqrt(2)),
l2=F.Convolution2D(32, 64, ksize=4, stride=2, nobias=False, wscale=np.sqrt(2)),
l3=F.Convolution2D(64, 64, ksize=3, stride=1, nobias=False, wscale=np.sqrt(2)),
l4=F.Linear(3136, 512, wscale=np.sqrt(2)),
q_value=F.Linear(512, self.num_of_actions,
initialW=np.zeros((self.num_of_actions, 512),
dtype=np.float32))
).to_gpu()
self.model_target = copy.deepcopy(self.model)
print "Initizlizing Optimizer"
self.optimizer = optimizers.RMSpropGraves(lr=0.00025, alpha=0.95, momentum=0.95, eps=0.0001)
self.optimizer.setup(self.model.collect_parameters())
# History Data : D=[s, a, r, s_dash, end_episode_flag]
self.D = [np.zeros((self.data_size, 4, 84, 84), dtype=np.uint8),
np.zeros(self.data_size, dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.int8),
np.zeros((self.data_size, 4, 84, 84), dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.bool)] 示例7: setup_optimizer
# 需要導入模塊: from chainer import optimizers [as 別名]
# 或者: from chainer.optimizers import RMSpropGraves [as 別名]
def setup_optimizer(self):
optimizer = optimizers.RMSpropGraves(
lr=self.args.start_lr, alpha=0.95, momentum=0.9, eps=1e-08)
optimizer.setup(self)
optimizer.add_hook(chainer.optimizer.GradientClipping(self.args.grad_clip))
return optimizer 示例8: __init__
# 需要導入模塊: from chainer import optimizers [as 別名]
# 或者: from chainer.optimizers import RMSpropGraves [as 別名]
def __init__(self, use_gpu, enable_controller, dim):
self.use_gpu = use_gpu
self.num_of_actions = len(enable_controller)
self.enable_controller = enable_controller
self.dim = dim
print("Initializing Q-Network...")
hidden_dim = 256
self.model = FunctionSet(
l4=F.Linear(self.dim*self.hist_size, hidden_dim, wscale=np.sqrt(2)),
q_value=F.Linear(hidden_dim, self.num_of_actions,
initialW=np.zeros((self.num_of_actions, hidden_dim),
dtype=np.float32))
)
if self.use_gpu >= 0:
self.model.to_gpu()
self.model_target = copy.deepcopy(self.model)
self.optimizer = optimizers.RMSpropGraves(lr=0.00025, alpha=0.95, momentum=0.95, eps=0.0001)
self.optimizer.setup(self.model.collect_parameters())
# History Data : D=[s, a, r, s_dash, end_episode_flag]
self.d = [np.zeros((self.data_size, self.hist_size, self.dim), dtype=np.uint8),
np.zeros(self.data_size, dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.int8),
np.zeros((self.data_size, self.hist_size, self.dim), dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.bool)]