importrandomimportgymimportnumpy as npimportcollectionsimporttorchimporttorch.nn.functional as Fimportmatplotlib.pyplot as pltfromtqdmimporttqdmfrommodelimportDQNimportrl_utilsfromrl_utilsimportReplayBuffer lr= 1e-2#学习率num_episodes = 200#迭代次数hidden_dim = 128#隐藏层gamma = 0.98epsilon= 0.01target...
!pip install matplotlib import matplotlib print(matplotlib.__version__) #查看版本 import matplotlib.pyplot as plt # 在图中从0 0 到 6 250 画一条直线 默认绘制直线 import numpy as np xpoints = np.array([0,6]) # 注意是两个x坐标值 ypoints = np.array([0,250]) plt.plot(xpoints , y...
desc='Iteration %d' % i) as pbar:for i_episode in range(int(num_episodes / 10)):episode_return = 0state = env.reset()done = Falsewhile not done:action = agent
model_selection import train_test_split %matplotlib inline # Treatment effect function def exp_te(x): return np.exp(2*x[0]) # DGP constants np.random.seed(123) n = 2000 n_w = 30 support_size = 5 n_x = 1 # Outcome support support_Y = np.random.choice(np.arange(n_w), size=...
import matplotlib.pyplot as plt import rl_utils from tqdm import tqdm class Qnet(torch.nn.Module): ''' 只有一层隐藏层的Q网络 ''' def __init__(self, state_dim, hidden_dim, action_dim): super(Qnet, self).__init__() self.fc1 = torch.nn.Linear(state_dim, hidden_dim) ...
import matplotlib from sklearn.model_selection import train_test_split %matplotlib inline # Treatment effect function def exp_te(x): return np.exp(2*x[0]) # DGP constants np.random.seed(123) n = 2000 n_w = 30 support_size = 5 ...
importrandomimportnumpyasnpimportmatplotlib.pyplotaspltimportgymnasiumasgymclassCliffWalkAgent:gama=0.85# For SARSAisEpsilon=Trueepsilon_value=1epsilon_final=0epsilon_decay=True# S:48 A:4v_s_value:np.ndarray q_sa_value:np.ndarray q_sa_value_best:np.ndarray ...
In Fig. 2 we show ray propagation for four of the profiles (2), the integration of the eikonal equation and plotting of the ray trajectories was carried out using the Python SciPy44, Numpy45, and Matplotlib46 libraries. Fig. 2: Double-layer lenses with foci at infinity. The Generalized ...
Plot Features The animation includes a color-coded representation of the probability density. Axes are labeled in nanometers for clarity. A color bar helps interpret the probability density values. Step 4: (Optional) Saving the Animation To save the animation as a video, uncomment the relevant co...
Python | Create a bar chart using matplotlib.pyplot Python | Create a bar graph with using matplotlib.pyplot Python | Create a stacked bar using matplotlib.pyplot Python | Create a scatter plot using matplotlib.pyplot Python | Create pie-chart using matplotlib.pyplot Python | Create stack plot ...