Solution Reinforcement Learning for Taxi-v2

There are 4 locations (labeled by different letters), and our job is to pick up the passenger at one location and drop him off at another. We receive +20 points for a successful drop-off and lose 1 point for every time-step it takes. There is also a 10 point penalty for illegal pick-up and drop-off actions.

First import all related libraries:

import numpy as np
import gym
import random
import pandas as pd
import spacy
from spacy.tokens import Span
from spacy.matcher import PhraseMatcher
env = gym.make("Taxi-v3")


Fetching Origing, Destination, and Time of Pickup from the sms data:

def fetch_pickup_drop(text_file_path):

    # Append All the texts in the List

    #read the text sms file

    for i in range(0,df.shape[0]):
    # add the locations through add_pipe
    LOCATIONS = ["dwarka sector 23", "dwarka sector 21", "hauz khaas", "airport"]

    nlp = spacy.load('en')
    matcher = PhraseMatcher(nlp.vocab)
    matcher.add("LOCATIONS", None, *list(nlp.pipe(LOCATIONS)))

    def places_component(doc):
        doc.ents = [Span(doc, start, end, label="GPE") for match_id, start, end in matcher(doc)]
        return doc

    nlp.add_pipe(places_component) #last=True

    #fetch the locations from the texts list of each text and append in the l1 list
    for doc in nlp.pipe(texts_list):
        l1.append([(ent.text, ent.label_) for ent in doc.ents])

    for i in range(0,len(texts_list)):
        str1='for '+l1[i][1][0]
        str2='to '+l1[i][1][0]
        str3='from '+l1[i][1][0]
        ## fetch the pickup and drop up location from the texts list of each text sms and append in the destination and pickup list
        if str1 in str_text or str2 in str_text:
        elif str3 in str_text:
        # fetch the timing from the texts list of each text and append in the timing list.
        if 'am' in str_text:
            n=new_str.rindex(' ')
            timing.append(new_str[n+1:]+' AM')

        elif 'pm' in str_text:
            n=new_str.rindex(' ')
            timing.append(new_str[n+1:]+' PM')

## create the dataframe of the pickup, Destination and time of pickup
    df1 = pd.DataFrame(pickup,columns=['origing'])
    df2 = pd.DataFrame(dest,columns=['destination'])
    df3 = pd.DataFrame(timing,columns=['time of pickup'])
    # concatenate the above three dataframe to get df_final dataframe of sms text file.
    df_table_final=pd.concat([df1,df2,df3], axis=1)
    return df_table_final

env.reset() # reset environment to a new, random state

action_size = env.action_space.n
print("Action size ", action_size)

state_size = env.observation_space.n
print("State size ", state_size)

q_table = np.zeros((state_size, action_size))


Training the Agent:

"""Training the agent"""

import random
from IPython.display import clear_output

# Hyperparameters
alpha = 0.1
gamma = 0.6
epsilon = 0.1

# For plotting metrics
all_epochs = []
all_penalties = []

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

    epochs, penalties, reward, = 0, 0, 0
    done = False
    while not done:
        if random.uniform(0, 1) < epsilon:
            action = env.action_space.sample() # Explore action space
            action = np.argmax(q_table[state]) # Exploit learned values

        next_state, reward, done, info = env.step(action) 
        old_value = q_table[state, action]
        next_max = np.max(q_table[next_state])
        new_value = (1 - alpha) * old_value + alpha * (reward + gamma * next_max)
        q_table[state, action] = new_value

        if reward == -10:
            penalties += 1

        state = next_state
        epochs += 1
    if i % 100 == 0:
        print(f"Episode: {i}")

print("Training finished.\n")


Episode: 100000 Training finished. CPU times: user 1min 10s, sys: 13.3 s, total: 1min 24s Wall time: 1min 12s

Distance between pic up and drop location

text_file_path='drive/My Drive/project_2_dataset/sms.txt'


# Create a Local Dictionary of city
city=pd.read_csv('drive/My Drive/project_2_dataset/city.csv')
city['mapping']=city['mapping'].map({0:0., 1:1., 2:2., 3:3.})


# Change the location by numeric value of city in df_original dataframe


origing destination time of pickup 0 airport hauz khaas 3 PM 1 airport hauz khaas 6 PM 2 hauz khaas dwarka sector 23 1 PM 3 airport hauz khaas 1 AM 4 airport dwarka sector 21 10 PM .. ... ... ... 995 airport dwarka sector 23 2 AM 996 dwarka sector 21 dwarka sector 23 2 PM 997 hauz khaas dwarka sector 21 5 AM 998 airport dwarka sector 23 6 PM 999 airport hauz khaas 1 AM [1000 rows x 3 columns]

Check Pick up and Drop up correction:

#### Check Pick up and Drop up correction 

def check_pick_up_drop_correction(pick_up, drop, line_num):
    original_origin = int(df_original.iloc[line_num,0])
    original_destination = int(df_original.iloc[line_num,1])
    if original_origin == pick_up and original_destination == drop:
        return True
        return False

Evaluate the agent's performance after Q-learning

"""Evaluate the agent's performance after Q-learning"""
total_epochs, total_penalties, wrong_predictions, total_reward = 0, 0, 0, 0
episodes = 1000

for i in range(episodes):
    epochs, penalties, reward = 0, 0, 0

    #Generate the random state from an enviroment and change the pick up and drop as the fetched one
    state = env.reset()
    done = False
    while not done:
        action = np.argmax(q_table[state,:])
        state, reward, done, info = env.step(action)

        epochs += 1
    checking = check_pick_up_drop_correction(int(q_table[state][4]), int(q_table[state][5]), i)
    if checking == False:
        wrong_predictions += 1
        penalties += 1

    total_penalties += penalties
    total_epochs += epochs
    total_reward += reward

print(f"Results after {episodes} episodes:")
print(f"Average timesteps per episode: {total_epochs / episodes}")
print(f"Average penalties per episode: {total_penalties / episodes}")
print(f"Total number of wrong predictions", wrong_predictions)
print("Total Reward is", total_reward)


Results after 1000 episodes: Average timesteps per episode: 196.365 Average penalties per episode: 0.019 Total number of wrong predictions 19 Total Reward is 19430

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