# Contest: Pacman Capture the Flag
——————————–
> 
> Enough of defense,\
> Onto enemy terrain.\
> Capture all their food!
## Introduction
The course contest involves a multi-player capture-the-flag variant of Pacman, where agents control both Pacman and ghosts in coordinated team-based strategies. Your team will try to eat the food on the far side of the map, while defending the food on your home side. The contest code is available in [this folder](pacman-contest).
### Key files to read:
`capture.py`
The main file that runs games locally. This file also describes the new capture the flag GameState type and rules.
`captureAgents.py`
Specification and helper methods for capture agents.
`baselineTeam.py`
Example code that defines two very basic reflex agents, to help you get started.
`myTeam.py`
This is where you define your own agents for inclusion in the nightly tournament. (This is the only file that you submit.)
### Supporting files (do not modify):
`game.py`
The logic behind how the Pacman world works. This file describes several supporting types like AgentState, Agent, Direction, and Grid.
`util.py`
Useful data structures for implementing search algorithms.
`distanceCalculator.py`
Computes shortest paths between all maze positions.
`graphicsDisplay.py`
Graphics for Pacman
`graphicsUtils.py`
Support for Pacman graphics
`textDisplay.py`
ASCII graphics for Pacman
`keyboardAgents.py`
Keyboard interfaces to control Pacman
`layout.py`
Code for reading layout files and storing their contents
### Academic Dishonesty:
While we won’t grade contests, we still expect you not to falsely represent your work. *Please* don’t let us down.
## Rules of Pacman Capture the Flag
### 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 food. When on the red side, a red
agent is a ghost. When crossing into enemy territory, the agent becomes a Pacman.
### Scoring:
When a Pacman eats a food dot, the food is permanently removed and is scored when that Pacman comes back *home*. Red team scores are positive, while Blue team scores are negative.
### 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.
### Winning:
A game ends when one team eats 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 eaten the most food wins. If the score is zero (i.e., tied) this is recorded as a tie game.
### Computation Time:
We will run your submissions on an Amazon EC2 like instance supported by Nectar Cloud, which has a 1.7 Ghz Xeon / Opteron processor equivalent and 1.7gb of RAM. Each agent has 1 second to return each action. Each move which does not return within one second will incur a warning. After three warnings, or any single move taking more than 3 seconds, the game is forfeit. There will be an initial start-up allowance of 15 seconds (use the `registerInitialState` function). If you agent times out or otherwise throws an exception, an error message will be present in the log files, which you can download from the results page (see below).
## Submission Instructions
To enter into the nightly tournaments, your team must be defined in `myTeam.py`. Due to the way the tournaments are run, your code must not rely on any additional files that we have not provided (The submission system may allow you to submit additional files, but the contest framework will not include them when your code is run in the tournament). You may not modify the code we provide.
You must also specify a unique team name in `name.txt`. This file should consist only of ASCII letters and digits (any other characters, including whitespace, will be ignored). Only your team name will be displayed to the rest of the class. Once you submit under a particular name, only you will be permitted to submit under that name, until you give up the name by either submitting under a different team name or
submitting with a blank team name, which serves to remove you from the tournament. As usual, you should also include a `partners.txt` file.
To actually submit, use the `submit` under the assignment name `contest`. The [contest submissions](http://inst.eecs.berkeley.edu/~cs188/fa10/contest-submissions.html) webpage shows the team names and most recent submission times of all teams, which you can use to verify that your submission has been received properly. This website should update within two minutes of your submission.
## Getting Started
By default, you can run a game with the simple `baselineTeam` that the staff has provided:
“`python
python capture.py
“`
**Make sure you are using python3**
A wealth of options are available to you:
“`python
python capture.py –help
“`
There are four slots for agents, where agents 0 and 2 are always on the red team, and 1 and 3 are on the blue team. Agents are created by agent factories (one for Red, one for Blue). See the section on designing
agents for a description of the agents invoked above. The only team that we provide is the `baselineTeam`. It is chosen by default as both the red and blue team, but as an example of how to choose teams:
“`python
python capture.py -r baselineTeam -b baselineTeam
“`
which specifies that the red team `-r` and the blue team `-b` are both created from `baselineTeam.py`. To control one of the four agents with the keyboard, pass the appropriate option:
“`python
python capture.py –keys0
“`
The arrow keys control your character, which will change from ghost to Pacman when crossing the center line.
### Layouts
By default, all games are run on the `defaultcapture` layout. To test your agent on other layouts, use the `-l` option. In particular, you can generate random layouts by specifying `RANDOM[seed]`. For example,
`-l RANDOM13` will use a map randomly generated with seed 13.
## Game Types
You can play the game in two ways: local games, and nightly tournaments.
Local games (described above) allow you to test your agents against the baseline teams we provide and are intended for use in development.
## Official Tournaments
The actual competitions will be run using nightly automated tournaments on an Amazon EC2 like cluster (1.7 Ghz Xeon / 1.7GB RAM machines), with the final tournament deciding the final contest outcome. See the submission instructions for details of how to enter a team into the tournaments. Tournaments are run everyday at approximately 0:10am and include all teams that have been submitted (either earlier in the day or on a previous day) as of the start of the tournament. Currently, each team plays every other team 2 times (to reduce randomness, the final run after submission will include 8 runs), but this may change later in the semester. The layouts used in the tournament will be drawn from both the default layouts included in the zip file as well as randomly generated layouts each night. All layouts are symmetric, and the team that moves first is randomly chosen. The [results](https://people.eng.unimelb.edu.au/nlipovetzky/comp90054tournament/) are updated on the website after the tournament completes each night – here you can view overall rankings and scores for each match. You can also download replays, the layouts used, and the stdout / stderr logs for each agent.
## Designing Agents
Unlike project 2, an agent now has the more complex job of trading off offense versus defense and effectively functioning as both a ghost and a Pacman in a team setting. Furthermore, the limited information provided to your agent will likely necessitate some probabilistic tracking (like project 4). Finally, the added time limit of computation introduces new challenges.
### Baseline Team:
To kickstart your agent design, we have provided you with a team of two baseline agents, defined in `baselineTeam.py`. They are both quite bad. The `OffensiveReflexAgent` moves toward the closest food on the opposing side. The `DefensiveReflexAgent` wanders around on its own side and tries to chase down invaders it happens to see.
### File naming:
For the purpose of testing or running games locally, you can define a team of agents in any arbitrarily-named python file. When submitting to the nightly tournament, however, you must define your agents in `myTeam.py` (and you must also create a `name.txt` file that specifies your team name).
### Interface:
The `GameState` in `capture.py` should look familiar, but contains new methods like `getRedFood`, which gets a grid of food on the red side (note that the grid is the size of the board, but is only true
for cells on the red side with food). Also, note that you can list a team’s indices with `getRedTeamIndices`, or test membership with `isOnRedTeam`.
Finally, you can access the list of noisy distance observations via `getAgentDistances`. These distances are within 6 of the truth, and the noise is chosen uniformly at random from the range [-6, 6] (e.g., if the
true distance is 6, then each of {0, 1, …, 12} is chosen with probability 1/13). You can get the likelihood of a noisy reading using `getDistanceProb`.
### Distance Calculation:
To facilitate agent development, we provide code in `distanceCalculator.py` to supply shortest path maze distances.
To get started designing your own agent, we recommend subclassing the `CaptureAgent` class. This provides access to several convenience methods. Some useful methods are:
“`python
def getFood(self, gameState):
“””
Returns the food you’re meant to eat. This is in the form
of a matrix where m[x][y]=true if there is food you can
eat (based on your team) in that square.
“””
def getFoodYouAreDefending(self, gameState):
“””
Returns the food you’re meant to protect (i.e., that your
opponent is supposed to eat). This is in the form of a
matrix where m[x][y]=true if there is food at (x,y) that
your opponent can eat.
“””
def getOpponents(self, gameState):
“””
Returns agent indices of your opponents. This is the list
of the numbers of the agents (e.g., red might be “1,3,5”)
“””
def getTeam(self, gameState):
“””
Returns agent indices of your team. This is the list of
the numbers of the agents (e.g., red might be “1,3,5”)
“””
def getScore(self, gameState):
“””
Returns how much you are beating the other team by in the
form of a number that is the difference between your score
and the opponents score. This number is negative if you’re
losing.
“””
def getMazeDistance(self, pos1, pos2):
“””
Returns the distance between two points; These are calculated using the provided
distancer object.
If distancer.getMazeDistances() has been called, then maze distances are available.
Otherwise, this just returns Manhattan distance.
“””
def getPreviousObservation(self):
“””
Returns the GameState object corresponding to the last
state this agent saw (the observed state of the game last
time this agent moved – this may not include all of your
opponent’s agent locations exactly).
“””
def getCurrentObservation(self):
“””
Returns the GameState object corresponding this agent’s
current observation (the observed state of the game – this
may not include all of your opponent’s agent locations
exactly).
“””
def debugDraw(self, cells, color, clear=False):
“””
Draws a colored box on each of the cells you specify. If clear is True,
will clear all old drawings before drawing on the specified cells.
This is useful for debugging the locations that your code works with.
color: list of RGB values between 0 and 1 (i.e. [1,0,0] for red)
cells: list of game positions to draw on (i.e. [(20,5), (3,22)])
“””
“`
### Restrictions:
You are free to design any agent you want. However, you will need to respect the provided APIs if you want to participate in the tournaments. Agents which compute during the opponent’s turn will be disqualified. In particular, any form of multi-threading is disallowed, because we have found it very hard to ensure that no computation takes place on the opponent’s turn.
### Warning:
If one of your agents produces any stdout/stderr output during its games in the nightly tournaments, that output will be included in the contest results posted on the website. Additionally, in some cases a stack trace may be shown among this output in the event that one of your agents throws an exception. You should design your code in such a way that this does not expose any information that you wish to keep confidential.
## Contest Details
### Teams: You may work in teams of up to 3/4 people.
### Prizes: Rankings are determined according to the number of points
received in a nightly round-robin tournaments, where a win is worth 3
points, a tie is worth 1 point, and losses are worth 0 (Ties are not
worth very much to discourage stalemates). To be included in a nightly
tournament, your submission must be in by 0:05pm that night.
Extra credit will be awarded according to the final competition, but participating early in the pre-competitions will increase your learning and feedback. If your team wins one of the final nightly tournaments you will receive 1 additional point.
In addition, dastardly staff members have entered the tournament with
their own devious agents, seeking fame and glory. These agents have team
names beginning with Staff-.
### Prize Summary:
– Will give diplomas to the winners
The earlier you submit your agents, the better your chances of earning a high ranking, and the more chances you will have to defeat the staff agents.
### Important dates:
———– ———— ———————————————————————-
Friday 13/9/2010 Contest announced and posted
Firday 27/9/2010 Participate at the pre-competition, time starts to run out.
Wednesday 16/10/2010 Final submission of contest
Thursday 24/10/2010 Results announced in class
———– ———— ———————————————————————-
## Acknowledgements
This tournament was developed by Berkley and adpated by staff at The University of Melbourne and RMIT.
Have fun! Please bring our attention to any problems you discover.