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q_learning_sarsa.py
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q_learning_sarsa.py
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import numpy as np
import matplotlib.pyplot as plt
from numpy import linalg as LA
from cProfile import label
class windyGridworld():
def __init__(self,start,end):
# action = [left up right down]
# action = [0 1 2 3]
self.states = np.arange(70).reshape(7,10)
self.action_space = [0,1,2,3]
self.size = 70
self.done = False
self.state = start
self.endstate = end
self.x = 10
self.y = 7
self.Q = np.zeros(len(self.action_space)*self.size).reshape(self.size,len(self.action_space))
# self.Q = np.zeros(len(self.action_space)*self.size).reshape(self.size,len(self.action_space))
# self.Q_q = np.zeros(70).reshape(7,10)
# self.Q = np.array([[ -2.55000000e+01, -3.67000000e+01, -6.73000000e+01, -2.16280000e+03, -4.20800000e+03, -5.67300000e+03, -6.38580000e+03, -5.29090000e+03 ,-2.66340000e+03, -8.64000000e+01],[ -2.55000000e+01, -2.55000000e+01, -3.66000000e+01, -2.76000000e+01, -2.19300000e+02, -1.75900000e+02, -1.04000000e+01, -7.47000000e+01 ,-7.84000000e+01, -8.60000000e+01],[ -2.54000000e+01, -2.54000000e+01, -2.55000000e+01, -2.58000000e+01, -1.13000000e+01, -9.80000000e+00, -8.40000000e+00, -1.86000000e+01, -3.92000000e+01 , -7.82000000e+01],[ -2.52000000e+01, -2.54000000e+01, -2.52000000e+01 , -2.81000000e+01, -1.50800000e+02, -9.67000000e+01, -2.27000000e+01, 0.00000000e+00 ,-1.99000000e+01 ,-3.92000000e+01],[ -2.52000000e+01, -2.51000000e+01, -2.50000000e+01, -2.40000000e+01, -1.93000000e+01, -2.08000000e+01, 0.00000000e+00, -4.60000000e+00, -1.02000000e+01, -1.99000000e+01],[ -2.49000000e+01, -2.46000000e+01, -2.42000000e+01, -2.39000000e+01, -2.20000000e+01, 0.00000000e+00, 0.00000000e+00, -4.50000000e+00, -4.90000000e+00, -1.01000000e+01],[ -2.44000000e+01, -2.44000000e+01 , -2.41000000e+01 , -2.28000000e+01, 0.00000000e+00 , 0.00000000e+00 ,0.00000000e+00 , 0.00000000e+00 ,-4.50000000e+00 ,-4.90000000e+00]])
def _step(self, action,reward):
reward -= 1
# left
pos = np.where(self.states == self.state)
if (action==0):
if (pos[1] == 0):
self.state = self.state
# reward += 1
else:
w = self.wind(pos)
newPos = list(pos)
newPos[0] -= w
if (newPos[0] < 0):
newPos[0] = 0
newPos = tuple(newPos)
self.state = int(self.states[newPos])
self.state = self.state - 1
# right
if (action==2):
if (pos[1] == 9):
self.state = self.state
# reward += 1
else:
w = self.wind(pos)
newPos = list(pos)
newPos[0] -= w
if (newPos[0] < 0):
newPos[0] = 0
newPos = tuple(newPos)
self.state = int(self.states[newPos])
self.state = self.state + 1
# up
if (action==1):
if (pos[0] == 0):
self.state = self.state
# reward += 1
else:
w = self.wind(pos)
newPos = list(pos)
newPos[0] -= w
newPos[0] -=1
if (newPos[0] < 0):
newPos[0] = 0
newPos = tuple(newPos)
self.state = int(self.states[newPos])
# down
if (action==3):
if (pos[0] == 6):
self.state = self.state
# reward += 1
else:
w = self.wind(pos)
newPos = list(pos)
newPos[0] -= w
newPos[0] +=1
if (newPos[0] < 0):
newPos[0] = 0
newPos = tuple(newPos)
self.state = int(self.states[newPos])
if (self.state == self.endstate):
reward = 0
self.done = True
# print('new state: ',self.state, ', reward: ', reward)
return self.state, reward, self.done
def _reset(self):
self.state = start
self.done = False
print('starting state: ', self.state)
def _render(self):
print('current state: ', self.state)
def wind(self,pos):
w = 0
if (int(pos[1])==3 or int(pos[1])==4 or int(pos[1])==5 or int(pos[1])==8):
w=1
elif (int(pos[1])==6 or int(pos[1])==7):
w = 2
return w
def eGreedy(self,epsilon):
m = 100
n = epsilon * 100
p = np.random.randint(m)
if (p>(m-n-1)):
epsilon = 1
else:
epsilon = 0
return epsilon
def plotQ(self,Q):
plt.xlim(-0.5,self.x+0.5)
plt.ylim(-self.y-0.5,0.5)
for i in range(len(Q[:,0])):
l = list(Q[i,:])
a = l.index(max(l))
pos = np.where(self.states == i)
# print('action:',a,'state',i)
if (a == 0):
plt.arrow(int(pos[1])+0.1,-int(pos[0]),-0.2,0,head_width=0.2, head_length=0.2, fc='c', ec='c')
if (a == 1):
plt.arrow(int(pos[1]),-int(pos[0])-0.1,0,0.2,head_width=0.2, head_length=0.2, fc='c', ec='c')
if (a == 2):
plt.arrow(int(pos[1])-0.1,-int(pos[0]),0.2,0,head_width=0.2, head_length=0.2, fc='c', ec='c')
if (a == 3):
plt.arrow(int(pos[1]),-int(pos[0])+0.1,0,-0.2,head_width=0.2, head_length=0.2, fc='c', ec='c')
start = 30
end = 37
episode = 100000
step = 1000
alpha = 0.1
gamma = 1
epsilon = 0.1
num = 0
num_ges = []
num2 = []
count = []
# algo = 0 -> SARSA algo = 1 -> Q-learning
def rlwindy(c,episode,step,alpha,gamma,epsilon,algo):
num2 = []
count = []
if (algo==0):
for j in range(step):
num = 0
row = []
reward = 0
#init a starting state s
c._reset()
#select an action a from s using a policy pi derived from Q e-greedy
e = c.eGreedy(epsilon)
l = list(c.Q[c.state,:])
a = e*np.random.randint(len(c.action_space)) + (1-e)*l.index(max(l))
row.append(c.state)
for i in range(episode):
# print('old_state: ',c.state,' action: ', a, ' epsilon:', e)
s = c.state
#execute a observe r,s'
snew,rnew,done = c._step(a, reward)
row.append(snew)
#Derive pi from Q, then selection action a' from s'
e = c.eGreedy(epsilon)
l = list(c.Q[c.state,:])
anew = e*np.random.randint(len(c.action_space)) + (1-e)*l.index(max(l))
#update Q_q
c.Q[s,a] = c.Q[s,a] + alpha * (rnew + gamma*c.Q[snew,anew] - c.Q[s,a])
#s = s' and a = a'
a = anew
num += 1
# print(c.Q)
if done:
if (j>0):
num_ges.append(num_ges[j-1]+num)
else:
num_ges.append(num)
count.append(j)
num2.append(num)
print('terminal state: ', c.state,'steps: ',num)
if (num < 18):
print(row)
break
num = 0
return c.Q,row,num2,count,num_ges
elif(algo == 1):
for j in range(step):
num = 0
row = []
reward = 0
#init a starting state s
c._reset()
row.append(c.state)
for i in range(episode):
# select an action a from s using a plicy pi derived from Q (epsilon greedy)
e = c.eGreedy(epsilon)
l = list(c.Q[c.state,:])
a = e*np.random.randint(len(c.action_space)) + (1-e)*l.index(max(l))
s = c.state
# execute a, observe r, s'
snew,r,done = c._step(a, reward)
row.append(snew)
#update Q
c.Q[s,a] = c.Q[s,a] + alpha*(r + gamma*max(c.Q[snew,:]) - c.Q[s,a])
#s = s' and a = a'
num += 1
# print(c.Q)
if done:
if (j>0):
num_ges.append(num_ges[j-1]+num)
else:
num_ges.append(num)
count.append(j)
num2.append(num)
print('terminal state: ', c.state,'steps: ',num)
if (num < 18):
print(row)
break
num = 0
return c.Q,row,num2,count,num_ges
def plotPath(row,color,algo,endstate):
x=[]
y=[]
dx =[]
dy = []
for k in range(len(row)):
pos = np.where(c.states == row[k])
y.append(int(-pos[0]))
x.append(int(pos[1]))
if (k+1 < len(row)):
posnext = np.where(c.states == row[k+1])
dy=(int(-posnext[0])-y[k]) - (int(-posnext[0])-y[k])*0.6
dx=(int(posnext[1])-x[k]) - (int(posnext[1])-x[k])*0.6
plt.arrow(int(x[k]), int(y[k]), dx, dy,head_width=0.2, head_length=0.5, fc=color, ec=color)
plt.plot(x,y,'ro',label = algo)
plt.plot(0,-3,'cx',label = 'start')
plt.plot(7,-3,'kx',label = 'original goal')
plt.plot(endstate[1],-endstate[0],'gx',label = 'goal')
plt.grid()
plt.legend()
plt.plot(x,y,color)
#-------------------------------------------------------------------------------
c = windyGridworld(start,end)
c._render()
Q,row,num2,count,num_ges = rlwindy(c,episode, step, alpha, gamma, epsilon,0)
c=windyGridworld(start,end)
c._render()
Q_q,row_q,num2_q,count_q,num_ges_q = rlwindy(c,episode, step, alpha, gamma, epsilon,1)
#-------------------------------------------------------------------------------
# 1a)
plt.figure(1)
plt.plot(count,num2,label="SARSA")
# plt.plot(num_ges,count,label='SARSA')
plt.plot(count_q,num2_q,label="Q-learning")
# plt.plot(num_ges_q,count,label="Q-learning")
plt.xlabel('#episodes')
plt.ylabel('#steps')
plt.grid()
plt.legend()
# 1b)
#-------------------------------------------------------------------------------
plt.figure(2)
plt.subplot(211)
plotPath(row, 'r', 'SARSA', [3,7])
plt.subplot(212)
plotPath(row_q, 'b', 'Q-learning', [3,7])
plt.figure(3)
plt.subplot(211)
c.plotQ(Q)
plt.title('SARSA')
plt.grid()
plt.subplot(212)
c.plotQ(Q_q)
plt.title('Q-learning')
plt.grid()
plt.show()
# ------------------------------------------------------------------------------
# 1c
# c_std = windyGridworld(start,end)
# c_1 = windyGridworld(start,26)
# c_2 = windyGridworld(start,27)
# c_3 = windyGridworld(start,28)
# c_4 = windyGridworld(start,36)
# c_5 = windyGridworld(start,38)
# # c_6 = windyGridworld(start,46)
# c_7 = windyGridworld(start,47)
# c_8 = windyGridworld(start,48)
#
# plt.figure(3)
# Q_std,row_std,num2_std,count_std,num_ges_std = rlwindy(c_std,episode, step, alpha, gamma, epsilon,1)
# plt.plot(count_std,num2_std,label = "std")
# Q_1,row_1,num2_1,count_1,num_ges_1 = rlwindy(c_1,episode, step, alpha, gamma, epsilon,1)
# plt.plot(count_1,num2_1,label = "26")
# Q_2,row_2,num2_2,count_2,num_ges_2 = rlwindy(c_2,episode, step, alpha, gamma, epsilon,1)
# plt.plot(count_2,num2_2,label = "27")
# Q_3,row_3,num2_3,count_3,num_ges_3 = rlwindy(c_3,episode, step, alpha, gamma, epsilon,1)
# plt.plot(count_3,num2_3,label = "28")
# Q_4,row_4,num2_4,count_4,num_ges_4 = rlwindy(c_4,episode, step, alpha, gamma, epsilon,1)
# plt.plot(count_4,num2_4,label = "36")
# Q_5,row_5,num2_5,count_5,num_ges_5 = rlwindy(c_5,episode, step, alpha, gamma, epsilon,1)
# plt.plot(count_5,num2_5,label = "38")
# # Q_6,row_6,num2_6,count_6,num_ges_6 = rlwindy(c_6,episode, step, alpha, gamma, epsilon,1)
# # plt.plot(count_6,num2_6,label = "46")
# Q_7,row_7,num2_7,count_7,num_ges_7 = rlwindy(c_7,episode, step, alpha, gamma, epsilon,1)
# plt.plot(count_7,num2_7,label = "47")
# Q_8,row_8,num2_8,count_8,num_ges_8 = rlwindy(c_8,episode, step, alpha, gamma, epsilon,1)
# plt.plot(count_8,num2_8,label = "48")
#
# plt.legend()
#
# plt.figure(4)
# plotPath(row_8, 'r', 'Q-learning',[4 ,8])
# plt.show()