DrBoltzmann

from collections import deque
from itertools import count
import time
import torch
import matplotlib.pyplot as plt
%matplotlib inline

from agent import Agent
import random

DrBoltzmann

import numpy as np

import torch
import torch.nn as nn
import torch.nn.functional as F

def hidden_init(layer):
    fan_in = layer.weight.data.size()[0]
    lim = 1. / np.sqrt(fan_in)
    return (-lim, lim)

DrBoltzmann

import numpy as np
from numpy import random as nprandom
import random
import copy
from collections import namedtuple, deque

from model import Actor, Critic

import torch
import torch.nn.functional as F

DrBoltzmann

import tensorflow as tf
import numpy as np

FC_SIZE = 1024
DTYPE = tf.float32


def _weight_variable(name, shape):
    return tf.get_variable(name, shape, DTYPE, tf.truncated_normal_initializer(stddev=0.1))