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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from __future__ import division
from __future__ import print_function
import itertools as it
from random import sample, randint, random
from time import time, sleep
import numpy as np
import skimage.color, skimage.transform
import tensorflow as tf
from tqdm import trange
import vizdoom as vzd
from argparse import ArgumentParser
# Q-learning settings
learning_rate = 0.00025
# learning_rate = 0.0001
discount_factor = 0.99
epochs = 20
learning_steps_per_epoch = 2000
replay_memory_size = 10000
# NN learning settings
batch_size = 64
# Training regime
test_episodes_per_epoch = 100
# Other parameters
frame_repeat = 12
resolution = (30, 45)
episodes_to_watch = 10
# TODO move to argparser
save_model = True
load_model = False
skip_learning = False
# Configuration file path
DEFAULT_MODEL_SAVEFILE = "/tmp/model"
DEFAULT_CONFIG = "../../scenarios/simpler_basic.cfg"
# config_file_path = "../../scenarios/rocket_basic.cfg"
# config_file_path = "../../scenarios/basic.cfg"
# Converts and down-samples the input image
def preprocess(img):
img = skimage.transform.resize(img, resolution)
img = img.astype(np.float32)
return img
class ReplayMemory:
def __init__(self, capacity):
channels = 1
state_shape = (capacity, resolution[0], resolution[1], channels)
self.s1 = np.zeros(state_shape, dtype=np.float32)
self.s2 = np.zeros(state_shape, dtype=np.float32)
self.a = np.zeros(capacity, dtype=np.int32)
self.r = np.zeros(capacity, dtype=np.float32)
self.isterminal = np.zeros(capacity, dtype=np.float32)
self.capacity = capacity
self.size = 0
self.pos = 0
def add_transition(self, s1, action, s2, isterminal, reward):
self.s1[self.pos, :, :, 0] = s1
self.a[self.pos] = action
if not isterminal:
self.s2[self.pos, :, :, 0] = s2
self.isterminal[self.pos] = isterminal
self.r[self.pos] = reward
self.pos = (self.pos + 1) % self.capacity
self.size = min(self.size + 1, self.capacity)
def get_sample(self, sample_size):
i = sample(range(0, self.size), sample_size)
return self.s1[i], self.a[i], self.s2[i], self.isterminal[i], self.r[i]
def create_network(session, available_actions_count):
# Create the input variables
s1_ = tf.placeholder(tf.float32, [None] + list(resolution) + [1], name="State")
a_ = tf.placeholder(tf.int32, [None], name="Action")
target_q_ = tf.placeholder(tf.float32, [None, available_actions_count], name="TargetQ")
# Add 2 convolutional layers with ReLu activation
conv1 = tf.contrib.layers.convolution2d(s1_, num_outputs=8, kernel_size=[6, 6], stride=[3, 3],
activation_fn=tf.nn.relu,
weights_initializer=tf.contrib.layers.xavier_initializer_conv2d(),
biases_initializer=tf.constant_initializer(0.1))
conv2 = tf.contrib.layers.convolution2d(conv1, num_outputs=8, kernel_size=[3, 3], stride=[2, 2],
activation_fn=tf.nn.relu,
weights_initializer=tf.contrib.layers.xavier_initializer_conv2d(),
biases_initializer=tf.constant_initializer(0.1))
conv2_flat = tf.contrib.layers.flatten(conv2)
fc1 = tf.contrib.layers.fully_connected(conv2_flat, num_outputs=128, activation_fn=tf.nn.relu,
weights_initializer=tf.contrib.layers.xavier_initializer(),
biases_initializer=tf.constant_initializer(0.1))
q = tf.contrib.layers.fully_connected(fc1, num_outputs=available_actions_count, activation_fn=None,
weights_initializer=tf.contrib.layers.xavier_initializer(),
biases_initializer=tf.constant_initializer(0.1))
best_a = tf.argmax(q, 1)
loss = tf.losses.mean_squared_error(q, target_q_)
optimizer = tf.train.RMSPropOptimizer(learning_rate)
# Update the parameters according to the computed gradient using RMSProp.
train_step = optimizer.minimize(loss)
def function_learn(s1, target_q):
feed_dict = {s1_: s1, target_q_: target_q}
l, _ = session.run([loss, train_step], feed_dict=feed_dict)
return l
def function_get_q_values(state):
return session.run(q, feed_dict={s1_: state})
def function_get_best_action(state):
return session.run(best_a, feed_dict={s1_: state})
def function_simple_get_best_action(state):
return function_get_best_action(state.reshape([1, resolution[0], resolution[1], 1]))[0]
return function_learn, function_get_q_values, function_simple_get_best_action
def learn_from_memory():
""" Learns from a single transition (making use of replay memory).
s2 is ignored if s2_isterminal """
# Get a random minibatch from the replay memory and learns from it.
if memory.size > batch_size:
s1, a, s2, isterminal, r = memory.get_sample(batch_size)
q2 = np.max(get_q_values(s2), axis=1)
target_q = get_q_values(s1)
# target differs from q only for the selected action. The following means:
# target_Q(s,a) = r + gamma * max Q(s2,_) if not isterminal else r
target_q[np.arange(target_q.shape[0]), a] = r + discount_factor * (1 - isterminal) * q2
learn(s1, target_q)
def perform_learning_step(epoch):
""" Makes an action according to eps-greedy policy, observes the result
(next state, reward) and learns from the transition"""
def exploration_rate(epoch):
"""# Define exploration rate change over time"""
start_eps = 1.0
end_eps = 0.1
const_eps_epochs = 0.1 * epochs # 10% of learning time
eps_decay_epochs = 0.6 * epochs # 60% of learning time
if epoch < const_eps_epochs:
return start_eps
elif epoch < eps_decay_epochs:
# Linear decay
return start_eps - (epoch - const_eps_epochs) / \
(eps_decay_epochs - const_eps_epochs) * (start_eps - end_eps)
else:
return end_eps
s1 = preprocess(game.get_state().screen_buffer)
# With probability eps make a random action.
eps = exploration_rate(epoch)
if random() <= eps:
a = randint(0, len(actions) - 1)
else:
# Choose the best action according to the network.
a = get_best_action(s1)
reward = game.make_action(actions[a], frame_repeat)
isterminal = game.is_episode_finished()
s2 = preprocess(game.get_state().screen_buffer) if not isterminal else None
# Remember the transition that was just experienced.
memory.add_transition(s1, a, s2, isterminal, reward)
learn_from_memory()
# Creates and initializes ViZDoom environment.
def initialize_vizdoom(config_file_path):
print("Initializing doom...")
game = vzd.DoomGame()
game.load_config(config_file_path)
game.set_window_visible(False)
game.set_mode(vzd.Mode.PLAYER)
game.set_screen_format(vzd.ScreenFormat.GRAY8)
game.set_screen_resolution(vzd.ScreenResolution.RES_640X480)
game.init()
print("Doom initialized.")
return game
if __name__ == '__main__':
parser = ArgumentParser("ViZDoom example showing how to train a simple agent using simplified DQN.")
parser.add_argument(dest="config",
default=DEFAULT_CONFIG,
nargs="?",
help="Path to the configuration file of the scenario."
" Please see "
"../../scenarios/*cfg for more scenarios.")
args = parser.parse_args()
# Create Doom instance
game = initialize_vizdoom(args.config)
# Action = which buttons are pressed
n = game.get_available_buttons_size()
actions = [list(a) for a in it.product([0, 1], repeat=n)]
# Create replay memory which will store the transitions
memory = ReplayMemory(capacity=replay_memory_size)
session = tf.Session()
learn, get_q_values, get_best_action = create_network(session, len(actions))
saver = tf.train.Saver()
if load_model:
print("Loading model from: ", DEFAULT_MODEL_SAVEFILE)
saver.restore(session, DEFAULT_MODEL_SAVEFILE)
else:
init = tf.global_variables_initializer()
session.run(init)
print("Starting the training!")
time_start = time()
if not skip_learning:
for epoch in range(epochs):
print("\nEpoch %d\n-------" % (epoch + 1))
train_episodes_finished = 0
train_scores = []
print("Training...")
game.new_episode()
for learning_step in trange(learning_steps_per_epoch, leave=False):
perform_learning_step(epoch)
if game.is_episode_finished():
score = game.get_total_reward()
train_scores.append(score)
game.new_episode()
train_episodes_finished += 1
print("%d training episodes played." % train_episodes_finished)
train_scores = np.array(train_scores)
print("Results: mean: %.1f±%.1f," % (train_scores.mean(), train_scores.std()), \
"min: %.1f," % train_scores.min(), "max: %.1f," % train_scores.max())
print("\nTesting...")
test_episode = []
test_scores = []
for test_episode in trange(test_episodes_per_epoch, leave=False):
game.new_episode()
while not game.is_episode_finished():
state = preprocess(game.get_state().screen_buffer)
best_action_index = get_best_action(state)
game.make_action(actions[best_action_index], frame_repeat)
r = game.get_total_reward()
test_scores.append(r)
test_scores = np.array(test_scores)
print("Results: mean: %.1f±%.1f," % (
test_scores.mean(), test_scores.std()), "min: %.1f" % test_scores.min(),
"max: %.1f" % test_scores.max())
print("Saving the network weigths to:", DEFAULT_MODEL_SAVEFILE)
saver.save(session, DEFAULT_MODEL_SAVEFILE)
print("Total elapsed time: %.2f minutes" % ((time() - time_start) / 60.0))
game.close()
print("======================================")
print("Training finished. It's time to watch!")
# Reinitialize the game with window visible
game.set_window_visible(True)
game.set_mode(vzd.Mode.ASYNC_PLAYER)
game.init()
for _ in range(episodes_to_watch):
game.new_episode()
while not game.is_episode_finished():
state = preprocess(game.get_state().screen_buffer)
best_action_index = get_best_action(state)
# Instead of make_action(a, frame_repeat) in order to make the animation smooth
game.set_action(actions[best_action_index])
for _ in range(frame_repeat):
game.advance_action()
# Sleep between episodes
sleep(1.0)
score = game.get_total_reward()
print("Total score: ", score)