FIT5222 Planning and automated reasoning
Assignment 2
Pacman: Capture the Flag
Pacman: Capture the Flag
Pacman Capture the Flag is developed by UC Berkeley CS188. It is a multiplayer
capture-the-flag variant of Pacman. Two teams, blue and red, will compete with each other.
Each team needs to eat the food on the far side of the map while defending their own foods.
We will hold a Pacman Capture the Flag competition at the end of this semester.
Part 1: Installation
Follow the instructions in week 1 to get codes of Pacman : Capture the Flag.
Part 2: Rules of Pacman: Capture the Flag
Characteristics:
● An agent needs to trade off offense versus defense.
● Only limited information is provided to agents.
Layout
The Pacman map is now divided into two halves: blue (right) and red (left). Red agents
(which all have even indices) must defend the red food while trying to eat the blue
FIT5222 Planning and automated reasoning
food. When on the red side, a red agent is a ghost. When crossing into enemy
territory, the agent becomes a Pacman.
Scoring
As a Pacman eats food dots, those food dots are stored up inside of that Pacman and
removed from the board. When a Pacman returns to his side of the board, he
"deposits" the food dots he is carrying, earning one point per food pellet delivered.
Red team scores are positive, while Blue team scores are negative.
If Pacman is eaten by a ghost before reaching his own side of the board, he will
explode into a cloud of food dots that will be deposited back onto the board.
Eating Pacman
When a Pacman is eaten by an opposing ghost, the Pacman returns to its starting
position (as a ghost). No points are awarded for eating an opponent.
Power Capsules
If Pacman eats a power capsule, agents on the opposing team become "scared" for
the next 40 moves, or until they are eaten and respawn, whichever comes sooner.
Agents that are "scared" are susceptible while in the form of ghosts (i.e. while on their
own team's side) to being eaten by Pacman. Specifically, if Pacman collides with a
"scared" ghost, Pacman is unaffected and the ghost respawns at its starting position
(no longer in the "scared" state).
Observations
Agents can only observe an opponent's configuration (position and direction) if they or
their teammate is within 5 squares (Manhattan distance). In addition, an agent always
gets a noisy distance reading for each agent on the board, which can be used to
approximately locate unobserved opponents.
FIT5222 Planning and automated reasoning
Winning
A game ends when one team returns all but two of the opponents' dots. Games are
also limited to 1200 agent moves (300 moves per each of the four agents). If this
move limit is reached, whichever team has returned the most food wins. If the score is
zero (i.e., tied) this is recorded as a tie game.
Part 3: Getting Started and Useful Tools
Use “git pull” to pull the latest code from pacman-public repo.
You can find two examples in the Pacman Capture the Flag project. One is baselineTeam.py
and another is team_pddl.py. baselineTeam.py is an official example for Pacman Capture the
Flag. team_pddl.py is a simple example of how we use PDDL to guide the high-level actions
(Should the agent defend home, attack for food or escape from enemies) of Pacman. Then
the low level actions (what exact action to do to complete a high level action) is guided in a
Q learning model, which the decision is based on a set of weights and features.
Important codes you should know in these examples:
● You can use the ReflexCaptureAgent(CautureAgent) class as template for
designing your own Pacman.
● def chooseAction(self,gameState)
This is the function that the game will call when it’s the agent’s turn to take action. In
other words where things start for each turn.
● gameState
This variable is an instance of GameState class (Defined in capture.py file) which
contains the current state of the game and a set of useful functions for you to easily
retrieve information of the current game state.
● getAgentState(i) function of an instance of GameState. This function returns an
instance of the class AgentState which holds the current accessible state of an
agent. AgentState provide getPosition() and getDirection() function, which will
return the agent’s position (if available) and the agent’s ambiguous direction.
Important codes you should read:
● GameState class in capture.py
The instance of this class stores all the information about the current game state and
a set of functions to retrieve information. Read each function in this class as you will
use them when retrieving information from the gameState variable in your agents.
● AgentState in game.py.
This class contains the state of an agent, which include is Pacman, sacred timer,
food carrying.
FIT5222 Planning and automated reasoning
● CaptureAgent class in captureAgents.py
This class is the base class of the agents you will implement. Make sure you are
familiar with available methods in this class.
Part 4: Task
Your task will be to implement the Pacman agent/s to navigate the maze while eating food
from the enemy’s home turf in myTeam.py. These agents will be uploaded to the server and
run against each other. The agents who perform this task quickest, without the enemy eating
all of their food will win the tournament. Your implementation should:
● Use PDDL to guide the high level action of agents. You can refer to team_pddl.py as
a base implementation, and improve it by considering more states, introducing more
high level actions and developing more complicated PDDL models.
● For each high level action, you may improve the low level implementation by
introducing more features and weights to the model. Then build the Approximate
Q-learning update process to train the model and obtain a set of good weights, this
part does not need to be implemented in PDDL. You should carefully consider for
each high level action what is the purpose of this high level action and how you will
award or penalize the agent.
● To train your Q-learning model, you can run pacman in silence mode with “-Q”
argument and automatically run pacman for dozens of game with “-n 1000”
argument. You can replace 1000 with other number you want. You also need to use
“-l ./layouts/bloxCapture.lay” (replace the bloxCapture.lay to other maps) to train your
agent on other maps in “layouts” folder.
Please consider that there are several different strategies to implementing your agent
considering whether the agents are defensive, offensive or both depending on certain
conditions. You may wish to consider either a different strategy for each of the two agents or
one unified strategy.
Upon submission to the server your agent will be evaluated against a baseline
(team_pddl.py) agent implementation for 21 rounds on 3 different maps, if your agent is
successful for more than 11 rounds you will proceed to be evaluated for another 21 rounds
against the agent ranked next and so on until you are either defeated or ranked first. You are
expected to at least beat the baseline implementation, initially on the server, otherwise you
will not pass to the next stage of competing with other students’ agents.
Within each round there will be a time limit and the agent that has succeeded in eating the
most food during that time will win that round. The winning agent will be the agent that beats
all other agents for more than 11 rounds out of 21.
FIT5222 Planning and automated reasoning
Part 5: Agent Report
Along with your agent you will be required to submit a report to Moodle describing your
agents’ strategy and explaining your code. Please include a description of your model as
well as a justification for your strategy choice. We will not provide the number of lines
needed for this submission but please make sure that your report is as detailed and
complete as possible, including references to the code as well as detailed strategy
motivation.
For example, when describing your model, also refer to how your code implements that
model description.
We would like to remind all students that obtaining code online and using it constitutes a
breach of academic integrity and will be liable to heavy repercussions. The work must be
completely your own. The same goes for obtaining code from a friend or colleague. As such
please make sure to work independently and take reasonable steps to make sure other
students can't copy or misuse your work.
Please see: https://www.monash.edu/students/study-support/academic-integrity
Part 6: Marking Rubric
Criteria N
0%-49%
P
50%-59%
C
60%-69%
D
70%-79%
HD
80%-100%
Percent
of grade
1. Rank in the
competition
(the exact
score depends
on the
performance
within each
cohort)
Loses to the
baseline
implementation
Outranks
baseline
implement
ation
Ranked at 75%
~ 50% of all
submissions
Ranked at 50%
~ 75% of all
submissions
Ranked at top
25% of all
submissions.
33.3%
2. Agent
strategy
implementation
Minor updates to
example PDDL
implementation in
the code base (for
example, only
changing
parameter values)
One
planning
strategy;
same
strategy for
both
agents
One unique
strategy for
each planning
agent.
Multiple
strategies of
varying
complexity.
Unique RL
implementation.
Adaptable
agents that can
transition
between
planning
strategies.
33.3%
FIT5222 Planning and automated reasoning
3. Report -
Description of
your approach
Incomplete or
insufficient
description of the
approach and/or
pseudo-code.
High-level
description
of the
approach
and
pseudo-co
de.
Discussion and
algorithmic
analysis. E.g.,
time, space,
completeness,
optimality. Link
the agent
strategy to what
was learned in
the Lectures
and tutorials,
provide
motivation for
your choices
and if possible
prove lower and
upper bounds.
Reflections:
advantages and
disadvantages
of your
approach(es)
Numerical
experiments,
analysing the
efficiency of your
implementations
(e.g. on
standard
benchmarks and
vs. appropriate
reference
algorithms)
23.3%
4. Report -
Communication
skills
Hard to follow with
no clear narrative.
Inadequate or no
separation of
discussion text
into coherent
sections.
Writing is not
accurate or
articulate.
The writing
has a
tenuously
logical
narrative.
Some
attempt at
the
expected
structural
elements
(e.g.
Intro,
conclusion)
.
Writing is
not
accurate or
articulate
most of the
time.
The text has a
clear logical
narrative and
expected
structural
elements (e.g.
intro,
conclusion).
Writing is not
accurate or
articulate most
of the time.
There are some
supporting
The writing is
well composed
and has a clear
and logical
narrative and is
well structured.
Writing is
generally
accurate and
articulate.
The document
has appropriate
The writing is
very well
composed and
has a very clear
and logically
formed narrative
as a whole.
Writing is
accurate and
articulate.
The document is
expertly
structured in the
style of a
scientific report,
including
10%
FIT5222 Planning and automated reasoning
Part 7: Submission Guide
Submission to Moodle:
The following submission items are required:
1. Your implementation source codes, in a single directory called "src" (you can copy
everything in the piglet folder to "src"). Zip the codes directory with file name
last_name_student_id_pacman.zip. (For example, Chen_123456_pacman.zip)
2. The report describing your approaches as a single pdf file. Name the pdf as
last_name_student_id_report_pacman.pdf. (For example,
Chen_123456_report_pacman.pdf)
Submission to Contest Server:
You must make a successful submission on the server as part of your mark
depending on your rank. Submit as early as possible as you don’t know what will
happen if you submit on the last day.
Submission deadline : 11:55 pm 28 May 2021

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