Pelita

Table of contents

• Pelita
  • Table of contents
• Introduction
  • Your task
  • Content of this repository
  • Running a game
  • The graphical interface
    • Key bindings
  • Testing
    • Manual testing
    • Unit testing
  • Layouts
  • Full API Description
    • The maze
    • The move function
    • The Bot object
    • Running multiple games in the background

---

Introduction

Pelita is a Pac-Man like game. Two teams each of two characters (bots) are placed in a maze with food pellets. The blue team starts on the left of the maze, with bots named a and b. The red team starts on the right of the maze, with bots named x and y.

The maze is split into two parts, the left one belongs to the blue team, the right one belongs to the red team. When a bot is in its own homezone it is a ghost. A ghost can defend its own food pellets by killing the enemies. When a bot is in its enemy's homezone it is a pac-man and can eat the enemy's food. The game is turn-based: one bot moves, the rules of the game are applied, a new state of the game is generated, the next bot moves. The first bot of the blue team, bot a, moves first, then the first bot of the red team, bot x, moves, then the second bot, b, of the blue team, and finally the second bot, y, of the read team. This defines a round. A standard game lasts at most 300 rounds.

The rules:

• eating food: when a bot eats a food pellet, the food pellet is permanently removed from the maze and one point is scored for the bot's team.

• killing enemies: when a ghost kills an enemy pac-man, the killed pac-man is immediately reset to its starting position and 5 points are scored for the ghost's team.

• noisy enemy positions: bots can know their enemies' exact positions only when the enemies are within a distance of 5 squares. If the enemies are further away than that, the bots have access only to a noisy position (more details below).

• timeouts: each bot has 3 seconds to return a valid move. If it doesn't return in time a random legal move is executed instead and an error is recorded.

• illegal moves: if a bot returns an illegal move, a random legal move is executed instead and an error is recorded.

• errors: each team can commit a maximum of 4 errors. At the 5th error the team is disqualified and the game is over. Errors are either illegal moves or timeouts.

• fatal errors: if a bot raises an Exception, the team is immediately disqualified and the game is over.

• game over: the game ends when one team eats all of its enemy's food pellets or after 300 rounds or when a team is disqualified.

• the winner: the team with the highest score after game over wins the game, regardless of which team finished the food. A team also wins if the opponent team is disqualified, regardless of the score.

Your task

Your task as a group is to write a bot implementation. You have to implement the intelligence to navigate your bots successfully through the maze, kill the enemy's pac-men, and eat the enemy's food. You can find a minimal implementation in the demo01_stopping.py file:

TEAM_NAME = 'StoppingBots'

def move(bot, state):
    # do not move at all
    return bot.position

As seen above, your implementation consists of a team name (the TEAM_NAME string) and a function move, which takes a bot object (which contains all the data that you need to make your decision) and a state dict (which you are supposed to fill) and returns the next position for current bot. Don't panic right now, in the Full API Description section you'll find all the details.

Content of this repository

In this repository you will find several demo implementations (all files named demoXX_XXX.py), that you can use as a starting point for your own implementations. The demos build upon each other, so it will be useful to follow them in order.

The files named test_demoXX_XXX.py are example unit tests for some of the demo bot implementations. You can run the tests within a clone of this repo with pytest by typing:

$ python -m pytest

In the notebooks folder you can find some examples of other ways of interacting with the pelita code. notebooks/nb1_matplotlib_and_numpy.ipynb shows how you might use numpy arrays and matplotlib to visualize game states. notebooks/nb2_enemy_noise.ipynb uses the same approach to visualize enemy positions and noisy positionsas explained below. Notebooks can be a useful way to get an intuition for the bot object properties or for visualizing game statistics, but should not be used to actually write your bot.

Running a game

• To run a demo game, just type at the command line:

  $ pelita

• To test a demo implementation against another one:

  $ pelita demo03_smartrandom.py demo05_basic_defender.py

More info about the command pelita below

The graphical interface

Pelita’s graphical interface contains a few tools that can help with debugging. For example, using the ‘slower’ and ‘faster’ buttons, one may change the maximum speed of the game. (Of course, it is not possible to speed up slow bots using this.)

When the game has been paused, the ‘step’ and ‘round’ buttons will execute a single step or play a complete round.

The ‘debug’ button will show the grid, which is useful to count the exact distances between two spots as well as the line of sight around the current bot. Additionally, it will show ‘ghost bots’, which denote the positions where the current (ie. the bot that has just moved) saw the noised enemies.

Additionally, selecting a specific square in the grid will show its coordinates and if the square is a wall or contains food or bots are sitting on it:

Key bindings

There are shortcuts if you don’t like using the mouse:

q               Quit
#               Enable grid + debug mode
>               Increase speed
<               Decrease speed
[Space]         Play/pause
[Enter]         Play a single step (when in pause mode)
[Shift+Enter]   Play one round (when in pause mode)

Testing

There are several strategies to test your bot implementations.

Manual testing

You can test an implementation by playing against some of the demo implementations or against itself.

You can pass several options to the pelita command to help you with testing.

• --seed SEED you can pass the --seed option to the pelita command to repeat a game using the same random seed. The random seed for a game is printed on standard output:

  $ pelita demo03_smartrandom.py demo02_random.py
  Replay this game with --seed 7487886765553999309
  Using layout 'normal_018'
  ᗧ blue team 'demo03_smartrandom.py' -> 'SmartRandomBots'
  ᗧ red team 'demo02_random.py' -> 'RandomBots'
  ...

  You can replay this exact game:

  $ pelita --seed 7487886765553999309 demo03_smartrandom.py demo02_random.py
  ...

• --stop-at ROUND you can pass the --stop-at option to the pelita command to stop a game at a specific round. You can then, for example, show the grid, play the next turns step by step, etc.

• --null you can pass the option --null to the pelita command to suppress the graphical interface and just let the game play in the background. This is useful if you want to play many games and just look at their outcome, for example to gather statistics. (A better strategy to play games in the background is shown in demo10_background_games.py).

• --ascii you can pass the option --ascii to the pelita command to suppress the graphical interface and instead use a textual visualization in the terminal, which contains a lot of useful debug info.

• --progress similar to --null but showing the progress of the running game.

• --no-timeout you can pass the option --no-timeout to disable the timeout detection. Usually, if a bot takes longer than 3 seconds to respond it is considered an error. Disabling it is useful for example if you want to run a debugger on your bot, like in demo07_debugger.py

• --help the full list of supported options can be obtained by passing --help.

Unit testing

You should write unit tests to test your utility functions and to test your bot implementations. It is quite hard to test a full strategy, especially because you can not have a real opponent in a test game. It is useful to focus on specific situations (called layouts) and verify that your bot is doing the right thing. More info about layouts in the Layouts section.

Several examples for unit testing are available in this repo in the files named test_XXX.py. If you name your test files starting with test_ your tests will be automatically picked up by pytest when you run on the console in the directory where you cloned this repo:

$ python -m pytest

An example unit test could look like this:

from demo01_stopping import move
from pelita.utils import setup_test_game


def test_stays_there_builtin_random_layout():
    # Using a random builtin layout, verify that the bot stays on its initial position
    bot = setup_test_game(layout=None, is_blue=True)
    next_pos = move(bot, {})
    # check that we did not move
    assert next_pos == bot.position

The test first has to set up a game environment by using the utility function setup_test_game imported from the pelita.utils module before calling the move funtion of the loaded bot.

setup_test_game(layout, is_blue=True, round=None, score=None, seed=None, food=None, bots=None, is_noisy=None) ⟶ bot

Given a layout, it returns a Bot which you can pass to the move function.

The blue team's homezone is always on the left and the red team's homezone is always on the right. In the setup_test_game function you can pass is_blue which defines which side you are currently playing on.

The full documentation for the setup_test_game function can be read in its docstring:

$ python
>>> from pelita.utils import setup_test_game
>>> help(setup_test_game)
>>> ...

Layouts

When you play a game using the command pelita without specifying a specific layout with the --layout option, one of the built-in layouts will be picked at random. The name of the used layout is seen in the lower right corner of the GUI and it is also print to the terminal:

$ pelita
Replay this game with --seed 354761256309345545
Using layout 'normal_035'

When you run your own games or write tests, you may want to play a game on a fixed layout. You can do that by specifying the option --layout normal_035 to the pelita command, or passing layout="normal_035" to setup_test_game.

You may also want to specify very simple layouts to test some basic features of your bot that require you to know exactly where the walls, the enemies and the food are. In this case you can pass to setup_test_game a layout string. There are several examples of layout strings in the demo tests.

By inserting print(bot) within your move function, you can print the layout string corresponding to the current layout, together with other useful information. An example:

Basic Attacker Bots (you) vs Basic Defender Bots.
Playing on blue side. Current turn: 1. Bot: b. Round: 79, score: 11:15. timeouts: 0:0
################################
#     .  .   .      #    #     #
#  ##### ####  ##.  # .# # ### #
#     ..       ##   #  # #     #
#  #    .. # . ###.##  #    . .#
#  #.. ..  #. .     #. ###### ##
#. # .    .  .      #  #    b .#
# ######## # .   .  #        # #
# #    a   #  .     # ######## #
#.  .   #  #  .     x     . # .#
## ###### .#     . .#  ..   #  #
#. .    #  ##.###   #y..    #  #
#     # #  #   ##              #
# ### # #. #  .##  #### #####  #
#     #    #      .   .  .     #
################################
Bots: {'a': (7, 8), 'x': (20, 9), 'b': (28, 6), 'y': (21, 11)}
Noisy: {'a': False, 'x': True, 'b': False, 'y': True}
Food: [(4, 9), (14, 13), (10, 6), (1, 6), (1, 9), (13, 11), (4, 5), (14, 9), (5, 6),
(9, 1), (8, 5), (9, 4), (13, 4), (9, 13), (13, 1), (13, 7), (1, 11), (6, 1), (7, 3),
(12, 5), (14, 5), (3, 11), (14, 8), (5, 5), (8, 4), (10, 10), (13, 6), (7, 5),
(6, 3), (7, 8), (22, 2), (22, 11), (23, 10), (22, 14), (30, 6), (30, 9), (18, 4),
(17, 2), (24, 10), (21, 5), (23, 11), (28, 4), (17, 7), (30, 4), (17, 10), (19, 10),
(25, 14), (26, 9), (18, 14)]

The walls are identified by #, the food by ., the bots are a b for the blue team and x y for the red team. The exact coordinates of all food pellets, the bots and their noisy state are listed. More details about noise below.

You can create smaller mazes, which are easier to test with and can be typed directly into the tests. For example a maze 8x4 with the blue bots in (1, 1) and (1, 2), where the red bots are on (5,2) and (6,2) and food pellets in (2, 2) and (6,1), and an additional wall in (4,1) will look like this:

layout="""
########
#a # . #
#b.  xy#
########
"""

In case some objects are overlapping (for example you want to locate a red bot over a food pellet) you can pass a partial layout and specify the positions of the objects in a list of coordinates to setup_test_game. For example:

from pelita.utils import setup_test_game

def test_print_stuff():
    layout="""
    ########
    #a # . #
    #b.  xy#
    ########
    """
    bot = setup_test_game(layout=layout, food=[(1,1), (1,2), (5,2), (6,2)])
    print(bot)

Save this into a file test_test.py. When you run this test with python -m pytest -s test_test.py you get:

...

test_test.py blue (you) vs red.
Playing on blue side. Current turn: 0. Bot: a. Round: None, score: 0:0. timeouts: 0:0
########
#a # . #
#b.  xy#
########
Bots: {'a': (1, 1), 'x': (5, 2), 'b': (1, 2), 'y': (6, 2)}
Noisy: {'a': False, 'x': False, 'b': False, 'y': False}
Food: [(1, 1), (1, 2), (2, 2), (6, 2), (5, 1), (5, 2)]
...

Notice that we have to pass the option -s to pytest so that it shows what we print. By default pytest swallows everything that gets printed to standard output and standard error on the terminal.

Full API Description

The maze

The maze is a grid. Each square in the grid is defined by its coordinates. The default width of the maze is 32 squares, the default height is 16 squares. The coordinate system has the origin (0, 0) in the top left of the maze and its maximum value (31, 15) in the bottom right. Each square which is not a wall can be empty or contain a food pellet or one or more bots. The different mazes are called layouts. For the tournament all layouts will have the default values for width and height and will have a wall on all squares around the border.

The move function

move(bot, state) ⟶ (x, y)

The move function gets two input arguments:

• bot is a reference to the bot in your team corresponding to the current turn. It is an instance of the Bot object, which contains all information about the current state of the game

• state is a dictionary which can be used to hold information persistently over rounds. It is empty when the game starts. In the move function you can store whatever you want in it. Examples for the usage of the state dictionary can be found in demo04_basic_attacker.py, demo05_basic_defender.py, demo06_switching_bots.py:

  def move(bot, state):
      state['something_to_remember'] = 'an important string'
      return bot.position

  The same state dictionary is available to both bots of a team. It will be passed untouched to the move function at every turn. Thus, if your bots want to remember distinct things, it may make sense to create a dictionary key for each bot.

  def move(bot, state):
      state[bot.turn] = f'i am bot {bot.turn}'
      return bot.position

The move function returns the position to move the bot to in the current turn. The position is a tuple of two integers (x, y), which are the coordinates on the game grid.

Note that the returned value must represent a legal position, i.e. it must be an adjacent position and you can not move your bot onto a wall or outside of the maze. If you return an illegal position, a legal position will be chosen at random instead and an error will be recorded for your team. After 5 errors the game is over and you lose the game.

The Bot object

Note that the Bot object is read-only, i.e. any modifications you make to that object within the move function will be discarded at the next round. Use the state dictionary for keeping track of information between rounds.

• bot.turn is the turn this bot is playing, either 0 (for bot a and x) or 1 (for bot b and y).

• bot.other is the other bot in your team. It is a reference to a Bot object.

• bot.position is a tuple of the coordinates your bot is on at the moment. For example (3, 9).

• bot.legal_positions is a list of positions your bot can take in the current turn without hitting a wall. At each turn the bot can move by one square in the grid, either horizontally or vertically, if the target square is not a wall. Note that the bot can always stay in the same position, i.e. you can let your move function return bot.position.

• bot.walls is a tuple of the coordinates of the walls in the maze:

  ((0, 0), (1, 0), (2, 0), ..., (29, 15), (30, 15), (31, 15))

  so, if for example you want to test if position (3, 9) in the maze is a wall, you can do:

  (3, 9) in bot.walls

  The maze is also represented as a graph in the attribute bot.graph.

• bot.graph is a representation of the maze as a graph. The graph represents the free squares in the maze –i.e. all the non-wall coordinates– and their connections. The bot.graph object is an instance of the Graph class from the networkx library. bot.graph is immutable: an editable copy of the graph is returned by the bot.graph.copy() method if you need it.

  Examples for using a graph representation for shortest path calculations using the networkx library can be found in demo04_basic_attacker.py and demo05_basic_defender.py.

• bot.shape is a tuple with the size of the maze. The usual maze size will be 32x16, that is (32, 16), unless other layouts are specified.

• bot.homezone is a tuple of all the coordinates of your side of the maze that are not a wall. If for example you are the red team in a 32×16 maze, your homezone might be:

  ((16, 1), (16, 2), (16, 3), ..., (30, 11), (30, 13), (30, 14))

  as with bot.walls you can test if position (3, 9) is in your homezone with

  (3, 9) in bot.homezone

  You can check if you got assigned the blue team – your homezone is the left side of the maze – with bot.is_blue. Otherwise you are the red team and your homezone is the right side of the maze. The blue team always plays the first move.

• bot.food is the list of the coordinates of the food pellets in your own homezone

  [(17, 8), (24, 8), (17, 7), ...]

  as soon as the enemy starts eating your food pellets this list will shorten up!

• bot.track is a list of the coordinates of the positions that the bot has taken until now. It gets reset every time the bot gets killed by an enemy ghost.

• When you are killed, the property bot.was_killed is set to True until the next round.

• bot.score and bot.round tell you the score of your team and the round you are playing.

• bot.random is an instance of the Python internal pseudo-random number generator. Do not import the Python random module in your code, just use this for all your random operations. Example of using it are found in demo02_random.py, demo03_smartrandom.py, and several others. If you need to use the numpy random module, initialize it with a seed taken from this instance like this:

  np.random.seed(bot.random.randint(0, 2**32-1))

  Note that you want to do it only once per game!

• bot.error_count is a count of the errors your team has collected. Remember that if you commit 5 errors you lose the game, independent of the score. Errors are either timeouts (it takes longer than 3 seconds to execute your move function) or illegal positions returned by your move function.

• bot.say(text) allows you to print text as a sort of speech bubble attached to your bot in the graphical user interface.

• bot.kills is the number of enemy bots your bots has killed until now.

• bot.deaths is the number of times your bot has been killed until now.

• bot.char is the character representing the bot: a, b, x or y.

• bot.enemy is a list containing the references to the two enemy bots, which are also Bot objects, so they have all the properties we have just seen above. So, for example the position of the first enemy bot:

  bot.enemy[0].position

  This position may be not exact (see below the is_noisy property).

• bot.enemy[0].food is the list of coordinates of the food pellets you want to eat.

• bot.enemy[0].team_name you can also inspect the enemy team name with bot.enemy[0].team_name.

• bot.enemy[0].is_noisy your bot has a sight-radius of 5 squares. This means that when an enemy bot is located more than 5 squares away from your bot, bot.enemy[0].position will not be exact and bot.enemy[0].is_noisy will be True. The sight-radius for red bot y is the red area in the picture below. Red bot y will see the exact position of blue bot a, because it falls into its sight-radius. Instead, red bot y will see blue bot b as if it were located in one random legal position up to 5 squares away from its true position. An example of using the is_noisy property is given in demo05_basic_defender.py.

Running multiple games in the background

You may want to run multiple games in the background to gather statistics about your implementation, or to fit some parameters of your implementation. The script demo08_background_games.py is an example of this. You can run it like this:

python demo08_background_games.py