-
Notifications
You must be signed in to change notification settings - Fork 0
/
frozenlake.py
113 lines (83 loc) · 2.87 KB
/
frozenlake.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
# -*- coding: utf-8 -*-
"""FrozenLake
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1iVfcf3YCwnCTKf_ixfx7skgDoDel0Fp4
"""
!pip install gym
import numpy as np
import gym
import random
import time
env = gym.make('FrozenLake-v0')
action_space_size = env.action_space.n
state_space_size = env.observation_space.n
q_table = np.zeros((state_space_size,action_space_size))
print(q_table)
num_episodes = 15000
max_steps_per_episode = 100
learning_rate = 0.1
discount_rate = 0.99
exploration_rate = 1
max_exploration_rate = 1
min_exploration_rate = 0.01
exploration_decay_rate = 0.01
rewards_all_episodes = []
for episode in range(num_episodes):
state = env.reset()
done = False
rewards_current_episode = 0
for step in range(max_steps_per_episode):
# Exploration-exploitation trade-off
exploration_rate_threshold = random.uniform(0, 1)
if exploration_rate_threshold > exploration_rate:
action = np.argmax(q_table[state,:])
else:
action = env.action_space.sample()
new_state, reward, done, info = env.step(action)
# Updating the Q-table for Q(s,a)
q_table[state, action] = q_table[state, action] * (1 - learning_rate) + \
learning_rate * (reward + discount_rate * np.max(q_table[new_state, :]))
state = new_state
rewards_current_episode += reward
if done == True:
break
# Exploration rate decay
exploration_rate = min_exploration_rate + (max_exploration_rate - min_exploration_rate) * np.exp(-exploration_decay_rate*episode)
rewards_all_episodes.append(rewards_current_episode)
# Calculate and print the average reward per thousand episodes
rewards_per_thousand_episodes = np.split(np.array(rewards_all_episodes),num_episodes/1000)
count = 1000
print("********Average reward per thousand episodes********\n")
for r in rewards_per_thousand_episodes:
print(count, ": ", str(sum(r/1000)))
count += 1000
#print updated Q-Table
print('\n\n ******Q-Table*******\n')
print(q_table)
from IPython.display import clear_output
for episode in range(3):
state = env.reset()
done = False
print('*********Episode ', episode+1,'*********\n\n')
time.sleep(1)
for step in range(max_steps_per_episode):
clear_output(wait=True)
env.render()
time.sleep(0.3)
action = np.argmax(q_table[state,:])
new_state, reward, done, info = env.step(action)
if done:
clear_output(wait=True)
env.render()
if reward == 1:
print('****** You Reached your Goal ******')
time.sleep(3)
else:
print('****** You fell through hole ******')
time.sleep(3)
clear_output(wait=True)
break
state = new_state
env.close()
: