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COMP9414 Artificial Intelligence

创建日期：2024-08-07 15:38:57

所属分类：计算机computer science

标签： COMP9414

Artificial Intelligence

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COMP9414

Artificial Intelligence

Assignment 2 - Reinforcement Learning

1 Problem context

Taxi Navigation with Reinforcement Learning: In this assignment,

you are asked to implement Q-learning and SARSA methods for a taxi nav-

igation problem. To run your experiments and test your code, you should

make use of the Gym library1, an open-source Python library for developing

and comparing reinforcement learning algorithms. You can install Gym on

your computer simply by using the following command in your command

prompt:

pip i n s t a l l gym

In the taxi navigation problem, there are four designated locations in the

grid world indicated by R(ed), G(reen), Y(ellow), and B(lue). When the

episode starts, one taxi starts off at a random square and the passenger is

at a random location (one of the four specified locations). The taxi drives

to the passenger’s location, picks up the passenger, drives to the passenger’s

destination (another one of the four specified locations), and then drops off

the passenger. Once the passenger is dropped off, the episode ends. To show

the taxi grid world environment, you can use the following code:

env = gym .make(”Taxi?v3 ” , render mode=”ans i ” ) . env

s t a t e = env . r e s e t ( )

rendered env = env . render ( )

p r i n t ( rendered env )

In order to render the environment, there are three modes known as

“human”, “rgb array, and “ansi”. The “human” mode visualizes the envi-

ronment in a way suitable for human viewing, and the output is a graphical

window that displays the current state of the environment (see Fig. 1). The

“rgb array” mode provides the environment’s state as an RGB image, and

the output is a numpy array representing the RGB image of the environment.

The “ansi” mode provides a text-based representation of the environment’s

state, and the output is a string that represents the current state of the

environment using ASCII characters (see Fig. 2).

Figure 1: “human” mode presentation for the taxi navigation problem in

Gym library.

You are free to choose the presentation mode between “human” and

“ansi”, but for simplicity, we recommend “ansi” mode. Based on the given

description, there are six discrete deterministic actions that are presented in

Table 1.

For this assignment, you need to implement the Q-learning and SARSA

algorithms for the taxi navigation environment. The main objective for this

assignment is for the agent (taxi) to learn how to navigate the gird-world

and drive the passenger with the minimum possible steps. To accomplish

the learning task, you should empirically determine hyperparameters, e.g.,

the learning rate α, exploration parameters (such as ? or T ), and discount

factor γ for your algorithm. Your agent should be penalized -1 per step it

Figure 2: “ansi” mode presentation for the taxi navigation problem in Gym

library. Gold represents the taxi location, blue is the pickup location, and

purple is the drop-off location.

Table 1: Six possible actions in the taxi navigation environment.

Action Number of the action

Move South 0

Move North 1

Move East 2

Move West 3

Pickup Passenger 4

Drop off Passenger 5

takes, receive a +20 reward for delivering the passenger, and incur a -10

penalty for executing “pickup” and “drop-off” actions illegally. You should

try different exploration parameters to find the best value for exploration

and exploitation balance.

As an outcome, you should plot the accumulated reward per episode and

the number of steps taken by the agent in each episode for at least 1000

learning episodes for both the Q-learning and SARSA algorithms. Examples

of these two plots are shown in Figures 3–6. Please note that the provided

plots are just examples and, therefore, your plots will not be exactly like the

provided ones, as the learning parameters will differ for your algorithm.

After training your algorithm, you should save your Q-values. Based on

your saved Q-table, your algorithms will be tested on at least 100 random

grid-world scenarios with the same characteristics as the taxi environment for

both the Q-learning and SARSA algorithms using the greedy action selection

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COMP532 MAS275 COMP5318 COMP4002 FIT5086 MAST20004 FIT3152 FIT5129 CIS2380 CS521 COMP 1005 EECMS CITS5501 ECE 462 ECON 6002 ENG 4051 CSC 648 CMT654 MECHENG 713 PHYS1002 AMME 2000 FP0060 DDES3200 MECHENG 743 BINF90002 FIT2081 ISYS90081 ELECTENG 722 BUS 360 ECOS3010 BUS2810 ETF2100 ENG2048 STAT5002 COMP2017 CEME 2003 MATS3004 COMP9319 PHYS1001 COMP5349 AMME2261 COMP-381 FIT3173 COSC2757 ENG4025 QBUS2820 COMP9414 MATH2021 EOSC118 MFIN6690 PMATH 321 CSE-141L MATH575A COMPSCI5002 COMP1511 management comp2123 FINS3616 COMP 3331 COMS30034 DTSC 71 AcF 702 AcF702 DTSC71 MAT021 ELE8083 ACFI213 EG-127J 127J FIT5163 FIT2001 ELE00113M CMT310 CS 245 ENGL 1101 MTH6115 EE497 CS 325 Math 124A MTH5130 MBA502 MGT 5380 ARCH 1261 F033800 20LLP109 ELE4009 FINANCIAL COMP3331 Computer ECE4179 20T3 COMP9311 COMP9311 UNSW MFIN6210 ACCT 5930 EE590 ENG2029 ECO100 SIT315 ACCT3610 COMP3600 STK2100 FIT2004 COMP30020 ACTL1101 Stat 101B BANA 200 COSC2473 ENGG1400 AI COMP90043 MMF 1914H PHYS 127 CS584 CIVL2010 ECMT2150 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