Notes

import numpy as np
import gym

#Building the environment
env = gym.make('FrozenLake-v1')

#Defining the different parameters
epsilon = 0.9
total_episodes = 10000
max_steps = 100
alpha = 0.85
gamma = 0.95

#Initializing the Q-matrix
Q = np.zeros((env.observation_space.n, env.action_space.n))

#Function to choose the next action
def choose_action(state):
action=0
if np.random.uniform(0, 1) < epsilon:
action = env.action_space.sample()
else:
action = np.argmax(Q[state, :])
return action

#Function to learn the Q-value
def update(state, state2, reward, action, action2):
predict = Q[state, action]
target = reward + gamma * Q[state2, action2]
Q[state, action] = Q[state, action] + alpha * (target - predict)

#Initializing the reward
reward=0

# Starting the SARSA learning
for episode in range(total_episodes):
t = 0
state1 = env.reset()
action1 = choose_action(state1)

while t < max_steps:
#Visualizing the training
#env.render()

#Getting the next state
state2, reward, done, info = env.step(action1)

#Choosing the next action
action2 = choose_action(state2)

#Learning the Q-value
update(state1, state2, reward, action1, action2)

state1 = state2
action1 = action2

#Updating the respective vaLues
t += 1
reward += 1

#If at the end of learning process
if done:
break


#Evaluating the performance
print ("Performance : ", reward/total_episodes)

#Visualizing the Q-matrix
print(Q)