强化学习 Reinforcement Learning（三）—— 是时候用 PARL 框架玩会儿 DOOM 了！！！（下）
呼~~~终于到代码部分了
废话不多说，直接上代码
训练代码
导入库与支持
# -*- coding:utf-8 -*-
from vizdoom import *
import random
import copy
import numpy as np
import parl
from parl import layers
import paddle.fluid as fluid
from parl.utils import logger
import collections
一些超参数与路径参数的设置
# initialize our doomgame environment
game = DoomGame()
# 指定场景文件的存放路径
# game.set_doom_scenario_path(DOOM_PATH)
game.load_config("/usr/local/lib/python3.6/dist-packages/vizdoom/scenarios/basic.cfg")
# 指定地图文件的路径
# game.set_doom_map(MAP_PATH)
# 设置屏幕分辨率和屏幕的格式
game.set_screen_resolution(ScreenResolution.RES_256X160)
game.set_screen_format(ScreenFormat.GRAY8)
# 通过简单的设置True或者False来添加所需的粒子和效果
game.set_render_hud(False)
game.set_render_minimal_hud(False)
game.set_render_crosshair(True)
game.set_render_weapon(True)
game.set_render_decals(True)
game.set_render_particles(True)
game.set_render_effects_sprites(True)
game.set_render_messages(True)
game.set_render_corpses(True)
game.set_render_screen_flashes(True)
# 设置智能体可用的按钮
game.add_available_button(Button.MOVE_LEFT)
game.add_available_button(Button.MOVE_RIGHT)
game.add_available_button(Button.ATTACK)
# 初始化行为数组
actions = np.zeros((game.get_available_buttons_size(), game.get_available_buttons_size()))
count = 0
for i in actions:
i[count] = 1
count += 1
actions = actions.astype(int).tolist()
# 添加游戏变量:弹药、生命力和杀死怪兽个数
game.add_available_game_variable(GameVariable.AMMO0)
game.add_available_game_variable(GameVariable.DAMAGECOUNT)
game.add_available_game_variable(GameVariable.HITCOUNT)
# 设置 episode_timeout ，在经过一些时间步之后终止情景。
# 另外，还设置 episode_start_time ，这对于省略初始事件非常有用
game.set_episode_timeout(6 * 200)
game.set_episode_start_time(10)
game.set_sound_enabled(False)
# 设存活奖励为0
game.set_living_reward(-10)
# doom有效具有不同模式，如玩家、观众、非同步玩家、非同步观众
# 在玩家模式下，智能体将真正玩游戏，因此，在此采用玩家模式
game.set_mode(Mode.PLAYER)
# initialize the game environment
game.init()
构建模型，主要是一个 CNN + 假装的 RNN（雾）
# Model
class Model(parl.Model):
def __init__(self, num_actions):
# define the hyperparameters of the CNN
# filter size
self.filter_size = 5
# number of filters
self.num_filters = [16, 32, 64]
# stride size
self.stride = 2
# pool size
self.poolsize = 2
self.vocab_size = 4000
self.emb_dim = 256
# drop out probability
self.dropout_probability = [0.3, 0.2]
self.act_dim = num_actions
def value(self, obs):
# first convolutional layer
self.conv1 = fluid.layers.conv2d(obs,
num_filters = self.num_filters[0],
filter_size = self.filter_size,
stride = self.stride,
act='relu')
self.pool1 = fluid.layers.pool2d(self.conv1,
pool_size = self.poolsize,
pool_type = "max",
pool_stride = self.stride)
# second convolutional layer
self.conv2 = fluid.layers.conv2d_transpose(self.pool1,
num_filters = self.num_filters[1],
filter_size = self.filter_size,
stride = self.stride,
act='relu')
self.pool2 = fluid.layers.pool2d(self.conv2,
pool_size = self.poolsize,
pool_type = "max",
pool_stride = self.stride)
# third convolutional layer
self.conv3 = fluid.layers.conv2d(self.pool2,
num_filters = self.num_filters[1],
filter_size = self.filter_size,
stride = self.stride,
act='relu')
self.pool3 = fluid.layers.pool2d(self.conv3,
pool_size = self.poolsize,
pool_type = "max",
pool_stride = self.stride)
# add dropout and reshape the input
self.fc1 = fluid.layers.fc(self.pool3, size = 512, act="relu")
self.drop1 = fluid.layers.dropout(self.fc1, dropout_prob = self.dropout_probability[0])
self.fc2 = fluid.layers.fc(self.drop1, size = 512, act="relu")
# build RNN(fake)
#self.input = fluid.layers.concat(input = [self.fc2, self.c], axis = 0)
self.tanh = fluid.layers.tanh(self.fc2)
self.o = fluid.layers.softmax(self.tanh)
self.drop2 = fluid.layers.dropout(self.o, dropout_prob = self.dropout_probability[1])
self.prediction = fluid.layers.fc(self.drop2, size = self.act_dim)
return self.prediction
DQN 算法部分
# Algorithm
class DQN(parl.Algorithm):
def __init__(self, model, act_dim=None, gamma=None, lr=None):
"""
Args:
model (parl.Model): 定义Q函数的前向网络结构
act_dim (int): action空间的维度，即有几个action
gamma (float): reward的衰减因子
lr (float): learning rate 学习率.
"""
self.model = model
self.target_model = copy.deepcopy(model)
assert isinstance(act_dim, int)
assert isinstance(gamma, float)
assert isinstance(lr, float)
self.act_dim = act_dim
self.gamma = gamma
self.lr = lr
def predict(self, obs):
"""
使用self.model的value网络来获取 [Q(s,a1),Q(s,a2),...]
"""
return self.model.value(obs)
def learn(self, obs, action, reward, next_obs, terminal):
"""
使用DQN算法更新self.model的value网络
"""
# 从target_model中获取 max Q' 的值，用于计算target_Q
next_pred_value = self.target_model.value(next_obs)
best_v = layers.reduce_max(next_pred_value, dim=1)
best_v.stop_gradient = True # 阻止梯度传递
terminal = layers.cast(terminal, dtype='float32')
target = reward + (1.0 - terminal) * self.gamma * best_v
pred_value = self.model.value(obs) # 获取Q预测值
# 将action转onehot向量，比如：3 => [0,0,0,1,0]
action_onehot = layers.one_hot(action, self.act_dim)
action_onehot = layers.cast(action_onehot, dtype='float32')
# 下面一行是逐元素相乘，拿到action对应的 Q(s,a)
# 比如：pred_value = [[2.3, 5.7, 1.2, 3.9, 1.4]], action_onehot = [[0,0,0,1,0]]
# ==> pred_action_value = [[3.9]]
pred_action_value = layers.reduce_sum(
layers.elementwise_mul(action_onehot, pred_value), dim=1)
# 计算 Q(s,a) 与 target_Q的均方差，得到loss
cost = layers.square_error_cost(pred_action_value, target)
cost = layers.reduce_mean(cost)
optimizer = fluid.optimizer.Adam(learning_rate=self.lr) # 使用Adam优化器
optimizer.minimize(cost)
return cost
def sync_target(self):
"""
把 self.model 的模型参数值同步到 self.target_model
"""
self.model.sync_weights_to(self.target_model)
定义智能体与经验回放部分
# Agent
class Agent(parl.Agent):
def __init__(self,
algorithm,
obs_dim,
act_dim,
e_greed=0.1,
e_greed_decrement=0):
assert isinstance(obs_dim, list)
assert isinstance(act_dim, int)
self.obs_dim = obs_dim
self.act_dim = act_dim
super(Agent, self).__init__(algorithm)
self.global_step = 0
self.update_target_steps = 200 # 每隔200个training steps再把model的参数复制到target_model中
self.e_greed = e_greed # 有一定概率随机选取动作，探索
self.e_greed_decrement = e_greed_decrement # 随着训练逐步收敛，探索的程度慢慢降低
def build_program(self):
self.pred_program = fluid.Program()
self.learn_program = fluid.Program()
with fluid.program_guard(self.pred_program): # 搭建计算图用于 预测动作，定义输入输出变量
obs = layers.data(name='obs', shape=self.obs_dim, dtype='float32')
self.value = self.alg.predict(obs)
with fluid.program_guard(self.learn_program): # 搭建计算图用于 更新Q网络，定义输入输出变量
obs = layers.data(name='obs', shape=self.obs_dim, dtype='float32')
action = layers.data(name='act', shape=[1], dtype='int32')
reward = layers.data(name='reward', shape=[], dtype='float32')
next_obs = layers.data(name='next_obs', shape=self.obs_dim, dtype='float32')
terminal = layers.data(name='terminal', shape=[], dtype='bool')
self.cost = self.alg.learn(obs, action, reward, next_obs, terminal)
def sample(self, obs):
sample = np.random.rand() # 产生0~1之间的小数
if sample < self.e_greed:
act = np.random.randint(self.act_dim) # 探索：每个动作都有概率被选择
else:
act = self.predict(obs) # 选择最优动作
self.e_greed = max(
0.01, self.e_greed - self.e_greed_decrement) # 随着训练逐步收敛，探索的程度慢慢降低
return act
def predict(self, obs): # 选择最优动作
obs = np.array(obs)
if(obs.shape == (3, 160, 256)):#CHW
pass
elif(obs.shape == (256, 3, 160)):
obs = obs.transpose(1, 2, 0) #NCHW
elif(obs.shape == (160, 256, 3)):
obs = obs.transpose(2, 0, 1) #NCHW
obs = np.expand_dims(obs, axis=0)
pred_Q = self.fluid_executor.run(
self.pred_program,
feed={'obs': obs.astype('float32')},
fetch_list=[self.value])[0]
pred_Q = np.squeeze(pred_Q, axis=0)
act = np.argmax(pred_Q) # 选择Q最大的下标，即对应的动作
return act
def learn(self, obs, act, reward, next_obs, terminal):
# 每隔200个training steps同步一次model和target_model的参数
if self.global_step % self.update_target_steps == 0:
self.alg.sync_target()
self.global_step += 1
obs = np.array(obs)
if(obs.shape == (3, 160, 256)):#CHW
pass
elif(obs.shape == (256, 3, 160)):
obs = obs.transpose(1, 2, 0) #NCHW
elif(obs.shape == (160, 256, 3)):
obs = obs.transpose(2, 0, 1) #NCHW
obs = np.expand_dims(obs, axis=0)
next_obs = np.array(next_obs)
if(next_obs.shape == (3, 160, 256)):#CHW
pass
elif(next_obs.shape == (256, 3, 160)):
next_obs = next_obs.transpose(1, 2, 0) #NCHW
elif(next_obs.shape == (160, 256, 3)):
next_obs = next_obs.transpose(2, 0, 1) #NCHW
next_obs = np.expand_dims(next_obs, axis=0)
act = np.expand_dims(act, -1)
feed = {
'obs': obs.astype('float32'),
'act': act.astype('int32'),
'reward': reward,
'next_obs': next_obs.astype('float32'),
'terminal': terminal
}
cost = self.fluid_executor.run(
self.learn_program, feed=feed, fetch_list=[self.cost])[0] # 训练一次网络
return cost
class ReplayMemory(object):
def __init__(self, max_size):
self.buffer = collections.deque(maxlen=max_size)
# 增加一条经验到经验池中
def append(self, exp):
self.buffer.append(exp)
# 从经验池中选取N条经验出来
def sample(self, batch_size):
mini_batch = random.sample(self.buffer, batch_size)
obs_batch, action_batch, reward_batch, next_obs_batch, done_batch = [], [], [], [], []
for experience in mini_batch:
s, a, r, s_p, done = experience
obs_batch.append(s)
action_batch.append(a)
reward_batch.append(r)
next_obs_batch.append(s_p)
done_batch.append(done)
return np.array(obs_batch[0]).astype('float32'), \
np.array(action_batch).astype('float32'), np.array(reward_batch).astype('float32'),\
np.array(next_obs_batch[0]).astype('float32'), np.array(done_batch).astype('float32')
def __len__(self):
return len(self.buffer)
小技巧，通过将三帧图像合成再输入 CNN 让模型可以获取到运动信息
def frame(state):
n = True
while True:
if n:
frame0 = state.screen_buffer
state = game.get_state()
frame1 = state.screen_buffer
state = game.get_state()
frame2 = state.screen_buffer
else:
frame0 = frame1
frame1 = frame2
frame2 = state.screen_buffer
obs = np.dstack([frame0, frame1, frame2])
n = False
yield obs
训练函数与评估函数
def run_episode(game, agent, rpm):
game.new_episode()
logger.info('start new episode')
state = game.get_state()
frame_gen = frame(state)
obs = next(frame_gen)
step = 0
while not game.is_episode_finished():
state = game.get_state()
step += 1
action = agent.sample(obs) # 采样动作，所有动作都有概率被尝试到
reward = game.make_action(actions[action])
next_obs = next(frame_gen)
next_obs = np.array(next_obs)
if(next_obs.shape == (3, 160, 256)):#CHW
pass
elif(next_obs.shape == (256, 3, 160)):
next_obs = next_obs.transpose(1, 2, 0) #NCHW
elif(next_obs.shape == (160, 256, 3)):
next_obs = next_obs.transpose(2, 0, 1) #NCHW
done = game.is_episode_finished()
rpm.append((obs, action, reward, next_obs, done))
# train model
if (len(rpm) > MEMORY_WARMUP_SIZE) and (step % LEARN_FREQ == 0):
(batch_obs, batch_action, batch_reward, batch_next_obs,
batch_done) = rpm.sample(BATCH_SIZE)
train_loss = agent.learn(batch_obs, batch_action, batch_reward,
batch_next_obs,
batch_done) # s,a,r,s',done
obs = next_obs
total_reward = game.get_total_reward()
print('training reward:'+str(total_reward))
return total_reward
# 评估 agent, 跑 5 个episode，总reward求平均
def evaluate(game, agent, render=False):
eval_reward = []
for i in range(5):
game.new_episode()
episode_reward = 0
while not game.is_episode_finished():
state = game.get_state()
frame_gen = frame(state)
obs = next(frame_gen)
action = agent.predict(obs) # 预测动作，只选最优动作
reward = game.make_action(actions[action])
done = not game.is_episode_finished()
misc = state.game_variables
if render:
game.set_window_visible(render)
episode_reward = game.get_total_reward()
print('evaluate reward:'+str(episode_reward))
eval_reward.append(episode_reward)
return np.mean(eval_reward)
训练主体部分
action_dim = game.get_available_buttons_size()
obs_shape = [-1, 3, 160, 256]
rpm = ReplayMemory(MEMORY_SIZE)
# 根据parl框架构建agent
model = Model(num_actions = action_dim)
algorithm = DQN(model, act_dim = action_dim, gamma=GAMMA, lr=LEARNING_RATE)
agent = Agent(
algorithm,
obs_dim=obs_shape,
act_dim=action_dim,
e_greed=0.15, # 有一定概率随机选取动作，探索
e_greed_decrement=1e-5) # 随着训练逐步收敛，探索的程度慢慢降reward = RewardBuilder()
# 加载模型
# save_path = './DRQN_DOOM.ckpt'
# agent.restore(save_path)
# 先往经验池里存一些数据，避免最开始训练的时候样本丰富度不够
while len(rpm) < MEMORY_WARMUP_SIZE:
run_episode(game, agent, rpm)
# 开始训练
episode = 0
while episode < max_episode: # 训练max_episode个回合，test部分不计算入episode数量
# train part
for i in range(0, 5):
total_reward = run_episode(game, agent, rpm)
episode += 1
logger.info('episode:{} e_greed:{} '.format(
episode, agent.e_greed))
# test part
eval_reward = evaluate(game, agent, render=False) # render=True 查看显示效果
logger.info('episode:{} e_greed:{} test_reward:{}'.format(
episode, agent.e_greed, eval_reward))
# 训练结束，保存模型
save_path = './DQN_DOOM.ckpt'
agent.save(save_path)
game.close()
效果展示
仍需改进的地方
- RNN 是个假的，主要是如果加上 LSTM 等结构，网络进行参数复制的时候会报错
- 奖励函数设计过于粗糙，很有可能会是一个及其稀疏的奖励，收敛性是个大问题
- 运行设施没有 GPU ，模型是用纯 CPU 上网本跑的，参数设置的非常保守
本人以后会发布一些关于机器学习模型算法，自动控制算法的其他文章，也会聊一聊自己做的一些小项目，希望读者朋友们能够喜欢。
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