"""
import torch
from MVC import MVC
import dgl
import torch.nn.functional as F
from Models import ACNet
import time
from copy import deepcopy as dc
import matplotlib.pyplot as plt
import networkx as nx
cuda_flag = True
num_nodes = 40
p_edge = 0.15
mvc = MVC(num_nodes, p_edge)
ndim = mvc.get_graph_dims()
if cuda_flag:
NN = ACNet(ndim, 264, 1).cuda()
else:
NN = ACNet(ndim, 264, 1)
PATH = 'mvc_net.pt'
NN.load_state_dict(torch.load(PATH))
init_state, done = mvc.reset()
pos = nx.spring_layout(init_state.g.to_networkx(), iterations=20)
#### GCN Policy
state = dc(init_state)
if cuda_flag:
"""
Created on Sat Mar 2 16:30:53 2019
@author: orrivlin
"""
from discrete_actor_critic import DiscreteActorCritic
from MVC import MVC
import matplotlib.pyplot as plt
from smooth_signal import smooth
import numpy as np
import time
import torch
n = 19 # number of nodes
p = 0.15 # edge probability
env = MVC(n, p)
cuda_flag = False
alg = DiscreteActorCritic(env, cuda_flag)
num_episodes = 1
lista = []
for i in range(num_episodes):
T1 = time.time()
log = alg.train()
T2 = time.time()
lista.append(log.get_current('tot_return'))
print('---------------------------------------------------')
print('Epoch: {}. R: {}. TD error: {}. H: {}. T: {}'.format(
i, np.round(log.get_current('tot_return'), 2),
np.round(log.get_current('TD_error'), 3),
np.round(log.get_current('entropy'), 3), np.round(T2 - T1, 3)))
