1、算法：
Sarsa-lambda 是基于 Sarsa 方法的升级版, 他能更有效率地学习到怎么样获得好的 reward. 如果说 Sarsa 和 Qlearning 都是每次获取到 reward, 只更新获取到 reward 的前一步. 那 Sarsa-lambda 就是更新获取到 reward 的前 lambda 步. lambda 是在 [0, 1] 之间取值,
如果 lambda = 0, Sarsa-lambda 就是 Sarsa, 只更新获取到 reward 前经历的最后一步.
如果 lambda = 1, Sarsa-lambda 更新的是 获取到 reward 前所有经历的步.
2、代码实现：
算法更新同Sarsa；
算法决策：
2.1、主结构：
class SarsaLambdaTable:
# 初始化 (有改变)
def __init__(self, actions, learning_rate=0.01, reward_decay=0.9, e_greedy=0.9, trace_decay=0.9):
# 选行为 (与之前一样)
def choose_action(self, observation):
# 学习更新参数 (有改变)
def learn(self, s, a, r, s_):
# 检测 state 是否存在 (有改变)
def check_state_exist(self, state):2.2、预设值：
在预设值当中, 添加了 trace_decay=0.9 这个就是 lambda 的值了. 这个值将会使得拿到 reward 前的每一步都有价值.
class SarsaLambdaTable(RL): # 继承 RL class
def __init__(self, actions, learning_rate=0.01, reward_decay=0.9, e_greedy=0.9, trace_decay=0.9):
super(SarsaLambdaTable, self).__init__(actions, learning_rate, reward_decay, e_greedy)
# 后向观测算法, eligibility trace.
self.lambda_ = trace_decay
self.eligibility_trace = self.q_table.copy() # 空的 eligibility trace 表2.3、检查state是否存在：
check_state_exist 和之前的是高度相似的. 唯一不同的地方是我们考虑了 eligibility_trace,
class SarsaLambdaTable(RL): # 继承 RL class
def __init__(self, actions, learning_rate=0.01, reward_decay=0.9, e_greedy=0.9, trace_decay=0.9):
...
def check_state_exist(self, state):
if state not in self.q_table.index:
# append new state to q table
to_be_append = pd.Series(
[0] * len(self.actions),
index=self.q_table.columns,
name=state,
)
self.q_table = self.q_table.append(to_be_append)
# also update eligibility trace
self.eligibility_trace = self.eligibility_trace.append(to_be_append)2.4、学习：
有了父类的 RL, 我们这次的编写就很简单, 只需要编写 SarsaLambdaTable 中 learn 这个功能就完成了. 因为其他功能都和父类是一样的. 这就是我们所有的 SarsaLambdaTable 于父类 RL 不同之处的代码. 是不是很简单.
class SarsaLambdaTable(RL): # 继承 RL class
def __init__(self, actions, learning_rate=0.01, reward_decay=0.9, e_greedy=0.9, trace_decay=0.9):
...
def check_state_exist(self, state):
...
def learn(self, s, a, r, s_, a_):
# 这部分和 Sarsa 一样
self.check_state_exist(s_)
q_predict = self.q_table.ix[s, a]
if s_ != 'terminal':
q_target = r + self.gamma * self.q_table.ix[s_, a_]
else:
q_target = r
error = q_target - q_predict
# 这里开始不同:
# 对于经历过的 state-action, 我们让他+1, 证明他是得到 reward 路途中不可或缺的一环
self.eligibility_trace.ix[s, a] += 1
# Q table 更新
self.q_table += self.lr * error * self.eligibility_trace
**# 更有效的更新方式:
self.eligibility_trace.ix[s, :] *= 0
self.eligibility_trace.ix[s, a] = 1**
# 随着时间衰减 eligibility trace 的值, 离获取 reward 越远的步, 他的"不可或缺性"越小
self.eligibility_trace *= self.gamma*self.lambda_2.5、环境：
import numpy as np
np.random.seed(1)
import tkinter as tk
import time
UNIT = 40 # pixels
MAZE_H = 4 # grid height
MAZE_W = 4 # grid width
class Maze(tk.Tk,object):
#注意不要却object
def __init__(self):
super(Maze, self).__init__()
self.action_space = ['u', 'd', 'l', 'r']
self.n_actions = len(self.action_space)
self.title('maze')
self.geometry('{0}x{1}'.format(MAZE_H * UNIT, MAZE_H * UNIT))
self._build_maze()
def _build_maze(self):
self.canvas = tk.Canvas(self, bg='white',
height=MAZE_H * UNIT,
width=MAZE_W * UNIT)
# create grids
for c in range(0, MAZE_W * UNIT, UNIT):
x0, y0, x1, y1 = c, 0, c, MAZE_H * UNIT
self.canvas.create_line(x0, y0, x1, y1)
for r in range(0, MAZE_H * UNIT, UNIT):
x0, y0, x1, y1 = 0, r, MAZE_H * UNIT, r
self.canvas.create_line(x0, y0, x1, y1)
# create origin
origin = np.array([20, 20])
# hell
hell1_center = origin + np.array([UNIT * 2, UNIT])
self.hell1 = self.canvas.create_rectangle(
hell1_center[0] - 15, hell1_center[1] - 15,
hell1_center[0] + 15, hell1_center[1] + 15,
fill='black')
# hell
hell2_center = origin + np.array([UNIT, UNIT * 2])
self.hell2 = self.canvas.create_rectangle(
hell2_center[0] - 15, hell2_center[1] - 15,
hell2_center[0] + 15, hell2_center[1] + 15,
fill='black')
# create oval
oval_center = origin + UNIT * 2
self.oval = self.canvas.create_oval(
oval_center[0] - 15, oval_center[1] - 15,
oval_center[0] + 15, oval_center[1] + 15,
fill='yellow')
# create red rect
self.rect = self.canvas.create_rectangle(
origin[0] - 15, origin[1] - 15,
origin[0] + 15, origin[1] + 15,
fill='red')
# pack all
self.canvas.pack()
def reset(self):
self.update()
time.sleep(0.5)
self.canvas.delete(self.rect)
origin = np.array([20, 20])
self.rect = self.canvas.create_rectangle(
origin[0] - 15, origin[1] - 15,
origin[0] + 15, origin[1] + 15,
fill='red')
# return observation
return self.canvas.coords(self.rect)
def step(self, action):
s = self.canvas.coords(self.rect)
base_action = np.array([0, 0])
if action == 0: # up
if s[1] > UNIT:
base_action[1] -= UNIT
elif action == 1: # down
if s[1] < (MAZE_H - 1) * UNIT:
base_action[1] += UNIT
elif action == 2: # right
if s[0] < (MAZE_W - 1) * UNIT:
base_action[0] += UNIT
elif action == 3: # left
if s[0] > UNIT:
base_action[0] -= UNIT
self.canvas.move(self.rect, base_action[0], base_action[1]) # move agent
s_ = self.canvas.coords(self.rect) # next state
# reward function
if s_ == self.canvas.coords(self.oval):
reward = 1
done = True
elif s_ in [self.canvas.coords(self.hell1), self.canvas.coords(self.hell2)]:
reward = -1
done = True
else:
reward = 0
done = False
return s_, reward, done
def render(self):
time.sleep(0.05)
self.update()
