agents.py

"""Implement Agents and Environments (Chapters 1-2).

The class hierarchies are as follows:

Object ## A physical object that can exist in an environment
    Agent
        Wumpus
        RandomAgent
        ReflexVacuumAgent
        ...
    Dirt
    Wall
    ...
    
Environment ## An environment holds objects, runs simulations
    XYEnvironment
        VacuumEnvironment
        WumpusEnvironment

EnvGUI ## A window with a graphical representation of the Environment

EnvToolbar ## contains buttons for controlling EnvGUI

EnvCanvas ## Canvas to display the environment of an EnvGUI

"""

# TO DO:
# Implement grabbing correctly.
# When an object is grabbed, does it still have a location?
# What if it is released?
# What if the grabbed or the grabber is deleted?
# What if the grabber moves?
#
# Speed control in GUI does not have any effect -- fix it.

from utils import *
import random, copy

#______________________________________________________________________________


class Object(object):
    """This represents any physical object that can appear in an Environment.
    You subclass Object to get the objects you want.  Each object can have a
    .__name__  slot (used for output only)."""
    def __repr__(self):
        return '<%s>' % getattr(self, '__name__', self.__class__.__name__)

    def is_alive(self):
        """Objects that are 'alive' should return true."""
        return hasattr(self, 'alive') and self.alive

    def show_state(self):
        """Display the agent's internal state.  Subclasses should override."""
        print "I don't know how to show_state."

    def display(self, canvas, x, y, width, height):
        # Do we need this?
        """Display an image of this Object on the canvas."""
        pass

class Agent(Object):
    """An Agent is a subclass of Object with one required slot,
    .program, which should hold a function that takes one argument, the
    percept, and returns an action. (What counts as a percept or action
    will depend on the specific environment in which the agent exists.) 
    Note that 'program' is a slot, not a method.  If it were a method,
    then the program could 'cheat' and look at aspects of the agent.
    It's not supposed to do that: the program can only look at the
    percepts.  An agent program that needs a model of the world (and of
    the agent itself) will have to build and maintain its own model.
    There is an optional slot, .performance, which is a number giving
    the performance measure of the agent in its environment."""

    def __init__(self, program=None):
        self.alive = True
        self.bump = False
        if program is None:
            def program(percept):
                return raw_input('Percept=%s; action? ' % percept)
        self.program = program

    def can_grab(self, obj):
        """Returns True if this agent can grab this object.
        Override for appropriate subclasses of Agent and Object."""
        return False
    
def TraceAgent(agent):
    """Wrap the agent's program to print its input and output. This will let
    you see what the agent is doing in the environment."""
    old_program = agent.program
    def new_program(percept):
        action = old_program(percept)
        print '%s perceives %s and does %s' % (agent, percept, action)
        return action
    agent.program = new_program
    return agent

#______________________________________________________________________________

def TableDrivenAgentProgram(table):
    """This agent selects an action based on the percept sequence.
    It is practical only for tiny domains.
    To customize it, provide as table a dictionary of all
    {percept_sequence:action} pairs. [Fig. 2.7]"""
    percepts = []
    def program(percept):
        percepts.append(percept)
        action = table.get(tuple(percepts))
        return action
    return program

def RandomAgentProgram(actions):
    "An agent that chooses an action at random, ignoring all percepts."
    def program(percept):
        return random.choice(actions)
    return program

#______________________________________________________________________________

def SimpleReflexAgentProgram(rules, interpret_input):
    "This agent takes action based solely on the percept. [Fig. 2.10]"
    def program(percept):
        state = interpret_input(percept)
        rule = rule_match(state, rules)
        action = rule.action
        return action
    return program

def ModelBasedReflexAgentProgram(rules, update_state):
    "This agent takes action based on the percept and state. [Fig. 2.12]"
    def program(percept):
        program.state = update_state(program.state, program.action, percept)
        rule = rule_match(program.state, rules)
        action = rule.action
        return action
    program.state = program.action = None
    return program

def rule_match(state, rules):
    "Find the first rule that matches state."
    for rule in rules:
        if rule.matches(state):
            return rule

#______________________________________________________________________________

loc_A, loc_B = (0, 0), (1, 0) # The two locations for the Vacuum world


def RandomVacuumAgent():
    "Randomly choose one of the actions from the vacuum environment."
    return Agent(RandomAgentProgram(['Right', 'Left', 'Suck', 'NoOp']))


def TableDrivenVacuumAgent():
    "[Fig. 2.3]"
    table = {((loc_A, 'Clean'),): 'Right',
             ((loc_A, 'Dirty'),): 'Suck',
             ((loc_B, 'Clean'),): 'Left',
             ((loc_B, 'Dirty'),): 'Suck',
             ((loc_A, 'Clean'), (loc_A, 'Clean')): 'Right',
             ((loc_A, 'Clean'), (loc_A, 'Dirty')): 'Suck',
             # ...
             ((loc_A, 'Clean'), (loc_A, 'Clean'), (loc_A, 'Clean')): 'Right',
             ((loc_A, 'Clean'), (loc_A, 'Clean'), (loc_A, 'Dirty')): 'Suck',
             # ...
             }
    return Agent(TableDrivenAgentProgram(table))


def ReflexVacuumAgent():
    "A reflex agent for the two-state vacuum environment. [Fig. 2.8]"
    def program((location, status)):
        if status == 'Dirty': return 'Suck'
        elif location == loc_A: return 'Right'
        elif location == loc_B: return 'Left'
    return Agent(program)

def ModelBasedVacuumAgent():
    "An agent that keeps track of what locations are clean or dirty."
    model = {loc_A: None, loc_B: None}
    def program((location, status)):
        "Same as ReflexVacuumAgent, except if everything is clean, do NoOp."
        model[location] = status ## Update the model here
        if model[loc_A] == model[loc_B] == 'Clean': return 'NoOp'
        elif status == 'Dirty': return 'Suck'
        elif location == loc_A: return 'Right'
        elif location == loc_B: return 'Left'
    return Agent(program)

#______________________________________________________________________________


class Environment(object):
    """Abstract class representing an Environment.  'Real' Environment classes
    inherit from this. Your Environment will typically need to implement:
        percept:           Define the percept that an agent sees.
        execute_action:    Define the effects of executing an action.
                           Also update the agent.performance slot.
    The environment keeps a list of .objects and .agents (which is a subset
    of .objects). Each agent has a .performance slot, initialized to 0.
    Each object has a .location slot, even though some environments may not
    need this."""

    def __init__(self):
        self.objects = []
        self.agents = []

    def object_classes(self):
        return [] ## List of classes that can go into environment

    def percept(self, agent):
        "Return the percept that the agent sees at this point. (Implement this.)"
        abstract

    def execute_action(self, agent, action):
        "Change the world to reflect this action. (Implement this.)"
        abstract

    def default_location(self, object):
        "Default location to place a new object with unspecified location."
        return None

    def exogenous_change(self):
        "If there is spontaneous change in the world, override this."
        pass

    def is_done(self):
        "By default, we're done when we can't find a live agent."
        for agent in self.agents:
            if agent.is_alive(): return False
        return True

    def step(self):
        """Run the environment for one time step. If the
        actions and exogenous changes are independent, this method will
        do.  If there are interactions between them, you'll need to
        override this method."""
        if not self.is_done():
            actions = [agent.program(self.percept(agent))
                       for agent in self.agents]
            for (agent, action) in zip(self.agents, actions):
                self.execute_action(agent, action)
            self.exogenous_change()

    def run(self, steps=1000):
        """Run the Environment for given number of time steps."""
        for step in range(steps):
            if self.is_done(): return
            self.step()

    def list_objects_at(self, location, oclass=Object):
        "Return all objects exactly at a given location."
        return [obj for obj in self.objects
                if obj.location == location and isinstance(obj, oclass)]
    
    def some_objects_at(self, location, oclass=Object):
        """Return true if at least one of the objects at location
        is an instance of class oclass (or a subclass)."""
        return self.list_objects_at(location, oclass) != []

    def add_object(self, obj, location=None):
        """Add an object to the environment, setting its location. Also keep
        track of objects that are agents.  Shouldn't need to override this."""
        obj.location = location or self.default_location(obj)
        self.objects.append(obj)
        if isinstance(obj, Agent):
            obj.performance = 0
            self.agents.append(obj)

    def delete_object(self, obj):
        """Remove an object from the environment."""
        try:
            self.objects.remove(obj)
        except ValueError, e:
            print e
            print "  in Environment delete_object"
            print "  Object to be removed: %s at %s" % (obj, obj.location)
            print "  from list: %s" % [(obj, obj.location)
                                       for obj in self.objects]
        if obj in self.agents:
            self.agents.remove(obj)

class XYEnvironment(Environment):
    """This class is for environments on a 2D plane, with locations
    labelled by (x, y) points, either discrete or continuous.

    Agents perceive objects within a radius.  Each agent in the
    environment has a .location slot which should be a location such
    as (0, 1), and a .holding slot, which should be a list of objects
    that are held."""

    def __init__(self, width=10, height=10):
        super(XYEnvironment, self).__init__()
        update(self, width=width, height=height, observers=[])
        
    def objects_near(self, location, radius=None):
        "Return all objects within radius of location."
        if radius is None: radius = self.perceptible_distance
        radius2 = radius * radius
        return [obj for obj in self.objects
                if distance2(location, obj.location) <= radius2]

    perceptible_distance = 1

    def percept(self, agent):
        "By default, agent perceives objects within a default radius."
        return [self.object_percept(obj, agent)
                for obj in self.objects_near(agent.location)]

    def execute_action(self, agent, action):
        agent.bump = False
        if action == 'TurnRight':
            agent.heading = self.turn_heading(agent.heading, -1)
        elif action == 'TurnLeft':
            agent.heading = self.turn_heading(agent.heading, +1)
        elif action == 'Forward':
            self.move_to(agent, vector_add(agent.heading, agent.location))
#         elif action == 'Grab':
#             objs = [obj for obj in self.list_objects_at(agent.location)
#                     if agent.can_grab(obj)]
#             if objs:
#                 agent.holding.append(objs[0])
        elif action == 'Release':
            if agent.holding:
                agent.holding.pop()

    def object_percept(self, obj, agent): #??? Should go to object?
        "Return the percept for this object."
        return obj.__class__.__name__

    def default_location(self, object):
        return (random.choice(self.width), random.choice(self.height))

    def move_to(self, obj, destination):
        "Move an object to a new location."
        obj.bump = self.some_objects_at(destination, Obstacle)
        if not obj.bump:
            obj.location = destination
            for o in self.observers:
                o.object_moved(obj)
        
    def add_object(self, obj, location=(1, 1)):
        super(XYEnvironment, self).add_object(obj, location)
        obj.holding = []
        obj.held = None
        for obs in self.observers:
            obs.object_added(obj)

    def delete_object(self, obj):
        super(XYEnvironment, self).delete_object(obj)
        # Any more to do?  Object holding anything or being held?
        for obs in self.observers:
            obs.object_deleted(obj)
    
    def add_walls(self):
        "Put walls around the entire perimeter of the grid."
        for x in range(self.width):
            self.add_object(Wall(), (x, 0))
            self.add_object(Wall(), (x, self.height-1))
        for y in range(self.height):
            self.add_object(Wall(), (0, y))
            self.add_object(Wall(), (self.width-1, y))

    def add_observer(self, observer):
        """Adds an observer to the list of observers.  
        An observer is typically an EnvGUI.
        
        Each observer is notified of changes in move_to and add_object,
        by calling the observer's methods object_moved(obj, old_loc, new_loc)
        and object_added(obj, loc)."""
        self.observers.append(observer)
        
    def turn_heading(self, heading, inc):
        "Return the heading to the left (inc=+1) or right (inc=-1) of heading."
        return turn_heading(heading, inc)

class Obstacle(Object):
    """Something that can cause a bump, preventing an agent from
    moving into the same square it's in."""
    pass

class Wall(Obstacle):
    pass

#______________________________________________________________________________
## Vacuum environment 

class Dirt(Object):
    pass
    
class VacuumEnvironment(XYEnvironment):
    """The environment of [Ex. 2.12]. Agent perceives dirty or clean,
    and bump (into obstacle) or not; 2D discrete world of unknown size;
    performance measure is 100 for each dirt cleaned, and -1 for
    each turn taken."""

    def __init__(self, width=10, height=10):
        super(VacuumEnvironment, self).__init__(width, height)
        self.add_walls()

    def object_classes(self):
        return [Wall, Dirt, ReflexVacuumAgent, RandomVacuumAgent,
                TableDrivenVacuumAgent, ModelBasedVacuumAgent]

    def percept(self, agent):
        """The percept is a tuple of ('Dirty' or 'Clean', 'Bump' or 'None').
        Unlike the TrivialVacuumEnvironment, location is NOT perceived."""
        status = if_(self.some_objects_at(agent.location, Dirt),
                     'Dirty', 'Clean')
        bump = if_(agent.bump, 'Bump', 'None')
        return (status, bump)

    def execute_action(self, agent, action):
        if action == 'Suck':
            dirt_list = self.list_objects_at(agent.location, Dirt)
            if dirt_list != []:
                dirt = dirt_list[0]
                agent.performance += 100
                self.delete_object(dirt)
        else:
            super(VacuumEnvironment, self).execute_action(agent, action)

        if action != 'NoOp':
            agent.performance -= 1

class TrivialVacuumEnvironment(Environment):
    """This environment has two locations, A and B. Each can be Dirty
    or Clean.  The agent perceives its location and the location's
    status. This serves as an example of how to implement a simple
    Environment."""

    def __init__(self):
        super(TrivialVacuumEnvironment, self).__init__()
        self.status = {loc_A: random.choice(['Clean', 'Dirty']),
                       loc_B: random.choice(['Clean', 'Dirty'])}

    def object_classes(self):
        return [Wall, Dirt, ReflexVacuumAgent, RandomVacuumAgent, 
                TableDrivenVacuumAgent, ModelBasedVacuumAgent]
    
    def percept(self, agent):
        "Returns the agent's location, and the location status (Dirty/Clean)."
        return (agent.location, self.status[agent.location])

    def execute_action(self, agent, action):
        """Change agent's location and/or location's status; track performance.
        Score 10 for each dirt cleaned; -1 for each move."""
        if action == 'Right':
            agent.location = loc_B
            agent.performance -= 1
        elif action == 'Left':
            agent.location = loc_A
            agent.performance -= 1
        elif action == 'Suck':
            if self.status[agent.location] == 'Dirty':
                agent.performance += 10
            self.status[agent.location] = 'Clean'

    def default_location(self, object):
        "Agents start in either location at random."
        return random.choice([loc_A, loc_B])

#______________________________________________________________________________
## The Wumpus World

class Gold(Object): pass
class Pit(Object): pass
class Arrow(Object): pass
class Wumpus(Agent): pass
class Explorer(Agent): pass

class WumpusEnvironment(XYEnvironment):

    def __init__(self, width=10, height=10):
        super(WumpusEnvironment, self).__init__(width, height)
        self.add_walls()

    def object_classes(self):
        return [Wall, Gold, Pit, Arrow, Wumpus, Explorer]

    ## Needs a lot of work ...

    
#______________________________________________________________________________

def compare_agents(EnvFactory, AgentFactories, n=10, steps=1000):
    """See how well each of several agents do in n instances of an environment.
    Pass in a factory (constructor) for environments, and several for agents.
    Create n instances of the environment, and run each agent in copies of 
    each one for steps. Return a list of (agent, average-score) tuples."""
    envs = [EnvFactory() for i in range(n)]
    return [(A, test_agent(A, steps, copy.deepcopy(envs))) 
            for A in AgentFactories]

def test_agent(AgentFactory, steps, envs):
    "Return the mean score of running an agent in each of the envs, for steps"
    def score(env):
        agent = AgentFactory()
        env.add_object(agent)
        env.run(steps)
        return agent.performance
    return mean(map(score, envs))

#_________________________________________________________________________

__doc__ += """
>>> a = ReflexVacuumAgent()
>>> a.program((loc_A, 'Clean'))
'Right'
>>> a.program((loc_B, 'Clean'))
'Left'
>>> a.program((loc_A, 'Dirty'))
'Suck'
>>> a.program((loc_A, 'Dirty'))
'Suck'

>>> e = TrivialVacuumEnvironment()
>>> e.add_object(ModelBasedVacuumAgent())
>>> e.run(5)

## Environments, and some agents, are randomized, so the best we can
## give is a range of expected scores.  If this test fails, it does
## not necessarily mean something is wrong.
>>> envs = [TrivialVacuumEnvironment() for i in range(100)]
>>> def testv(A): return test_agent(A, 4, copy.deepcopy(envs)) 
>>> 7 < testv(ModelBasedVacuumAgent) < 11
True
>>> 5 < testv(ReflexVacuumAgent) < 9
True
>>> 2 < testv(TableDrivenVacuumAgent) < 6
True
>>> 0.5 < testv(RandomVacuumAgent) < 3
True
"""

#______________________________________________________________________________
# GUI - Graphical User Interface for Environments
# If you do not have Tkinter installed, either get a new installation of Python
# (Tkinter is standard in all new releases), or delete the rest of this file
# and muddle through without a GUI.

import Tkinter as tk

class EnvGUI(tk.Tk, object):

    def __init__(self, env, title = 'AIMA GUI', cellwidth=50, n=10):

        # Initialize window
        
        super(EnvGUI, self).__init__()
        self.title(title)

        # Create components
        
        canvas = EnvCanvas(self, env, cellwidth, n)
        toolbar = EnvToolbar(self, env, canvas)
        for w in [canvas, toolbar]:
            w.pack(side="bottom", fill="x", padx="3", pady="3")


class EnvToolbar(tk.Frame, object):

    def __init__(self, parent, env, canvas):
        super(EnvToolbar, self).__init__(parent, relief='raised', bd=2)

        # Initialize instance variables
        
        self.env = env
        self.canvas = canvas
        self.running = False
        self.speed = 1.0

        # Create buttons and other controls
        
        for txt, cmd in [('Step >', self.env.step),
                         ('Run >>', self.run),
                         ('Stop [ ]', self.stop),
                         ('List objects', self.list_objects),
                         ('List agents', self.list_agents)]:
            tk.Button(self, text=txt, command=cmd).pack(side='left')

        tk.Label(self, text='Speed').pack(side='left')
        scale = tk.Scale(self, orient='h',
                         from_=(1.0), to=10.0, resolution=1.0,
                         command=self.set_speed)
        scale.set(self.speed)
        scale.pack(side='left')

    def run(self):
        print 'run'
        self.running = True
        self.background_run()

    def stop(self):
        print 'stop'
        self.running = False

    def background_run(self):
        if self.running:
            self.env.step()
            # ms = int(1000 * max(float(self.speed), 0.5))
            #ms = max(int(1000 * float(self.delay)), 1)
            delay_sec = 1.0 / max(self.speed, 1.0) # avoid division by zero
            ms = int(1000.0 * delay_sec)  # seconds to milliseconds
            self.after(ms, self.background_run)
        
    def list_objects(self):
        print "Objects in the environment:"
        for obj in self.env.objects:
            print "%s at %s" % (obj, obj.location)

    def list_agents(self):
        print "Agents in the environment:"
        for agt in self.env.agents:
            print "%s at %s" % (agt, agt.location)

    def set_speed(self, speed):
        self.speed = float(speed)