def __init__(self, gamma: float, epsilon: float):
# CartPole 環境ロード
self.env = gym.make('CartPole-v0')
# Action
self.list_action = np.array([0, 1])
# Value Function
nn = TorchNN(
self.env.env.observation_space.shape[0],
Layer("fc1", torch.nn.Linear, 16, None, (), {}),
Layer("relu1", torch.nn.ReLU, None, None, (), {}),
Layer("fc2", torch.nn.Linear, 16, None, (), {}),
Layer("relu2", torch.nn.ReLU, None, None, (), {}),
Layer("fc3", torch.nn.Linear, 16, None, (), {}),
Layer("relu3", torch.nn.ReLU, None, None, (), {}),
Layer("fc4", torch.nn.Linear, self.env.env.action_space.n, None,
(), {}),
)
qfunc = DQN(nn,
self.list_action,
gamma=gamma,
batch_size=128,
capacity=10000,
lr=0.0001)
qfunc.to_cuda()
QLearn.__init__(self, qfunc=qfunc, epsilon=epsilon)
# Others
self.done = False
def __init__(self, gamma: float, epsilon: float):
# CartPole 環境ロード
self.env = gym.make('CartPole-v0')
# Action
self.list_action = np.array([0, 1])
# Value Function
self.insize = 20
nn = TorchNN(
self.insize,
Layer("conv1", torch.nn.Conv2d, 64, None, (), {
"kernel_size": 20,
"stride": 10,
}),
Layer("relu1", torch.nn.ReLU, None, None, (), {}),
Layer("conv2", torch.nn.Conv2d, 64, None, (), {
"kernel_size": 5,
"stride": 5
}),
Layer("relu2", torch.nn.ReLU, None, None, (), {}),
Layer("view7", torch.nn.Identity, 64 * 3 * 11, "reshape(x,-1)", (),
{}),
Layer("fc7", torch.nn.Linear, 512, None, (), {}),
Layer("norm7", torch.nn.BatchNorm1d, 0, None, (), {}),
Layer("relu7", torch.nn.ReLU, None, None, (), {}),
Layer("fc8", torch.nn.Linear, 256, None, (), {}),
Layer("norm8", torch.nn.BatchNorm1d, 0, None, (), {}),
Layer("relu8", torch.nn.ReLU, None, None, (), {}),
Layer("fc9", torch.nn.Linear, 64, None, (), {}),
Layer("norm9", torch.nn.BatchNorm1d, 0, None, (), {}),
Layer("relu9", torch.nn.ReLU, None, None, (), {}),
Layer("output", torch.nn.Linear, self.env.env.action_space.n, None,
(), {}),
)
qfunc = DQN(nn,
self.list_action,
gamma=gamma,
batch_size=192,
capacity=1000,
lr=0.001)
qfunc.to_cuda()
QLearn.__init__(self, qfunc=qfunc, epsilon=epsilon)
# Others
self.done = False
self.history = [None for _ in range(self.insize)]
def __init__(self,
epsilon: float,
gamma: float,
file_csv: str = "../data/s59h30megacities_utf8.csv",
n_capital: int = None):
# まずは Base class で初期化して, df を load
TSPModelBase.__init__(self, file_csv=file_csv, n_capital=n_capital)
# Action の定義
self.list_action = np.random.permutation(
self.df["capital_en"].unique())
# State の定義
self.state_mng = StateManager()
self.state_mng.set_state("country",
state_type="onehot",
state_list=self.list_action)
self.state_mng.set_state("history",
state_type="onehot_binary",
state_list=self.list_action)
# DQN の定義
torch_nn = TorchNN(
len(self.state_mng),
Layer("fc1", torch.nn.Linear, 128, None, (), {}),
Layer("norm1", torch.nn.BatchNorm1d, 0, None, (), {}),
Layer("relu1", torch.nn.ReLU, None, None, (), {}),
Layer("fc2", torch.nn.Linear, 256, None, (), {}),
Layer("norm2", torch.nn.BatchNorm1d, 0, None, (), {}),
Layer("relu2", torch.nn.ReLU, None, None, (), {}),
Layer("fc3", torch.nn.Linear, 128, None, (), {}),
Layer("norm3", torch.nn.BatchNorm1d, 0, None, (), {}),
Layer("relu3", torch.nn.ReLU, None, None, (), {}),
Layer("fc4", torch.nn.Linear, len(self.list_action), None, (), {}),
)
qfunc = DQN(torch_nn,
self.list_action,
gamma=gamma,
batch_size=128,
capacity=1000)
qfunc.to_cuda()
QLearn.__init__(self, qfunc=qfunc, epsilon=epsilon)
def __init__(self,
epsilon: float,
gamma: float,
file_csv: str = "../data/s59h30megacities_utf8.csv",
n_capital: int = None):
super(TSPModel5, self).__init__(epsilon,
gamma,
file_csv=file_csv,
n_capital=n_capital)
# DQN の定義
torch_nn = TorchNN(
len(self.state_mng),
Layer("lstm", torch.nn.LSTM, 128, None, (), {}),
Layer("calc1", torch.nn.Identity, None, "rnn_outonly", (), {}),
Layer("calc2", torch.nn.Identity, None, "call_options", (), {}),
Layer("calc3", torch.nn.Identity, None, "rnn_all", (), {}),
Layer("norm1", torch.nn.BatchNorm1d, 0, None, (), {}),
Layer("relu1", torch.nn.ReLU, None, None, (), {}),
Layer("fc2", torch.nn.Linear, 128, None, (), {}),
Layer("norm2", torch.nn.BatchNorm1d, 0, None, (), {}),
Layer("relu2", torch.nn.ReLU, None, None, (), {}),
Layer("fc3", torch.nn.Linear, 64, None, (), {}),
Layer("norm3", torch.nn.BatchNorm1d, 0, None, (), {}),
Layer("relu3", torch.nn.ReLU, None, None, (), {}),
Layer("fc4", torch.nn.Linear, len(self.list_action), None, (), {}),
)
qfunc = DQN(torch_nn,
self.list_action,
gamma=gamma,
batch_size=20,
capacity=100,
unit_memory="episode",
lr=0.001)
self.qfunc = qfunc
self.qfunc.to_cuda()
self.state_history = [] # LSTMなのでepisode中のstate履歴を記憶する
def __init__(self,
epsilon: float,
gamma: float,
file_csv: str = "../data/s59h30megacities_utf8.csv",
n_capital: int = None):
# まずは Base class で初期化して, df を load
TSPModelBase.__init__(self, file_csv=file_csv, n_capital=n_capital)
# Action の定義
self.list_action = np.random.permutation(
self.df["capital_en"].unique())
# DQN の定義
torch_nn = TorchNN(
3,
Layer("conv1", torch.nn.Conv2d, 128, None, (), {
"kernel_size": 5,
"stride": 5,
}),
Layer("relu1", torch.nn.ReLU, None, None, (), {}),
Layer("pool1", torch.nn.MaxPool2d, None, None, (), {
"kernel_size": 2,
"stride": 2,
}),
Layer("conv2", torch.nn.Conv2d, 128, None, (), {
"kernel_size": 3,
"stride": 3,
}),
Layer("relu2", torch.nn.ReLU, None, None, (), {}),
Layer("pool2", torch.nn.MaxPool2d, None, None, (), {
"kernel_size": 2,
"stride": 2,
}),
Layer("conv3", torch.nn.Conv2d, 256, None, (), {
"kernel_size": 2,
"stride": 2,
}),
Layer("relu3", torch.nn.ReLU, None, None, (), {}),
Layer("pool3", torch.nn.MaxPool2d, None, None, (), {
"kernel_size": 3,
"stride": 3,
}),
Layer("view6", torch.nn.Identity, 256 * 1 * 2, "reshape(x,-1)", (),
{}),
Layer("fc7", torch.nn.Linear, 256, None, (), {}),
Layer("norm7", torch.nn.BatchNorm1d, 0, None, (), {}),
Layer("relu7", torch.nn.ReLU, None, None, (), {}),
Layer("fc8", torch.nn.Linear, 128, None, (), {}),
Layer("norm8", torch.nn.BatchNorm1d, 0, None, (), {}),
Layer("relu8", torch.nn.ReLU, None, None, (), {}),
Layer("fc9", torch.nn.Linear, 64, None, (), {}),
Layer("norm9", torch.nn.BatchNorm1d, 0, None, (), {}),
Layer("relu9", torch.nn.ReLU, None, None, (), {}),
Layer("output", torch.nn.Linear, len(self.list_action), None, (),
{}),
)
qfunc = DQN(torch_nn,
self.list_action,
gamma=gamma,
batch_size=128,
capacity=1000,
lr=0.001)
qfunc.to_cuda()
QLearn.__init__(self, qfunc=qfunc, epsilon=epsilon)
def __init__(self, gamma: float, epsilon: float):
# CartPole 環境ロード
self.env = gym.make('CartPole-v0')
# Action
self.list_action = np.array([0, 1])
# Value Function
cnn1 = TorchNN(
3,
Layer("conv1", torch.nn.Conv2d, 128, None, (), {
"kernel_size": 3,
"stride": 1,
}),
Layer("relu1", torch.nn.ReLU, None, None, (), {}),
Layer("pool1", torch.nn.MaxPool2d, None, None, (), {
"kernel_size": 2,
"stride": 2,
}),
Layer("conv2", torch.nn.Conv2d, 128, None, (), {
"kernel_size": 2,
"stride": 1,
}),
Layer("relu2", torch.nn.ReLU, None, None, (), {}),
Layer("pool2", torch.nn.MaxPool2d, None, None, (), {
"kernel_size": 2,
"stride": 2,
}),
Layer("conv3", torch.nn.Conv2d, 64, None, (), {
"kernel_size": 2,
"stride": 2,
}),
Layer("relu3", torch.nn.ReLU, None, None, (), {}),
Layer("pool3", torch.nn.MaxPool2d, None, None, (), {
"kernel_size": 2,
"stride": 2,
}),
Layer("conv4", torch.nn.Conv2d, 64, None, (), {
"kernel_size": 2,
"stride": 2,
}),
Layer("relu4", torch.nn.ReLU, None, None, (), {}),
Layer("pool4", torch.nn.MaxPool2d, None, None, (), {
"kernel_size": 2,
"stride": 2,
}),
Layer("conv5", torch.nn.Conv2d, 32, None, (), {
"kernel_size": 2,
"stride": 1,
}),
Layer("relu5", torch.nn.ReLU, None, None, (), {}),
Layer("pool5", torch.nn.MaxPool2d, None, None, (), {
"kernel_size": 2,
"stride": 2,
}),
)
cnn2 = TorchNN(
3,
Layer("conv1", torch.nn.Conv2d, 128, None, (), {
"kernel_size": 3,
"stride": 1,
}),
Layer("relu1", torch.nn.ReLU, None, None, (), {}),
Layer("pool1", torch.nn.MaxPool2d, None, None, (), {
"kernel_size": 2,
"stride": 2,
}),
Layer("conv2", torch.nn.Conv2d, 128, None, (), {
"kernel_size": 2,
"stride": 1,
}),
Layer("relu2", torch.nn.ReLU, None, None, (), {}),
Layer("pool2", torch.nn.MaxPool2d, None, None, (), {
"kernel_size": 2,
"stride": 2,
}),
Layer("conv3", torch.nn.Conv2d, 64, None, (), {
"kernel_size": 2,
"stride": 2,
}),
Layer("relu3", torch.nn.ReLU, None, None, (), {}),
Layer("pool3", torch.nn.MaxPool2d, None, None, (), {
"kernel_size": 2,
"stride": 2,
}),
Layer("conv4", torch.nn.Conv2d, 64, None, (), {
"kernel_size": 2,
"stride": 2,
}),
Layer("relu4", torch.nn.ReLU, None, None, (), {}),
Layer("pool4", torch.nn.MaxPool2d, None, None, (), {
"kernel_size": 2,
"stride": 2,
}),
Layer("conv5", torch.nn.Conv2d, 32, None, (), {
"kernel_size": 2,
"stride": 1,
}),
Layer("relu5", torch.nn.ReLU, None, None, (), {}),
Layer("pool5", torch.nn.MaxPool2d, None, None, (), {
"kernel_size": 2,
"stride": 2,
}),
)
cnn3 = TorchNN(
3,
Layer("conv1", torch.nn.Conv2d, 128, None, (), {
"kernel_size": 3,
"stride": 1,
}),
Layer("relu1", torch.nn.ReLU, None, None, (), {}),
Layer("pool1", torch.nn.MaxPool2d, None, None, (), {
"kernel_size": 2,
"stride": 2,
}),
Layer("conv2", torch.nn.Conv2d, 128, None, (), {
"kernel_size": 2,
"stride": 1,
}),
Layer("relu2", torch.nn.ReLU, None, None, (), {}),
Layer("pool2", torch.nn.MaxPool2d, None, None, (), {
"kernel_size": 2,
"stride": 2,
}),
Layer("conv3", torch.nn.Conv2d, 64, None, (), {
"kernel_size": 2,
"stride": 2,
}),
Layer("relu3", torch.nn.ReLU, None, None, (), {}),
Layer("pool3", torch.nn.MaxPool2d, None, None, (), {
"kernel_size": 2,
"stride": 2,
}),
Layer("conv4", torch.nn.Conv2d, 64, None, (), {
"kernel_size": 2,
"stride": 2,
}),
Layer("relu4", torch.nn.ReLU, None, None, (), {}),
Layer("pool4", torch.nn.MaxPool2d, None, None, (), {
"kernel_size": 2,
"stride": 2,
}),
Layer("conv5", torch.nn.Conv2d, 32, None, (), {
"kernel_size": 2,
"stride": 1,
}),
Layer("relu5", torch.nn.ReLU, None, None, (), {}),
Layer("pool5", torch.nn.MaxPool2d, None, None, (), {
"kernel_size": 2,
"stride": 2,
}),
)
nn = TorchNN(
None,
Layer("split", torch.nn.Identity, None, "split_numpy_3", (), {}),
Layer("cnn1", cnn1, None, None, (), {}),
Layer("cnn2", cnn2, None, None, (), {}),
Layer("cnn3", cnn3, None, None, (), {}),
Layer("view6", torch.nn.Identity, 3 * 32 * 1 * 4, "combine", (),
{}),
Layer("fc6", torch.nn.Linear, 128, None, (), {}),
Layer("norm6", torch.nn.BatchNorm1d, 0, None, (), {}),
Layer("relu6", torch.nn.ReLU, None, None, (), {}),
Layer("fc7", torch.nn.Linear, 64, None, (), {}),
Layer("norm7", torch.nn.BatchNorm1d, 0, None, (), {}),
Layer("relu7", torch.nn.ReLU, None, None, (), {}),
Layer("output", torch.nn.Linear, self.env.env.action_space.n, None,
(), {}),
)
qfunc = DQN(nn,
self.list_action,
gamma=gamma,
batch_size=8,
capacity=256,
lr=0.0001)
qfunc.to_cuda()
QLearn.__init__(self, qfunc=qfunc, epsilon=epsilon)
# Others
self.done = False
self.history = [None, None, None, None, None]