hristian-carabulea

import time
import numpy as np
from collections import deque
from RoadEnv import RoadEnv
from DQNAgent import DQNAgent

# Initialize environment
env = RoadEnv()

# size of input image

hristian-carabulea

import random
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Activation, Flatten
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.models import model_from_yaml
from tensorflow.keras.models import load_model
from collections import deque

hristian-carabulea

import gym
from gym import spaces
import numpy as np
from gym import utils
from random import randint


class Obstacle:

    def __init__(self):

hristian-carabulea

# 
# .... body of model_fn
#

optimizer = tf.train.AdamOptimizer()
  if FLAGS.use_tpu:
    optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer)

  train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
  

hristian-carabulea

# This address identifies the TPU we'll use when configuring TensorFlow.
TPU_WORKER = 'grpc://' + os.environ['COLAB_TPU_ADDR']
tf.logging.set_verbosity(tf.logging.INFO)

resnet_model = tf.contrib.tpu.keras_to_tpu_model(
    resnet_model,
    strategy=tf.contrib.tpu.TPUDistributionStrategy(
        tf.contrib.cluster_resolver.TPUClusterResolver(TPU_WORKER)))

hristian-carabulea

import os
import pprint
import tensorflow as tf

if 'COLAB_TPU_ADDR' not in os.environ:
  print('ERROR: Not connected to a TPU runtime; please see the first cell in this notebook for instructions!')
else:
  tpu_address = 'grpc://' + os.environ['COLAB_TPU_ADDR']
  print ('TPU address is', tpu_address)

hristian-carabulea

#!/usr/bin/env python3
# coding=utf-8

import collections
import sys
import itertools
import matplotlib.pyplot as plt
import matplotlib as mpl
import time
import datetime