Carla自动驾驶强化学习

carla强化学习

来自youtube大神的代码。
自己首先复制一下项目旨在跑通，然后再精读。
自己在tf2.0环境下一直没有跑通，于是改为
因为自己下载的carla版本限制了python只能用3.7

conda create  -n carla python=3.7
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple  tensorflow==1.14
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple  keras==2.2.5 

import glob
import os
import sys
import random
import time
import numpy as np
import cv2
import math
from collections import deque
from keras.applications.xception import Xception
from keras.layers import Dense, GlobalAveragePooling2D
from keras.optimizers import Adam
from keras.models import Model
from keras.callbacks import TensorBoard

import tensorflow as tf
import keras.backend.tensorflow_backend as backend
from threading import Thread

from tqdm import tqdm

try:
    sys.path.append(glob.glob('../carla/dist/carla-*%d.%d-%s.egg' % (
        sys.version_info.major,
        sys.version_info.minor,
        'win-amd64' if os.name == 'nt' else 'linux-x86_64'))[0])
except IndexError:
    pass
import carla


SHOW_PREVIEW = False
IM_WIDTH = 640
IM_HEIGHT = 480
SECONDS_PER_EPISODE = 10
REPLAY_MEMORY_SIZE = 5_000
MIN_REPLAY_MEMORY_SIZE = 1_000
MINIBATCH_SIZE = 16
PREDICTION_BATCH_SIZE = 1
TRAINING_BATCH_SIZE = MINIBATCH_SIZE // 4
UPDATE_TARGET_EVERY = 5
MODEL_NAME = "Xception"

MEMORY_FRACTION = 0.4
MIN_REWARD = -200

EPISODES = 100

DISCOUNT = 0.99
epsilon = 1
EPSILON_DECAY = 0.95 ## 0.9975 99975
MIN_EPSILON = 0.001

AGGREGATE_STATS_EVERY = 10


# Own Tensorboard class
class ModifiedTensorBoard(TensorBoard):

    # Overriding init to set initial step and writer (we want one log file for all .fit() calls)
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.step = 1
        self.writer = tf.summary.FileWriter(self.log_dir)

    # Overriding this method to stop creating default log writer
    def set_model(self, model):
        pass

    # Overrided, saves logs with our step number
    # (otherwise every .fit() will start writing from 0th step)
    def on_epoch_end(self, epoch, logs=None):
        self.update_stats(**logs)

    # Overrided
    # We train for one batch only, no need to save anything at epoch end
    def on_batch_end(self, batch, logs=None):
        pass

    # Overrided, so won't close writer
    def on_train_end(self, _):
        pass

    # Custom method for saving own metrics
    # Creates writer, writes custom metrics and closes writer
    def update_stats(self, **stats):
        self._write_logs(stats, self.step)


class CarEnv:
    SHOW_CAM = SHOW_PREVIEW
    STEER_AMT = 1.0
    im_width = IM_WIDTH
    im_height = IM_HEIGHT
    front_camera = None

    def __init__(self):
        self.client = carla.Client("localhost", 2000)
        self.client.set_timeout(2.0)
        self.world = self.client.get_world()
        self.blueprint_library = self.world.get_blueprint_library()
        self.model_3 = self.blueprint_library.filter("model3")[0]

    def reset(self):
        self.collision_hist = []
        self.<