强化深度学习中使用Dyna-Q算法和优先遍历算法在机器人实战中的对比分析（超详细 附源码）
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一、优先遍历
在随机环境中，值函数变化的大小以及状态-动作对更新的优先级都受迁移概率估计值的影响，可以根据紧急程度对其更新顺序进行优先级排序，这就是优先遍历。
优先遍历是一种常用的提高规划效率的分布计算方法，在一定程度上，该方法可以避免随机选择状态和动作所导致的低效率问题，在使用优先遍历法时，用一个优先队列PQueue来存储值函数变化较大的状态-动作对，并以动作值函数改变的大小作为优先级P来对其进行排序，然后依据该优先队列依次产生模拟经验
算法流程图如下
二、在扫地机器人中实战
在扫地机器人环境中，分别用n=10，n=30的Dyna-Q算法和优先遍历算法进行训练，将到达垃圾状态和充电状态的迁移奖赏均设置为+1，其他情况迁移奖赏均设置为-0.1，参数为0.9，但是学习率为0.1.
实验结果表明：在除了到达垃圾和充电状态之外，其他奖赏都为负值的情况下，优先遍历算法运行效果好于Dyna-Q算法，能更快地找到最优路径
效果如下
现在将设置改为：除了到达垃圾和充电状态之外，其他奖赏都为0，同样采用如下条件的算法进行训练，此时优先遍历算法效果略低于Dyna-Q算法
可能原因如下
1：Q值更新次数太少
2：扫地机器人环境设计状态空间较小，在大规模空间中优先遍历算法优势大
3：优先遍历算法只对靠近终止位置的状态进行规划更新
效果如下
图一代码如下
from random import random, choice
import queue as Q
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
import gym
from gym import spaces
from gym.utils import seeding
class Grid(object):
def __init__(self, x:int = None,
y:int = None,
type:int = 0,
reward:float = 0.0):
self.x = x # 坐标x
self.y = y
self.type = type # 类别值（0：空；1：障碍或边界）
self.reward = reward # 该格子的即时奖励
self.name = None # 该格子的名称
self._update_name()
def _update_name(self):
self.name = "X{0}-Y{1}".format(self.x, self.y)
def __str__(self):
return "name:{3}, x:{0}, y:{1}, type:{2}".format(self.x,
self.y,
self.type,
self.name
)
class GridMatrix(object):
def __init__(self, n_width:int, # 水平方向格子数
n_height:int, # 竖直方向格子数
default_type:int = 0, # 默认类型
default_reward:float = 0.0, # 默认即时奖励值
):
self.grids = None
self.n_height = n_height
self.n_width = n_width
self.len = n_width * n_height
self.default_reward = default_reward
self.default_type = default_type
self.reset()
def reset(self):
self.grids = []
for x in range(self.n_height):
for y in range(self.n_width):
self.grids.append(Grid(x,
y,
self.default_type,
self.default_reward))
def get_grid(self, x, y=None):
'''获取一个格子信息
args:坐标信息，由x，y表示或仅有一个类型为tuple的x表示
return:grid object
'''
xx, yy = None, None
if isinstance(x, int):
xx, yy = x, y
elif isinstance(x, tuple):
xx, yy = x[0], x[1]
assert(xx >= 0 and yy >= 0 and xx < self.n_width and yy < self.n_height), "任意坐标值应在合理区间"
index = yy * self.n_width + xx
return self.grids[index]
def set_reward(self, x, y, reward):
grid = self.get_grid(x, y)
if grid is not None:
grid.reward = reward
else:
raise("grid doesn't exist")
def set_type(self, x, y, type):
grid = self.get_grid(x, y)
if grid is not None:
grid.type = type
else:
raise("grid doesn't exist")
def get_reward(self, x, y):
grid = self.get_grid(x, y)
if grid is None:
return None
return grid.reward
def get_type(self, x, y):
grid = self.get_grid(x, y)
if grid is None:
return None
return grid.type
class GridWorldEnv(gym.Env):
metadata = {
'render.modes': ['human', 'rgb_array'],
'video.frames_per_second': 30
}
def __init__(self, n_width: int=5,
n_height: int = 5,
u_size=40,
default_reward: float = 0.0,
default_type=0):
self.u_size = u_size # 当前格子绘制尺寸
self.n_width = n_width # 格子世界宽度（以格子数计）
self.n_height = n_height # 高度
self.width = u_size * n_width # 场景宽度 screen width
self.height = u_size * n_height # 场景长度
self.default_reward = default_reward
self.default_type = default_type
self.grids = GridMatrix(n_width=self.n_width,
n_height=self.n_height,
default_reward=self.default_reward,
default_type=self.default_type)
self.reward = 0 # for rendering
self.action = None # for rendering
# 0,1,2,3 represent up, down, left, right
self.action_space = spaces.Discrete(4)
# 观察空间由low和high决定
self.observation_space = spaces.Discrete(self.n_height * self.n_width)
self.ends = [(0, 0)] # 终止格子坐标，可以有多个
self.start = (0, 4) # 起始格子坐标，只有一个
self.types = [(2, 2, 1)]
self.rewards = []
self.refresh_setting()
self.viewer = None # 图形接口对象
self.seed() # 产生一个随机子
self.reset()
def seed(self, seed=None):
# 产生一个随机化时需要的种子，同时返回一个np_random对象，支持后续的随机化生成操作
self.np_random, seed = seeding.np_random(seed)
return [seed]
def step(self, action):
assert self.action_space.contains(action), "%r (%s) invalid" % (action, type(action))
self.action = action # action for rendering
old_x, old_y = self._state_to_xy(self.state)
new_x, new_y = old_x, old_y
if action == 0: new_y += 1 # up
elif action == 1: new_y -= 1 # down
elif action == 2: new_x -= 1 # left
elif action == 3: new_x += 1 # right
# boundary effect
if new_x < 0: new_x = 0
if new_x >= self.n_width: new_x = self.n_width - 1
if new_y < 0: new_y = 0
if new_y >= self.n_height: new_y = self.n_height - 1
# wall effect:
# 类型为1的格子为障碍格子，不可进入
if self.grids.get_type(new_x, new_y) == 1:
new_x, new_y = old_x, old_y
self.reward = self.grids.get_reward(new_x, new_y)
done = self._is_end_state(new_x, new_y)
self.state = self._xy_to_state(new_x, new_y)
# 提供格子世界所有的信息在info内
info = {"x": new_x, "y": new_y, "grids": self.grids}
return self.state, self.reward, done, info
# 将状态变为横纵坐标
def _state_to_xy(self, s):
x = s % self.n_width
y = int((s - x) / self.n_width)
return x, y
def _xy_to_state(self, x, y=None):
if isinstance(x, int):
assert (isinstance(y, int)), "incomplete Position info"
return x + self.n_width * y
elif isinstance(x, tuple):
return x[0] + self.n_width * x[1]
return -1 # 未知状态
def refresh_setting(self):
'''用户在使用该类创建格子世界后可能会修改格子世界某些格子类型或奖励值
的设置，修改设置后通过调用该方法使得设置生效。
'''
for x, y, r in self.rewards:
self.grids.set_reward(x, y, r)
for x, y, t in self.types:
self.grids.set_type(x, y, t)
def reset(self):
self.state = self._xy_to_state(self.start)
return self.state
# 判断是否是终止状态
def _is_end_state(self, x, y=None):
if y is not None:
xx, yy = x, y
elif isinstance(x, int):
xx, yy = self._state_to_xy(x)
else:
assert (isinstance(x, tuple)), "坐标数据不完整"
xx, yy = x[0], x[1]
for end in self.ends:
if xx == end[0] and yy == end[1]:
return True
return False
# 图形化界面
def render(self, mode='human', close=False):
if close:
if self.viewer is not None:
self.viewer.close()
self.viewer = None
return
zero = (0, 0)
u_size = self.u_size
m = 2 # 格子之间的间隙尺寸
# 如果还没有设定屏幕对象，则初始化整个屏幕具备的元素。
if self.viewer is None:
from gym.envs.classic_control import rendering
self.viewer = rendering.Viewer(self.width, self.height)
# 绘制格子
for x in range(self.n_width):
for y in range(self.n_height):
v = [(x * u_size + m, y * u_size + m),
((x + 1) * u_size - m, y * u_size + m),
((x + 1) * u_size - m, (y + 1) * u_size - m),
(x * u_size + m, (y + 1) * u_size - m)]
rect = rendering.FilledPolygon(v)
r = self.grids.get_reward(x, y) / 10
if r < 0:
rect.set_color(0.9 - r, 0.9 + r, 0.9 + r)
elif r > 0:
rect.set_color(0.3, 0.5 + r, 0.3)
else:
rect.set_color(0.9, 0.9, 0.9)
self.viewer.add_geom(rect)
# 绘制边框
v_outline = [(x * u_size + m, y * u_size + m),
((x + 1) * u_size - m, y * u_size + m),
((x + 1) * u_size - m, (y + 1) * u_size - m),
(x * u_size + m, (y + 1) * u_size - m)]
outline = rendering.make_polygon(v_outline, False)
outline.set_linewidth(3)
if self._is_end_state(x, y):
# 给终点方格添加金黄色边框
outline.set_color(0.9, 0.9, 0)
self.viewer.add_geom(outline)
if self.start[0] == x and self.start[1] == y:
outline.set_color(0.5, 0.5, 0.8)
self.viewer.add_geom(outline)
if self.grids.get_type(x, y) == 1: # 障碍格子用深灰色表示
rect.set_color(0.3, 0.3, 0.3)
else:
pass
# 绘制个体
self.agent = rendering.make_circle(u_size / 4, 30, True)
self.agent.set_color(1.0, 1.0, 0.0)
self.viewer.add_geom(self.agent)
self.agent_trans = rendering.Transform()
self.agent.add_attr(self.agent_trans)
# 更新个体位置
x, y = self._state_to_xy(self.state)
self.agent_trans.set_translation((x + 0.5) * u_size, (y + 0.5) * u_size)
return self.viewer.render(return_rgb_array= mode == 'rgb_array')
# 环境参数设定
class Agent():
def __init__(self, env):
self.episode = 1
self.Q = {}
self.actions = [0, 1, 2, 3]
self.position = env.start
self.model={}
self.priority_mode={}
self.q=Q.PriorityQueue()
# 建立模型
def make_model(self, pos, act, reward, next_state):
self.model["{0},{1}".format(pos, act)] = "{0},{1}".format(reward, next_state)
# 模型规划
def q_planning(self,n):
for i in range(0, n):
a = [i for i in self.model.keys()]
done=False
if a != []:
str = choice(a)
pos = str.split(",")[0]+","+str.split(",")[1]
act = int(str.split(",")[2])
reward = float(self.model[str].split(",")[0])
next_state = self.model[str].split(",")[1]+","+self.model[str].split(",")[2]
if next_state == "(8,5)" or next_state == "(1,6)":
done = True
self.updateQ(pos, act, next_state, reward, done)
# 优先序列
def queue_put(self,s,a):
self.chaxunQ(s, a)
reward = self.model["{0},{1}".format(s, a)].split(",")[0]
next_state = self.model["{0},{1}".format(s, a)].split(",")[1]+","+self.model["{0},{1}".format(s,a)].split(",")[2]
argmax_action = self.performmax(next_state)
old_q = self.Q["{0},{1}".format(s, a)]
new_q = self.Q["{0},{1}".format(next_state, argmax_action)]
p = abs(float(reward)+0.9*new_q-old_q)
if p > maque:
self.q.put([p, "{0},{1}".format(s, a)])
# 优先扫描
def priority_sweeping(self):
done = False
while self.q.empty() == False:
elem = self.q.get()
pos = elem[1].split(",")[0]+","+elem[1].split(",")[1]
act = elem[1].split(",")[2]
next_state = self.model["{0},{1}".format(pos, act)].split(",")[1]+","+self.model["{0},{1}".format(pos, act)].split(",")[2]
reward = self.model["{0},{1}".format(pos, act)].split(",")[0]
if next_state == "(0,0)" or next_state == "(4,3)":
done = True
self.updateQ(pos, act, next_state, float(reward), done)
for k in self.model:
state = k.split(",")[0]+","+k.split(",")[1]
state_act = k.split(",")[2]
next_pos = self.model[k].split(",")[1]+","+self.model[k].split(",")[2]
if next_pos == pos:
self.queue_put(state, state_act)
def chaxunQ(self, pos, act):
judge = False
for i in self.Q:
if i == "{0},{1}".format(pos, act):
judge = True
break
if judge == True:
return True
else:
self.Q["{0},{1}".format(pos, act)] = float(format(random()/10000, '.3f'))
return
# 更新状态动作值Q函数
def updateQ(self, pos, action, next_pos, reward, done):
if done == False:
self.chaxunQ(pos, action)
old_q = self.Q["{0},{1}".format(pos, action)]
action1 = self.performmax(next_pos)
# self.chaxunQ(next_pos, action1)
new_q = self.Q["{0},{1}".format(next_pos, action1)]
old_q = old_q + 0.1 * (reward+0.9 * new_q - old_q)
self.Q["{0},{1}".format(pos, action)] = float(format(old_q, '.3f'))
else:
self.chaxunQ(pos, action)
self.Q["{0},{1}".format(pos, action)] = float(format(reward, '.3f'))
# print(pos, action,reward)
# 动作选择策略
def perform(self, pos):
eplison = random()
self.chaxunQ(pos, choice([0, 1, 2, 3]))
if eplison > 1/self.episode:
maxq = -1000
act = ""
for i in self.Q:
list = i.split(",")
state = list[0] + "," + list[1]
if state == str(pos):
if self.Q[i] > maxq:
maxq = self.Q[i]
act = list[2]
return int(act)
else:
return choice([0, 1, 2, 3])
# argmaxQ
def performmax(self, pos):
maxq = -1000
str1 = ""
self.chaxunQ(pos,choice([0,1,2,3]))
for i in self.Q:
list = i.split(",")
state = list[0]+","+list[1]
if state == str(pos):
if self.Q[i] > maxq:
maxq = self.Q[i]
str1 = list[2]
return int(str1)
# Dyna-Q
def run(n):
agent = Agent(env)
total_j = 0
total_r=0
a=[]
b=[]
# env.types = [(2,4,0)]
env.refresh_setting()
for i in range(0, 300):
done = False
j = 0
env.reset()
r = 0
while done == False and j<50:
j = j + 1
state = env._state_to_xy(env.state)
action = agent.perform(state)
next_state, reward, done, info = env.step(action)
next_state = env._state_to_xy(next_state)
# 更新模型
agent.make_model(state, action, reward, next_state)
# 更新Q值
agent.updateQ(state, action, next_state, reward, done)
r += reward
# 模型规划
agent.q_planning(n)
if i >= 2:
total_r += r
a.append(total_r)
agent.episode += 1
total_j += j
b.append(j)
print("回合={0},步数={1},奖赏={2}".format(i, j, '%.3f' % r))
# return (np.array(a)/np.array(total_j)).tolist()
# return a
return a
# 优先扫描
def p_run():
agent = Agent(env)
a = []
total_r = 0
total_j = 0
b = []
for i in range(0, 300):
done = False
j = 0
env.reset()
r = 0
while done == False and j < 50:
j = j+1
state = env._state_to_xy(env.state)
action = agent.perform(state)
next_state, reward, done, info = env.step(action)
next_state = env._state_to_xy(next_state)
agent.make_model(state, action, reward, next_state)
agent.updateQ(state, action, next_state, reward, done)
# 加入优先序列
agent.queue_put(state, action)
r += reward
if i >= 2:
total_r += r
a.append(total_r)
# 优先扫描
agent.priority_sweeping()
agent.episode += 1
# print(agent.Q)
total_j += j
b.append(j)
print("回合={0},步数={1},奖赏={2}".format(i, j, '%.3f' % r))
= [(1, 2, 1), (2, 2, 1), (3, 2, 1), (4, 2, 1), (5, 2, 1), (6, 2, 1), (7, 2, 1), (8, 2, 1), (9, 2, 1),
(10, 2, 1)]
env.rewards = [(8, 5, 1), (1, 6, 1)] # 奖赏值设定
env.start = (4, 0)
env.ends = [(8, 5), (1, 6)]
env.refresh_setting()
x = range(2, 300)
ln1, = plt.plot(x, p_run(), label=u"n=0")
ln2, = plt.plot(x, run(10), label=u"n=10")
plt.xlabel(u'情节', fontproperties=font1)
plt.ylabel(u'累计奖励', fontproperties=font1)
plt.show()图2代码如下
import 扫地机器人gym环境 as bg
from random import random, choice
import queue as Q
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
import gym
from gym import spaces
from gym.utils import seeding
import numpy as np
class Grid(object):
def __init__(self, x: int = None,
y: int = None,
type: int = 0,
reward: float = 0.0):
self.x = x # 坐标x
self.y = y
self.type = type # 类别值（0：空；1：障碍或边界）
self.reward = reward # 该格子的即时奖励
self.name = None # 该格子的名称
self._update_name()
def _update_name(self):
self.name = "X{0}-Y{1}".format(self.x, self.y)
def __str__(self):
return "name:{3}, x:{0}, y:{1}, type:{2}".format(self.x,
self.y,
self.type,
self.name
)
class GridMatrix(object):
def __init__(self, n_width: int, # 水平方向格子数
n_height: int, # 竖直方向格子数
default_type: int = 0, # 默认类型
default_reward: float = 0.0, # 默认即时奖励值
):
self.grids = None
self.n_height = n_height
self.n_width = n_width
self.len = n_width * n_height
self.default_reward = default_reward
self.default_type = default_type
self.reset()
def reset(self):
self.grids = []
for x in range(self.n_height):
for y in range(self.n_width):
self.grids.append(Grid(x,
y,
self.default_type,
self.default_reward))
def get_grid(self, x, y=None):
'''获取一个格子信息
args:坐标信息，由x，y表示或仅有一个类型为tuple的x表示
return:grid object
'''
xx, yy = None, None
if isinstance(x, int):
xx, yy = x, y
elif isinstance(x, tuple):
xx, yy = x[0], x[1]
assert (xx >= 0 and yy >= 0 and xx < self.n_width and yy < self.n_height), "任意坐标值应在合理区间"
index = yy * self.n_width + xx
return self.grids[index]
def set_reward(self, x, y, reward):
grid = self.get_grid(x, y)
if grid is not None:
grid.reward = reward
else:
raise ("grid doesn't exist")
def set_type(self, x, y, type):
grid = self.get_grid(x, y)
if grid is not None:
grid.type = type
else:
raise ("grid doesn't exist")
def get_reward(self, x, y):
grid = self.get_grid(x, y)
if grid is None:
return None
return grid.reward
def get_type(self, x, y):
grid = self.get_grid(x, y)
if grid is None:
return None
return grid.type
# 格子世界环境
class GridWorldEnv(gym.Env):
metadata = {
'render.modes': ['human', 'rgb_array'],
'video.frames_per_second': 30
}
def __init__(self, n_width: int = 5,
n_height: int = 5,
u_size=40,
default_reward: float = 0.0,
default_type=0):
self.u_size = u_size # 当前格子绘制尺寸
self.n_width = n_width # 格子世界宽度（以格子数计）
self.n_height = n_height # 高度
self.width = u_size * n_width # 场景宽度 screen width
self.height = u_size * n_height # 场景长度
self.default_reward = default_reward
self.default_type = default_type
self.grids = GridMatrix(n_width=self.n_width,
n_height=self.n_height,
default_reward=self.default_reward,
default_type=self.default_type)
self.reward = 0 # for rendering
self.action = None # for rendering
# 0,1,2,3 represent up, down, left, right
self.action_space = spaces.Discrete(4)
# 观察空间由low和high决定
self.observation_space = spaces.Discrete(self.n_height * self.n_width)
self.ends = [(0, 0)] # 终止格子坐标，可以有多个
self.start = (0, 4) # 起始格子坐标，只有一个
self.types = [(2, 2, 1)]
self.rewards = []
self.refresh_setting()
self.viewer = None # 图形接口对象
self.seed() # 产生一个随机子
self.reset()
def seed(self, seed=None):
# 产生一个随机化时需要的种子，同时返回一个np_random对象，支持后续的随机化生成操作
self.np_random, seed = seeding.np_random(seed)
return [seed]
def step(self, action):
assert self.action_space.contains(action), "%r (%s) invalid" % (action, type(action))
self.action = action # action for rendering
old_x, old_y = self._state_to_xy(self.state)
new_x, new_y = old_x, old_y
if action == 0:
new_y += 1 # up
elif action == 1:
new_y -= 1 # down
elif action == 2:
new_x -= 1 # left
elif action == 3:
new_x += 1 # right
# boundary effect
if new_x < 0: new_x = 0
if new_x >= self.n_width: new_x = self.n_width - 1
if new_y < 0: new_y = 0
if new_y >= self.n_height: new_y = self.n_height - 1
# wall effect:
# 类型为1的格子为障碍格子，不可进入
if self.grids.get_type(new_x, new_y) == 1:
new_x, new_y = old_x, old_y
self.reward = self.grids.get_reward(new_x, new_y)
done = self._is_end_state(new_x, new_y)
self.state = self._xy_to_state(new_x, new_y)
# 提供格子世界所有的信息在info内
info = {"x": new_x, "y": new_y, "grids": self.grids}
return self.state, self.reward, done, info
# 将状态变为横纵坐标
def _state_to_xy(self, s):
x = s % self.n_width
y = int((s - x) / self.n_width)
return x, y
def _xy_to_state(self, x, y=None):
if isinstance(x, int):
assert (isinstance(y, int)), "incomplete Position info"
return x + self.n_width * y
elif isinstance(x, tuple):
return x[0] + self.n_width * x[1]
return -1 # 未知状态
def refresh_setting(self):
'''用户在使用该类创建格子世界后可能会修改格子世界某些格子类型或奖励值
的设置，修改设置后通过调用该方法使得设置生效。
'''
for x, y, r in self.rewards:
self.grids.set_reward(x, y, r)
for x, y, t in self.types:
self.grids.set_type(x, y, t)
def reset(self):
self.state = self._xy_to_state(self.start)
return self.state
# 判断是否是终止状态
def _is_end_state(self, x, y=None):
if y is not None:
xx, yy = x, y
elif isinstance(x, int):
xx, yy = self._state_to_xy(x)
else:
assert (isinstance(x, tuple)), "坐标数据不完整"
xx, yy = x[0], x[1]
for end in self.ends:
if xx == end[0] and yy == end[1]:
return True
return False
# 图形化界面
def render(self, mode='human', close=False):
if close:
if self.viewer is not None:
self.viewer.close()
self.viewer = None
return
zero = (0, 0)
u_size = self.u_size
m = 2 # 格子之间的间隙尺寸
# 如果还没有设定屏幕对象，则初始化整个屏幕具备的元素。
if self.viewer is None:
from gym.envs.classic_control import rendering
self.viewer = rendering.Viewer(self.width, self.height)
# 绘制格子
for x in range(self.n_width):
for y in range(self.n_height):
v = [(x * u_size + m, y * u_size + m),
((x + 1) * u_size - m, y * u_size + m),
((x + 1) * u_size - m, (y + 1) * u_size - m),
(x * u_size + m, (y + 1) * u_size - m)]
rect = rendering.FilledPolygon(v)
r = self.grids.get_reward(x, y) / 10
if r < 0:
rect.set_color(0.9 - r, 0.9 + r, 0.9 + r)
elif r > 0:
rect.set_color(0.3, 0.5 + r, 0.3)
else:
rect.set_color(0.9, 0.9, 0.9)
self.viewer.add_geom(rect)
# 绘制边框
v_outline = [(x * u_size + m, y * u_size + m),
((x + 1) * u_size - m, y * u_size + m),
((x + 1) * u_size - m, (y + 1) * u_size - m),
(x * u_size + m, (y + 1) * u_size - m)]
outline = rendering.make_polygon(v_outline, False)
outline.set_linewidth(3)
if self._is_end_state(x, y):
# 给终点方格添加金黄色边框
outline.set_color(0.9, 0.9, 0)
self.viewer.add_geom(outline)
if self.start[0] == x and self.start[1] == y:
outline.set_color(0.5, 0.5, 0.8)
self.viewer.add_geom(outline)
if self.grids.get_type(x, y) == 1: # 障碍格子用深灰色表示
rect.set_color(0.3, 0.3, 0.3)
else:
pass
# 绘制个体
self.agent = rendering.make_circle(u_size / 4, 30, True)
self.agent.set_color(1.0, 1.0, 0.0)
self.viewer.add_geom(self.agent)
self.agent_trans = rendering.Transform()
self.agent.add_attr(self.agent_trans)
# 更新个体位置
x, y = self._state_to_xy(self.state)
self.agent_trans.set_translation((x + 0.5) * u_size, (y + 0.5) * u_size)
return self.viewer.render(return_rgb_array=mode == 'rgb_array')
# 环境参数设定
class Agent():
def __init__(self, env):
self.episode = 1
self.Q = {}
self.actions = [0, 1, 2, 3]
self.position = env.start
self.model = {}
self.priority_mode = {}
self.q = Q.PriorityQueue()
# 建立模型
def make_model(self, pos, act, reward, next_state):
self.model["{0},{1}".format(pos, act)] = "{0},{1}".format(reward, next_state)
# 模型规划
def q_planning(self, n):
for i in range(0, n):
a = [i for i in self.model.keys()]
done = False
if a != []:
str = choice(a)
pos = str.split(",")[0] + "," + str.split(",")[1]
act = int(str.split(",")[2])
reward = float(self.model[str].split(",")[0])
next_state = self.model[str].split(",")[1] + "," + self.model[str].split(",")[2]
if next_state == "(8,5)" or next_state == "(1,6)":
done = True
self.updateQ(pos, act, next_state, reward, done)
# 加入优先序列
def queue_put(self, s, a):
self.chaxunQ(s, a)
reward = self.model["{0},{1}".format(s, a)].split(",")[0]
next_state = self.model["{0},{1}".format(s, a)].split(",")[1] + "," + \
self.model["{0},{1}".format(s, a)].split(",")[2]
argmax_action = self.performmax(next_state)
old_q = self.Q["{0},{1}".format(s, a)]
new_q = self.Q["{0},{1}".format(next_state, argmax_action)]
p = abs(float(reward) + 0.9 * new_q - old_q)
if p > maque:
self.q.put([p, "{0},{1}".format(s, a)])
# 优先扫描
def priority_sweeping(self):
done = False
while self.q.empty() == False:
elem = self.q.get()
pos = elem[1].split(",")[0] + "," + elem[1].split(",")[1]
act = elem[1].split(",")[2]
next_state = self.model["{0},{1}".format(pos, act)].split(",")[1] + "," + \
self.model["{0},{1}".format(pos, act)].split(",")[2]
reward = self.model["{0},{1}".format(pos, act)].split(",")[0]
if next_state == "(0,0)" or next_state == "(4,3)":
done = True
self.updateQ(pos, act, next_state, float(reward), done)
for k in self.model:
state = k.split(",")[0] + "," + k.split(",")[1]
state_act = k.split(",")[2]
next_pos = self.model[k].split(",")[1] + "," + self.model[k].split(",")[2]
if next_pos == pos:
self.queue_put(state, state_act)
def chaxunQ(self, pos, act):
judge = False
for i in self.Q:
if i == "{0},{1}".format(pos, act):
judge = True
break
if judge == True:
return True
else:
self.Q["{0},{1}".format(pos, act)] = float(format(random() / 10000, '.3f'))
return
# 更新状态动作值Q函数
def updateQ(self, pos, action, next_pos, reward, done):
if done == False:
self.chaxunQ(pos, action)
old_q = self.Q["{0},{1}".format(pos, action)]
action1 = self.performmax(next_pos)
# self.chaxunQ(next_pos, action1)
new_q = self.Q["{0},{1}".format(next_pos, action1)]
old_q = old_q + 0.1 * (reward + 0.9 * new_q - old_q)
self.Q["{0},{1}".format(pos, action)] = float(format(old_q, '.3f'))
else:
self.chaxunQ(pos, action)
self.Q["{0},{1}".format(pos, action)] = float(format(reward, '.3f'))
# print(pos, action,reward)
# 动作选取策略
def perform(self, pos):
eplison = random()
self.chaxunQ(pos, choice([0, 1, 2, 3]))
if eplison > 1 / self.episode:
maxq = -1000
act = ""
for i in self.Q:
list = i.split(",")
state = list[0] + "," + list[1]
if state == str(pos):
if self.Q[i] > maxq:
maxq = self.Q[i]
act = list[2]
return int(act)
else:
return choice([0, 1, 2, 3])
# argmaxQ
def performmax(self, pos):
maxq = -1000
str1 = ""
self.chaxunQ(pos, choice([0, 1, 2, 3]))
for i in self.Q:
list = i.split(",")
state = list[0] + "," + list[1]
if state == str(pos):
if self.Q[i] > maxq:
maxq = self.Q[i]
str1 = list[2]
return int(str1)
# Dyna-Q
def run(n):
agent = Agent(env)
total_j = 0
total_r = 0
a = []
b = []
# env.types = [(2,4,0)]
env.refresh_setting()
for i in range(0, 300):
done = False
j = 0
env.reset()
r = 0
while done == False and j < 500:
j = j + 1
state = env._state_to_xy(env.state)
action = agent.perform(state)
next_state, reward, done, info = env.step(action)
next_state = env._state_to_xy(next_state)
# 模型更新
agent.make_model(state, action, reward, next_state)
# 更新Q值
agent.updateQ(state, action, next_state, reward, done)
r += reward
# 模型规划
agent.q_planning(n)
total_r += r
a.append(total_r)
agent.episode += 1
total_j += j
b.append(j)
print("回合={0},步数={1},奖赏={2}".format(i, j, '%.3f' % r))
return (np.array(a) / np.array(total_j)).tolist()
# 优先扫描
def p_run():
agent = Agent(env)
a = []
total_r = 0
total_j = 0
b = []
for i in range(0, 300):
done = False
j = 0
env.reset()
r = 0
while done == False and j < 500:
j = j + 1
state = env._state_to_xy(env.state)
action = agent.perform(state)
next_state, reward, done, info = env.step(action)
next_state = env._state_to_xy(next_state)
agent.make_model(state, action, reward, next_state)
agent.updateQ(state, action, next_state, reward, done)
# 加入优先序列
agent.queue_put(state, action)
r += reward
total_r += r
a.append(total_r)
# 优先扫描
agent.priority_sweeping()
agent.episode += 1
total_j += j
b.append(j)
print("回合={0},步数={1},奖赏={2}".format(i, j, '%.3f' % r))
return (np.array(a) / np.array(total_j)).tolist()
if __name__ == "__main__":
maque )] # 奖赏值设定
env.start = (6, 0)
env.ends = [(8, 5), (1, 6)]
env.refresh_setting()
font1 = Fnt1)
plt.show()创作不易 觉得有帮助请点赞关注收藏~~~