代做COMP532、代写Python/c++编程设计

日期：2024-04-18 10:09

COMP532-202324 Assignment 2

You need to solve each of the following problems. The assignment aims to design and

implement a deep reinforcement learning agent for a video game from OpenAI Gym or

Gymnasium. You must also include a brief report describing and discussing your solutions to the

problems. Students can do the assignment in groups or individuals.

● This assignment is worth 15% of the total mark for COMP532

● 80% of the assignment marks will be awarded for correctness of results

● 20% of the assignment marks will be awarded for the quality of the accompanying report

● Students will do the assignment in groups

● The assignment marks will be awarded for correctness of results

● We expect 5 students in one group (it would be fine to have groups of 1, 2, 3, and 4 as

well, but it is suggested to have groups of 5), please find your team members on your

own.

● Only one single submission is needed for each group

● The same marks will be granted to all the members in the same group

● Please list all your group members (names, emails, student ids) and individual

contributions in your submitted report

Submission Instructions

● Deadline: 22 Apr 2024 17:00 (UK Time)

● Send all solutions as a single PDF document containing your answers, results, and

discussion of the results. Attach the source code for the programming problems as

separate files.

● Submit your solution via Canvas.

● Penalties for late submission apply in accordance with departmental policy as set

out in the student handbook, which can be found at

https://intranet.csc.liv.ac.uk/student/msc-handbook.pdf and the University Code of

Practice on Assessment, found at

https://www.liverpool.ac.uk/media/livacuk/tqsd/code-of-practice-on-assessment/code_of_

practice_on_assessment.pdf

Problem 1 (80 marks)

Implement a deep reinforcement learning agent for a game or environment of OpenAI Gym or

Gymnasium.

Use the lunar_lander environment:

https://gymnasium.farama.org/environments/box2d/lunar_lander/.

Please plot the learning progress of your method from 0 to 1000 episodes. You can have a

figure to show rewards and another figure to show training loss.

Please use a video or gifs or figures to demonstrate how your agent works.

Prepare a report explaining your solution and containing your results, and discussion of the

results.

Attach the source code as separate files. For example, .ipnb - an ipython notebook file.

Problem 2 (20 marks)

Explain exploration and exploitation for deep reinforcement learning.