以下代码演示策略迭代强化算法
前提：
- python语言
- OpenAI gym库
主要演示AI自动寻路的算法。如图：
图中格子从做到右，从上到下依次编号，1~8.暗黄色的圆球，初始随机出现在1~5位置，在格子上移动。移动到黑色点失败，移动到黄色点胜利。
首先，写一个gym环境：grid_map.py，代码如下
import logging
import numpy
import random
from gym import spaces
import gym
logger = logging.getLogger(__name__)
class GridEnv(gym.Env):
metadata = {
'render.modes': ['human', 'rgb_array'],
'video.frames_per_second': 2
}
def __init__(self):
self.states = [1,2,3,4,5,6,7,8] #状态空间
self.x = [140,220,300,380,460,140,300,460]
self.y = [250,250,250,250,250,150,150,150]
self.terminate_states = dict() #终止状态为字典格式
self.terminate_states[6] = 1
self.terminate_states[7] = 1
self.terminate_states[8] = 1
self.actions = ['n','e','s','w']
self.rewards = dict() #回报的数据结构为字典
self.rewards['1_s'] = -1.0
self.rewards['3_s'] = 1.0
self.rewards['5_s'] = -1.0
self.t = dict() #状态转移的数据格式为字典
self.t['1_s'] = 6
self.t['1_e'] = 2
self.t['2_w'] = 1
self.t['2_e'] = 3
self.t['3_s'] = 7
self.t['3_w'] = 2
self.t['3_e'] = 4
self.t['4_w'] = 3
self.t['4_e'] = 5
self.t['5_s'] = 8
self.t['5_w'] = 4
self.gamma = 0.8 #折扣因子
self.viewer = None
self.state = None
def getTerminal(self):
return self.terminate_states
def transform(self,state,action):
key = "%d_%s" % (state, action) #将状态和动作组成字典的键值
r = 0
s = -1
if key in self.rewards:
r = self.rewards[key]
if key in self.t:
s = self.t[key]
return self.t,s,r
def getGamma(self):
return self.gamma
def getStates(self):
return self.states
def getAction(self):
return self.actions
def getTerminate_states(self):
return self.terminate_states
def setAction(self,s):
self.state = s
def seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed)
return [seed]
def step(self, action):
#系统当前状态
state = self.state
if state in self.terminate_states:
return state, 0, True, {}
key = "%d_%s" % (state, action) #将状态和动作组成字典的键值
#状态转移
if key in self.t:
next_state = self.t[key]
else:
next_state = state
self.state = next_state
is_terminal = False
if next_state in self.terminate_states:
is_terminal = True
if key not in self.rewards:
r = 0.0
else:
r = self.rewards[key]
return next_state, r,is_terminal,{}
def reset(self):
self.state = self.states[int(random.random() * 5)]
return self.state
def render(self, mode='human', close=False):
if close:
if self.viewer is not None:
self.viewer.close()
self.viewer = None
return
screen_width = 600
screen_height = 400
if self.viewer is None:
from gym.envs.classic_control import rendering
self.viewer = rendering.Viewer(screen_width, screen_height)
#创建网格世界
self.line1 = rendering.Line((100,300),(500,300))
self.line2 = rendering.Line((100, 200), (500, 200))
self.line3 = rendering.Line((100, 300), (100, 100))
self.line4 = rendering.Line((180, 300), (180, 100))
self.line5 = rendering.Line((260, 300), (260, 100))
self.line6 = rendering.Line((340, 300), (340, 100))
self.line7 = rendering.Line((420, 300), (420, 100))
self.line8 = rendering.Line((500, 300), (500, 100))
self.line9 = rendering.Line((100, 100), (180, 100))
self.line10 = rendering.Line((260, 100), (340, 100))
self.line11 = rendering.Line((420, 100), (500, 100))
#创建第一个骷髅
self.kulo1 = rendering.make_circle(40)
self.circletrans = rendering.Transform(translation=(140,150))
self.kulo1.add_attr(self.circletrans)
self.kulo1.set_color(0,0,0)
#创建第二个骷髅
self.kulo2 = rendering.make_circle(40)
self.circletrans = rendering.Transform(translation=(460, 150))
self.kulo2.add_attr(self.circletrans)
self.kulo2.set_color(0, 0, 0)
#创建金条
self.gold = rendering.make_circle(40)
self.circletrans = rendering.Transform(translation=(300, 150))
self.gold.add_attr(self.circletrans)
self.gold.set_color(1, 0.9, 0)
#创建机器人
self.robot = rendering.make_circle(30)
self.robotrans = rendering.Transform()
self.robot.add_attr(self.robotrans)
self.robot.set_color(0.8, 0.6, 0.4)
self.line1.set_color(0, 0, 0)
self.line2.set_color(0, 0, 0)
self.line3.set_color(0, 0, 0)
self.line4.set_color(0, 0, 0)
self.line5.set_color(0, 0, 0)
self.line6.set_color(0, 0, 0)
self.line7.set_color(0, 0, 0)
self.line8.set_color(0, 0, 0)
self.line9.set_color(0, 0, 0)
self.line10.set_color(0, 0, 0)
self.line11.set_color(0, 0, 0)
self.viewer.add_geom(self.line1)
self.viewer.add_geom(self.line2)
self.viewer.add_geom(self.line3)
self.viewer.add_geom(self.line4)
self.viewer.add_geom(self.line5)
self.viewer.add_geom(self.line6)
self.viewer.add_geom(self.line7)
self.viewer.add_geom(self.line8)
self.viewer.add_geom(self.line9)
self.viewer.add_geom(self.line10)
self.viewer.add_geom(self.line11)
self.viewer.add_geom(self.kulo1)
self.viewer.add_geom(self.kulo2)
self.viewer.add_geom(self.gold)
self.viewer.add_geom(self.robot)
if self.state is None: return None
#self.robotrans.set_translation(self.x[self.state-1],self.y[self.state-1])
self.robotrans.set_translation(self.x[self.state - 1], self.y[self.state - 1])
return self.viewer.render(return_rgb_array=mode == 'rgb_array')
安装到gym环境下：
- 拷贝grid_map.py至gym目录的env/classic_control目录下。
- 修改此目录下的文件__init__.py ，添加一行代码：
from gym.envs.classic_control.grid_map import GridEnv3.到上层目录，修改__init__.py，添加代码：
register(
id='GridWorld-v0',
entry_point='gym.envs.classic_control:GridEnv',
max_episode_steps=500,
reward_threshold = 100.0,
)4.完毕
现在可以开始策略迭代的算法了。代码如下
import gym
import random
import time
env = gym.make('GridWorld-v0')
print(env.env)
print(env.env.states)
STEP = 100
gm = env.env
class Learn:
def __init__(self,grid_mdp):
self.v = dict()
for s in grid_mdp.states:
self.v[s] = 0
self.pi = dict()
self.pi[1] = random.choice(['e','s'])
self.pi[2] = random.choice(['e','w'])
self.pi[3] = random.choice(['e','s','w'])
self.pi[4] = random.choice(['e','w'])
self.pi[5] = random.choice(['w','s'])
def policy_iterate(self,grid_mdp):
for i in range(100):
self.policy_evaluate(grid_mdp)
self.policy_improve(grid_mdp)
def policy_evaluate(self,grid_mdp):
for i in range(1000):
delta = 0.0
for state in grid_mdp.states:
if state in grid_mdp.terminate_states:continue
action = self.pi[state]
t,s,r = grid_mdp.transform(state,action)
if s!= -1:
new_v = r+grid_mdp.gamma*self.v[s]
delta += abs(self.v[state]-new_v)
self.v[state] = new_v
if(delta<1e-6):
break
def policy_improve(self,grid_mdp):
for state in grid_mdp.states:
if state in grid_mdp.terminate_states:continue
a1 = self.pi[state]#grid_mdp.actions[0]
t,s,r = grid_mdp.transform(state,a1)
if s!=-1:
v1 = r+grid_mdp.gamma*self.v[s]
for action in grid_mdp.actions:
t,s,r = grid_mdp.transform(state,action)
if s!= -1:
if v1 < r+grid_mdp.gamma*self.v[s]:
a1 = action
v1 = r+grid_mdp.gamma*self.v[s]
self.pi[state] = a1
def action(self,state):
return self.pi[state]
state = env.reset()
learn = Learn(gm)
learn.policy_iterate(gm)
#print(gm.pi)
total_reward = 0
for j in range(STEP):
env.render()
action = learn.action(state) # direct action for test
state,reward,done,_ = env.step(action)
total_reward += reward
time.sleep(1)
if done:
env.render()
break