AshNguyen

class TrafficSimulator_1Lane:


    def __init__(self, initial_density, length, v_max, p_slow_down, random_v = False):
        '''
        Initialize the 1 lane traffic simulator object, also keep track of the flow density as a list
        
        
        Args:
        initial_density (float, between 0 and 1): the initial density of cars on the lane

AshNguyen

class TrafficSimulator_1Lane:


def __init__(self, initial_density, length, v_max, p_slow_down, random_v = False):
'''
Initialize the 1 lane traffic simulator object, also keep track of the flow density as a list


Args:
initial_density (float, between 0 and 1): the initial density of cars on the lane

AshNguyen

class VAE(nn.Module):
    def __init__(self, n_latent):
        super(VAE, self).__init__()

        self.fc1 = nn.Linear(784, 400)
        self.fc21 = nn.Linear(400, n_latent)
        self.fc22 = nn.Linear(400, n_latent)
        self.fc3 = nn.Linear(n_latent, 400)
        self.fc4 = nn.Linear(400, 784)

AshNguyen

class AE(nn.Module):
    def __init__(self, n_latent):
        super(AE, self).__init__()
        self.encoder = nn.Sequential(
            nn.Linear(28 * 28, 128),
            nn.ReLU(True),
            nn.Linear(128, 64),
            nn.ReLU(True), 
            nn.Linear(64, n_latent))
        self.decoder = nn.Sequential(

AshNguyen

def average_prob(p1, p2, p3):
    '''
    Takes three different probability vectors in and outputs a randomly
    sampled action from n_action with probability equals the average \
    probability of the input vectors
    '''
    a = range(n_action)
    p = (p1 + p2 + p3)/3
    a = np.random.choice(a=a, p=p)
    return a

AshNguyen

def average_prob(p1, p2, p3):
    '''
    Takes three different probability vectors in and outputs a randomly
    sampled action from n_action with probability equals the average \
    probability of the input vectors
    '''
    a = range(n_action)
    p = (p1 + p2 + p3)/3
    a = np.random.choice(a=a, p=p)
    return a

AshNguyen

def majority_vote(p1, p2, p3):
'''
Takes three different probability vectors in and outputs a randomly
sampled action from n_action according to majority voting scheme
'''
    a = range(n_action)
    a1 = np.random.choice(a=a, p=p1)
    a2 = np.random.choice(a=a, p=p2)
    a3 = np.random.choice(a=a, p=p3)
    l = [a1, a2, a3]

AshNguyen

AshNguyen

AshNguyen