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SIT215 – Artificial and Computational
Intelligence
Project: Investigating
Reinforcement Learning
Overview
Within SIT215 you have been learning about a range of problems that can be solved using techniques from
artificial and computational intelligence. This study has included coverage of both models and algorithms
suitable for AI and CI solutions. A particular limitation of all of the solutions that we have considered is that
they are designed by hand, or rely on the problem being formulated as an optimisation task.
In this project you are going to explore an advanced technique for solving many interesting and challenging
real world problems. One in which an agent learns a solution to a problem through interaction with the
environment, and through perception of a reinforcement, or feedback signal. This field is called, naturally,
reinforcement learning (RL). RL can also be seen as an online method for solving Markov Decision Problems
– as opposed to the offline methods of policy iteration, value iteration or dynamic programming, presented in
lectures (in week 9 & 10).
This project will require you to undertake self-directed study and learning of RL solution methods, building
upon topics and content covered in the first 10 weeks of this course. While this might seem daunting (not
being told how to solve the problem), you’ve been practicing this approach throughout the unit in the groupbased
PBL tasks, and so this is your chance to demonstrate individually what you’ve learned about problem
solving methodology.
Learning Objectives
This project addresses ULO2 and ULO3 for this unit:
• Design and implement software artefacts to demonstrate effectiveness and efficiency of solutions for
intelligent systems development
• Apply theoretical concepts and models to explain and communicate the design of intelligent systems
Specifically, these are addressed through achievement of the following task-specific learning objectives:
• Demonstrate ability to work with and extend software systems and frameworks for RL
• Describe and model RL problems using specific concepts and models
• Implement, evaluate and analyse the performance of different solutions on a range of RL problems
• Effectively communicate the process and outcomes of your research and development project
Preparatory Learning Activities
In order to complete this assessment task you will need to have first developed an understanding of a range
of topics covered in this unit in weeks 1 to 10. Given the assessment deadline, this may require you to
complete independent study of these topics prior to their presentation in lectures. The topics that you will
need to be familiar with are:
• Bayesian AI (working with probabilistic representations of uncertainty)
• State Space Search (understanding state space representations of systems)
• Normative Decision Theory (definitions of rational action, utility, intertemporal utility,
payoff/reward)
• Markov Decision Problems (representing sequential decision problems for agents acting in complex
domains, reward processes and finite horizon decision problems, optimal policies)
• Dynamic Programming (optimal solutions to sequential decision problems under specified
constraints)
Ultimately you will be able to complete this assessment task without a sound theoretical grounding in each
of these areas. However, having some knowledge of these areas and understanding of how they inter-relate
will make it far easier to understand learning materials on reinforcement learning, and far easier to explain
and describe your investigations and outcomes in this project. Our advice is that you use this project as a
basis for further study of these underlying areas, to assist in integrating the knowledge covered in this unit
into a meaningful ‘whole’, which supports completing this assessment task.
Task Requirements
This project will require you to use the OpenAI Gym environment for experimenting with reinforcement
learning tasks. You should start by reviewing the website for the Gym: https://gym.openai.com/. There are
links to documentation and software downloads.
To complete this project, you need to complete the following requirements and sub-tasks.
1. Read the relevant documentation for installing AI Gym, starting with https://gym.openai.com/docs/.
2. Read and complete the following tutorial: https://www.learndatasci.com/tutorials/reinforcement-qlearning-scratch-python-openai-gym/,
ensuring that you can reproduce all steps discussed.
3. Write a brief report (2-3 pages at most) on the Taxi problem, including a mathematical description of
the reinforcement learning problem and the Q-learning algorithm for its solution. To do this, you may
want to refer to a good textbook on reinforcement learning. A good starting point is the “bible” of RL:
“Reinforcement Learning: An Introduction”, by Sutton & Barto. You can find this book online as a
free PDF download. There’s even a 2
nd edition draft completed just this year. In your report you
should contrast the quality of solution of a random policy versus the “optimal” policy obtained by Qlearning.
4. Complete the following tutorial to explore the Cart-Pole environment in the Gym:
http://kvfrans.com/simple-algoritms-for-solving-cartpole/. In this case, implement a random policy
and Q-learning. It’s not essential that you attempt the policy gradient method, but you might like to
try it.
5. Extend your report to cover briefly the Cart-Pole problem, highlighting any differences with the Taxi
problem. Compare performance of Q-learning on both of these problems, presenting evidence (such
as graphs) to support your evaluation.
If you’ve gotten to this point and created a good report that details what you’ve learned, you’ve met
the minimum requirements for this assessment task. Assuming a reasonable quality of report and
evidence, you can expect to earn a credit grade. Continue on to achieve a higher grade.
6. [Distinction] Select another environment from the OpenAI Gym, and implement Q-learning for this
environment. Extend your report to describe this new environment, including a mathematical model.
Evaluate performance of Q-learning on this model, and identify any significant outcomes or
limitations of this approach on this new problem, compared to previous problems. Attempt to explain
any difference or limitations.
7. [High Distinction] Implement Temporal Difference learning on the new environment you completed
for step 6, as well as one of the Taxi problem, or the Cart-Pole problem. Contrast the performance of
TD learning and Q-learning in your report, providing evidence such as graphs and performance data.
Submission Components & Due Dates
This is an individual assessment task and as such, each student will complete their own project and
submission components. To be eligible for assessment in this task you must submit the following artefacts to
the relevant submission folder on the Unit Site no later than the given deadline:
1) The report detailing your models, experiments and outcomes of your reinforcement learning problems
and solutions. Your report should provide adequate information to evidence your learning against the
objectives stated above, and in line with the assessment rubric provided.
2) All code developed or used in this project. Your code must include appropriate documentation
(internal comments are sufficient) that explains what the code does. You should also provide
instructions on how to execute the code (for example, in a README file).
You may assume that the assessment team has access to OpenAI Gym and can execute your code. If you
rely on any third party libraries or applications that are required to run your solution, you need to provide
those, or make them accessible to the assessor (e.g., by providing a link to a dowload site, and instructions
on how to install and use the library in your solution).
Assignment Marking
This assignment will be marked on the following scale
Level Grade
Does Not Meet Minimum Standards N
Meets Minimum Standards P or C
Exceeds Minimum Standards D
Greatly Exceeds Minimum Standards HD
A numeric mark will be assigned based on the assessor’s determination as to where within the relevant grade
category the standard or work sits. A rubric will be provided on the Unit Site, under the
Resources>Assessment folder, to indicate the criteria upon which your submission components will be
assessed and the standards that will be applied for these criteria. Please contact the teaching team if you have
any concerns or questions regarding how you will be assessed.
Penalties
In accordance with Faculty assessment policies, late submissions to the submission folder will incur a penalty
of 5% of the total available marks per day, up to five days total, after which the score for this part of the task
is 0. Such penalties will be deducted from the awarded numeric mark to determine the final grade for this task.
Submissions will not be accepted or marked more than five days after the final submission deadline, except in
cases where an extension has been approved prior to the deadline.
Getting Help and Support
Students are encouraged to support each other to discuss the tasks, as well as to assist in overcoming problems
in understanding the concepts, models and algorithms relevant to RL problems and solutions. Getting feedback
from peers will certainly improve your understanding in this project, and help others to build their
understanding. Note however that as this is an individual assessment task, and all development work and report
writing must principally be the work of the student being assessed. Where you are asked to replicate the work
of others (e.g., completing the tutorials and reproducing the code and results of others), ensure that you
accurately and appropriately reference the source work. Academic penalties for collusion and plagiarism are
severe and students are urged to seek guidance and advice from the teaching team if they have any concerns
about how to complete this task appropriately.
Programming & Software Help
The School of IT runs a Learning Support Hub (LSH), which is accessible Monday to Friday, both on campus
and in the cloud. The LSH staff can assist with issues surrounding programming, and installation of software
(such as setting up Python), but they are NOT going to help you to complete the assignment. They cannot tell
you how to do this project, nor tell you if you’re doing it correctly. They are not teaching staff. You should
contact the LSH in the first instance to deal with any programming or software related issues.
Mathematics & Algorithm Help
The teaching team are here to support you in this task, in particular to assist you to develop an understanding
of reinforcement learning models and solution algorithms. We are also here to help you learn the underlying
knowledge, as described in the Preparatory Learning Activities section. If you are having trouble
understanding this material, or this task, please make contact with us. The best way to do this is by asking
questions in the Project Discussion forum on the Unit Site. Answers to your question will also help other
students, who are undoubtedly having the same kinds of problems as you.
Beyond this you may seek assistance from the teaching team during practical classes, or Bb Collaborate
sessions. Additional Bb Collaborate sessions dedicated to this project will be run in weeks 5 and 8. Details of
these will be provided on the Unit Site.
Report Writing Help
While the teaching team are also happy to provide advice and guidance on writing your report, the University
also provides support services for students. In particular, the Writing Mentors team offer great assistance for
students completing written assessment tasks – especially report writing. Visit
http://www.deakin.edu.au/students/studying/study-support/writing-mentors for more information.
Feedback
Students will receive verbal, written or recorded audio feedback on their project submission as part of their
assessment. Due to the timing of assessment and scheduling of exams by DSA, it cannot be guaranteed that
this feedback will be provided before the unit exam. Where a student requires specific feedback prior to the
exam, they should contact the Unit Chair, allowing sufficient time prior to the exam for this feedback to be
provided.
Students are actively encouraged to seek formative feedback from peers and teaching staff, on their work
completed before the submission deadline, to ensure they are on track with this task. Feedback may be
obtained during weekly scheduled practical classes upon request. Talk to us and we’ll support you!

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