OMP9414辅导、辅导Java，Python程序

日期：2023-06-30 08:29

OMP9414 23T2
Artificial Intelligence
Assignment 1 - Reward-based learning agents
Due: Week 5, Friday, 30 June 2023, 11:55 PM
1 Activities
In this assignment, you are asked to implement a modified version of the
temporal-difference method Q-learning and SARSA. Additionally, you are
asked to implement a modified version of the action selection methods soft-
max and ?-greedy.
To run your experiments and test your code you should make use of the
example gridworld used for Tutorial 3 (see Fig. 1). The modification of the
method includes the following two aspects:
Random numbers will be obtained sequentially from a file.
The initial Q-values will be obtained from a file as well.
The random numbers are available in the file random numbers.txt.
The file contains 100k random numbers between 0 and 1 with seed = 9999
created with numpy.random.random as follows:
import numpy as np
np.random.seed(9999)
random_numbers=np.random.random(100000)
np.savetxt("random_numbers.txt", random_numbers)
1
04
8 9 10 11
5 6 7
1 2 3
Figure 1: 3× 4 gridworld with one goal state and one fear state..
1.1 Implementing modified SARSA and ?-greedy
For the modified SARSA you must use the code review during Tutorial 3 as
a base. Consider the following:
The method will use a given set of initial Q-values, i.e., instead of
initialising them using random values the initial Q-values should be
obtained from the file initial Q values.txt. You must load the
values using np.loadtxt(initial Q values.txt).
The initial state for the agent before the training will be always 0.
For the modified ?-greedy, create an action selection method that receives
the state as an argument and returns the action. Consider the following:
The method must use sequentially one random number from the pro-
vided file each time, i.e., a random number is used only once.
In case of a random number rnd <= ? the method returns an ex-
ploratory action. We will use the next random number to decide what
action to return, as shown in Table 1.
You should keep a counter for the random numbers, as you will need it
to access the numbers sequentially, i.e., you should increase the counter
every time after using a random number.
2
Random number (r) Action Action code
r <= 0.25 down 0
0.25 < r <= 0.5 up 1
0.5 < r <= 0.75 right 2
0.75 < r <= 1 left 3
Table 1: Exploratory action selection given the random number.
1.2 Implementing Q-learning and softmax
You should implement the temporal-difference method Q-learning. Consider
the following for the implementation:
For Q-learning the same set of initial Q-values will be used (provided
in the file initial Q values.txt).
Update the Q-values according to the method. Remember this is an
off-policy method.
As in the previous case, the initial state before training is also 0.
For the softmax action selection method, consider the following:
Use a temperature parameter τ = 0.1.
Use a random number from the provided file to compare it with the cu-
mulative probabilities to select an action. Hint: np.searchsorted
returns the position where a number should be inserted in a sorted array
to keep it sorted, this is equivalent to the action selected by softmax.
Remember to use and increase a counter every time you use a random
number.
1.3 Testing and plotting the results
You should plot a heatmap with the final Q-values after 1,000 learning
episodes. Additionally, you should plot the accumulated reward per episode
and the number of steps taken by the agent in each episode.
For instance, if you want to test your code, you can use the gridworld
shown in Fig. 1 and you will obtain the rewards shown in Fig. 2 and the
(d) SARSA + softmax.
Figure 3: Steps per episode.
2 Submitting your assignment
You can do the assignment either individually or working in a couple with
a classmate. If you decide to work in a couple with a classmate in another
tutorial section, you need the approval of one of these two tutors, who will
conduct the discussion with you and your classmate. However, the other
tutor still needs to be informed by the student.
Your submission should be done by Moodle and consist of only one .py
file. If you work in a couple, only one person is required to submit the file.
However, the file should indicate on top as a comment the full name and
zID of the students. It is your responsibility to indicate the names, we will
not add people to a work after the deadline if you forget to include the names.
5
You can submit as many times as you like before the deadline – later
submissions overwrite earlier ones. After submitting your file a good practice
is to take a screenshot of it for future reference.
Late submission penalty: UNSW has a standard late submission
penalty of 5% per day from your mark, capped at five days from the as-
sessment deadline, after that students cannot submit the assignment.
3 Deadline and questions
Deadline: Week 5, Friday 30th of June 2023, 11:55pm. Please use the forum
on Moodle to ask questions related to the project. However, you should not
share your code to avoid making it public and possible plagiarism.
Although we try to answer questions as quickly as possible, we might take
up to 1 or 2 business days to reply, therefore, last-moment questions might
not be answered timely.
4 Plagiarism policy
Your program must be entirely your own work. Plagiarism detection software
might be used to compare submissions pairwise (including submissions for
any similar projects from previous years) and serious penalties will be applied,
particularly in the case of repeat offences.
Do not copy from others. Do not allow anyone to see your code.
Please refer to the UNSW Policy on Academic Honesty and Plagiarism if you
require further clarification on this matter.