ReinforcementLearningCaseStudy.py

import gymnasium as gym
import numpy as np
import random 
from tqdm import tqdm

env = gym.make("Taxi-v3",render_mode="ansi")
env.reset()

print(env.render())

"""
0:kuzey 
1:güney
2:dogu
3:bati
4:yolcuyu almak
5:yolcuyu birakmak
"""

action_space =env.action_space.n
state_space =env.observation_space.n

q_table=np.zeros((state_space,action_space))

alpha =0.1
gamma =0.6
epsilon=0.1

for i in tqdm(range(1,100001)):
    state,_=env.reset()

    done =False

    while not done:
        #explore
        if random.uniform(0,1)<epsilon:
            action=env.action_space.sample()
        #exploit     
        else:
            action=np.argmax(q_table[state])

        next_state ,reward , done ,info,_=env.step(action)

        q_table[state,action] = q_table[state,action]+alpha * (reward+gamma*np.max(q_table[state,action])-q_table[state,action])
        state=next_state
print("training done")

#test 
total_epoch , total_penalties =0,0
episodes=100

for i in tqdm(range(episodes)):
    state,_=env.reset()
    epochs,penalties,reward=0,0,0
    done =False

    while not done:

        action=np.argmax(q_table[state])

        next_state ,reward , done ,info,_=env.step(action)

        state=next_state

        if reward ==10:
            penalties+=1
        epochs+=1
    total_epoch+=epochs
    total_penalties+=penalties

print("result after {} episodes" .format(episodes))
print("average timesteps per episodes: " , total_epoch/episodes)
print("average penalties per episodes: " , total_penalties/episodes)