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本篇內(nèi)容主要講解“Python如何實(shí)現(xiàn)自動(dòng)駕駛訓(xùn)練模型”,感興趣的朋友不妨來看看。本文介紹的方法操作簡單快捷,實(shí)用性強(qiáng)。下面就讓小編來帶大家學(xué)習(xí)“Python如何實(shí)現(xiàn)自動(dòng)駕駛訓(xùn)練模型”吧!
gym是用于開發(fā)和比較強(qiáng)化學(xué)習(xí)算法的工具包,在python中安裝gym庫和其中子場(chǎng)景都較為簡便。
安裝gym:
pip install gym
安裝自動(dòng)駕駛模塊,這里使用Edouard Leurent發(fā)布在github上的包highway-env:
pip install --user git+https://github.com/eleurent/highway-env
其中包含6個(gè)場(chǎng)景:
高速公路——“highway-v0”
匯入——“merge-v0”
環(huán)島——“roundabout-v0”
泊車——“parking-v0”
十字路口——“intersection-v0”
賽車道——“racetrack-v0”
安裝好后即可在代碼中進(jìn)行實(shí)驗(yàn)(以高速公路場(chǎng)景為例):
import gym import highway_env %matplotlib inline env = gym.make('highway-v0') env.reset() for _ in range(3): action = env.action_type.actions_indexes["IDLE"] obs, reward, done, info = env.step(action) env.render()
運(yùn)行后會(huì)在模擬器中生成如下場(chǎng)景:
綠色為ego vehicle env類有很多參數(shù)可以配置,具體可以參考原文檔。
(1)state
highway-env包中沒有定義傳感器,車輛所有的state (observations) 都從底層代碼讀取,節(jié)省了許多前期的工作量。根據(jù)文檔介紹,state (ovservations) 有三種輸出方式:Kinematics,Grayscale Image和Occupancy grid。
Kinematics
輸出V*F的矩陣,V代表需要觀測(cè)的車輛數(shù)量(包括ego vehicle本身),F(xiàn)代表需要統(tǒng)計(jì)的特征數(shù)量。例:
數(shù)據(jù)生成時(shí)會(huì)默認(rèn)歸一化,取值范圍:[100, 100, 20, 20],也可以設(shè)置ego vehicle以外的車輛屬性是地圖的絕對(duì)坐標(biāo)還是對(duì)ego vehicle的相對(duì)坐標(biāo)。
在定義環(huán)境時(shí)需要對(duì)特征的參數(shù)進(jìn)行設(shè)定:
config = \ { "observation": { "type": "Kinematics", #選取5輛車進(jìn)行觀察(包括ego vehicle) "vehicles_count": 5, #共7個(gè)特征 "features": ["presence", "x", "y", "vx", "vy", "cos_h", "sin_h"], "features_range": { "x": [-100, 100], "y": [-100, 100], "vx": [-20, 20], "vy": [-20, 20] }, "absolute": False, "order": "sorted" }, "simulation_frequency": 8, # [Hz] "policy_frequency": 2, # [Hz] }
Grayscale Image
生成一張W*H的灰度圖像,W代表圖像寬度,H代表圖像高度
Occupancy grid
生成一個(gè)WHF的三維矩陣,用W*H的表格表示ego vehicle周圍的車輛情況,每個(gè)格子包含F(xiàn)個(gè)特征。
(2) action
highway-env包中的action分為連續(xù)和離散兩種。連續(xù)型action可以直接定義throttle和steering angle的值,離散型包含5個(gè)meta actions:
ACTIONS_ALL = { 0: 'LANE_LEFT', 1: 'IDLE', 2: 'LANE_RIGHT', 3: 'FASTER', 4: 'SLOWER' }
(3) reward
highway-env包中除了泊車場(chǎng)景外都采用同一個(gè)reward function:
這個(gè)function只能在其源碼中更改,在外層只能調(diào)整權(quán)重。(泊車場(chǎng)景的reward function原文檔里有,懶得打公式了……)
DQN網(wǎng)絡(luò)的結(jié)構(gòu)和搭建過程已經(jīng)在我另一篇文章中討論過,所以這里不再詳細(xì)解釋。我采用第一種state表示方式——Kinematics進(jìn)行示范。
由于state數(shù)據(jù)量較小(5輛車*7個(gè)特征),可以不考慮使用CNN,直接把二維數(shù)據(jù)的size[5,7]轉(zhuǎn)成[1,35]即可,模型的輸入就是35,輸出是離散action數(shù)量,共5個(gè)。
import torch import torch.nn as nn from torch.autograd import Variable import torch.nn.functional as F import torch.optim as optim import torchvision.transforms as T from torch import FloatTensor, LongTensor, ByteTensor from collections import namedtuple import random Tensor = FloatTensor EPSILON = 0 # epsilon used for epsilon greedy approach GAMMA = 0.9 TARGET_NETWORK_REPLACE_FREQ = 40 # How frequently target netowrk updates MEMORY_CAPACITY = 100 BATCH_SIZE = 80 LR = 0.01 # learning rate class DQNNet(nn.Module): def __init__(self): super(DQNNet,self).__init__() self.linear1 = nn.Linear(35,35) self.linear2 = nn.Linear(35,5) def forward(self,s): s=torch.FloatTensor(s) s = s.view(s.size(0),1,35) s = self.linear1(s) s = self.linear2(s) return s class DQN(object): def __init__(self): self.net,self.target_net = DQNNet(),DQNNet() self.learn_step_counter = 0 self.memory = [] self.position = 0 self.capacity = MEMORY_CAPACITY self.optimizer = torch.optim.Adam(self.net.parameters(), lr=LR) self.loss_func = nn.MSELoss() def choose_action(self,s,e): x=np.expand_dims(s, axis=0) if np.random.uniform() < 1-e: actions_value = self.net.forward(x) action = torch.max(actions_value,-1)[1].data.numpy() action = action.max() else: action = np.random.randint(0, 5) return action def push_memory(self, s, a, r, s_): if len(self.memory) < self.capacity: self.memory.append(None) self.memory[self.position] = Transition(torch.unsqueeze(torch.FloatTensor(s), 0),torch.unsqueeze(torch.FloatTensor(s_), 0),\ torch.from_numpy(np.array([a])),torch.from_numpy(np.array([r],dtype='float32')))# self.position = (self.position + 1) % self.capacity def get_sample(self,batch_size): sample = random.sample(self.memory,batch_size) return sample def learn(self): if self.learn_step_counter % TARGET_NETWORK_REPLACE_FREQ == 0: self.target_net.load_state_dict(self.net.state_dict()) self.learn_step_counter += 1 transitions = self.get_sample(BATCH_SIZE) batch = Transition(*zip(*transitions)) b_s = Variable(torch.cat(batch.state)) b_s_ = Variable(torch.cat(batch.next_state)) b_a = Variable(torch.cat(batch.action)) b_r = Variable(torch.cat(batch.reward)) q_eval = self.net.forward(b_s).squeeze(1).gather(1,b_a.unsqueeze(1).to(torch.int64)) q_next = self.target_net.forward(b_s_).detach() # q_target = b_r + GAMMA * q_next.squeeze(1).max(1)[0].view(BATCH_SIZE, 1).t() loss = self.loss_func(q_eval, q_target.t()) self.optimizer.zero_grad() # reset the gradient to zero loss.backward() self.optimizer.step() # execute back propagation for one step return loss Transition = namedtuple('Transition',('state', 'next_state','action', 'reward'))
各個(gè)部分都完成之后就可以組合在一起訓(xùn)練模型了,流程和用CARLA差不多,就不細(xì)說了。
初始化環(huán)境(DQN的類加進(jìn)去就行了):
import gym import highway_env from matplotlib import pyplot as plt import numpy as np import time config = \ { "observation": { "type": "Kinematics", "vehicles_count": 5, "features": ["presence", "x", "y", "vx", "vy", "cos_h", "sin_h"], "features_range": { "x": [-100, 100], "y": [-100, 100], "vx": [-20, 20], "vy": [-20, 20] }, "absolute": False, "order": "sorted" }, "simulation_frequency": 8, # [Hz] "policy_frequency": 2, # [Hz] } env = gym.make("highway-v0") env.configure(config)
訓(xùn)練模型:
dqn=DQN() count=0 reward=[] avg_reward=0 all_reward=[] time_=[] all_time=[] collision_his=[] all_collision=[] while True: done = False start_time=time.time() s = env.reset() while not done: e = np.exp(-count/300) #隨機(jī)選擇action的概率,隨著訓(xùn)練次數(shù)增多逐漸降低 a = dqn.choose_action(s,e) s_, r, done, info = env.step(a) env.render() dqn.push_memory(s, a, r, s_) if ((dqn.position !=0)&(dqn.position % 99==0)): loss_=dqn.learn() count+=1 print('trained times:',count) if (count%40==0): avg_reward=np.mean(reward) avg_time=np.mean(time_) collision_rate=np.mean(collision_his) all_reward.append(avg_reward) all_time.append(avg_time) all_collision.append(collision_rate) plt.plot(all_reward) plt.show() plt.plot(all_time) plt.show() plt.plot(all_collision) plt.show() reward=[] time_=[] collision_his=[] s = s_ reward.append(r) end_time=time.time() episode_time=end_time-start_time time_.append(episode_time) is_collision=1 if info['crashed']==True else 0 collision_his.append(is_collision)
我在代碼中添加了一些畫圖的函數(shù),在運(yùn)行過程中就可以掌握一些關(guān)鍵的指標(biāo),每訓(xùn)練40次統(tǒng)計(jì)一次平均值。
平均碰撞發(fā)生率:
epoch平均時(shí)長(s):
平均reward:
可以看出平均碰撞發(fā)生率會(huì)隨訓(xùn)練次數(shù)增多逐漸降低,每個(gè)epoch持續(xù)的時(shí)間會(huì)逐漸延長(如果發(fā)生碰撞epoch會(huì)立刻結(jié)束)
到此,相信大家對(duì)“Python如何實(shí)現(xiàn)自動(dòng)駕駛訓(xùn)練模型”有了更深的了解,不妨來實(shí)際操作一番吧!這里是億速云網(wǎng)站,更多相關(guān)內(nèi)容可以進(jìn)入相關(guān)頻道進(jìn)行查詢,關(guān)注我們,繼續(xù)學(xué)習(xí)!
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