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"""Implement Agents and Environments (Chapters 1-2).
The class hierarchies are as follows:
Thing ## A physical object that can exist in an environment
Agent
Wumpus
Dirt
Wall
...
Environment ## An environment holds objects, runs simulations
XYEnvironment
VacuumEnvironment
WumpusEnvironment
An agent program is a callable instance, taking percepts and choosing actions
SimpleReflexAgentProgram
...
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
grid
import
distance2
from
statistics
import
mean
import
random
import
copy
import
collections
# ______________________________________________________________________________
class
Thing
(
object
):
"""This represents any physical object that can appear in an Environment.
You subclass Thing to get the things you want. Each thing can have a
.__name__ slot (used for output only)."""
def
__repr__
(
self
):
return
'<{}>'
.
format
(
getattr
(
self
,
'__name__'
,
self
.
__class__
.
__name__
))
def
is_alive
(
self
):
"Things 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 Thing on the canvas."
pass
class
Agent
(
Thing
):
"""An Agent is a subclass of Thing 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
self
.
holding
=
[]
self
.
performance
=
0
if
program
is
None
:
def
program
(
percept
):
return
eval
(
input
(
'Percept={}; action? '
.
format
(
percept
)))
assert
isinstance
(
program
,
collections
.
Callable
)
self
.
program
=
program
def
can_grab
(
self
,
thing
):
"""Returns True if this agent can grab this thing.
Override for appropriate subclasses of Agent and Thing."""
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
(
'{} perceives {} and does {}'
.
format
(
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. [Figure 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."
return
lambda
percept
:
random
.
choice
(
actions
)
# ______________________________________________________________________________
def
SimpleReflexAgentProgram
(
rules
,
interpret_input
):
"This agent takes action based solely on the percept. [Figure 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. [Figure 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
():
"[Figure 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. [Figure 2.8]"
def
program
(
percept
):
location
,
status
=
percept
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
(
percept
):
"Same as ReflexVacuumAgent, except if everything is clean, do NoOp."
location
,
status
=
percept
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 .things and .agents (which is a subset
of .things). Each agent has a .performance slot, initialized to 0.
Each thing has a .location slot, even though some environments may not
need this."""
def
__init__
(
self
):
self
.
things
=
[]
self
.
agents
=
[]
def
thing_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.)
'''
raise
NotImplementedError
def
execute_action
(
self
,
agent
,
action
):
"Change the world to reflect this action. (Implement this.)"
raise
NotImplementedError
def
default_location
(
self
,
thing
):
"Default location to place a new thing 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."
return
not
any
(
agent
.
is_alive
()
for
agent
in
self
.
agents
)
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
=
[]
for
agent
in
self
.
agents
:
if
agent
.
alive
:
actions
.
append
(
agent
.
program
(
self
.
percept
(
agent
)))
else
:
actions
.
append
(
""
)
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_things_at
(
self
,
location
,
tclass
=
Thing
):
"Return all things exactly at a given location."
return
[
thing
for
thing
in
self
.
things
if
thing
.
location
==
location
and
isinstance
(
thing
,
tclass
)]
def
some_things_at
(
self
,
location
,
tclass
=
Thing
):
"""Return true if at least one of the things at location
is an instance of class tclass (or a subclass)."""
return
self
.
list_things_at
(
location
,
tclass
)
!=
[]
def
add_thing
(
self
,
thing
,
location
=
None
):
"""Add a thing to the environment, setting its location. For
convenience, if thing is an agent program we make a new agent
for it. (Shouldn't need to override this."""
if
not
isinstance
(
thing
,
Thing
):
thing
=
Agent
(
thing
)
assert
thing
not
in
self
.
things
,
"Don't add the same thing twice"
thing
.
location
=
location
if
location
is
not
None
else
self
.
default_location
(
thing
)
self
.
things
.
append
(
thing
)
if
isinstance
(
thing
,
Agent
):
thing
.
performance
=
0
self
.
agents
.
append
(
thing
)
def
delete_thing
(
self
,
thing
):
"""Remove a thing from the environment."""
try
:
self
.
things
.
remove
(
thing
)
except
ValueError
as
e
:
print
(
e
)
print
(
" in Environment delete_thing"
)
print
(
" Thing to be removed: {} at {}"
.
format
(
thing
,
thing
.
location
))
print
(
" from list: {}"
.
format
([(
thing
,
thing
.
location
)
for
thing
in
self
.
things
]))
if
thing
in
self
.
agents
:
self
.
agents
.
remove
(
thing
)
class
Direction
():
'''A direction class for agents that want to move in a 2D plane
Usage:
d = Direction("Down")
To change directions:
d = d + "right" or d = d + Direction.R #Both do the same thing
Note that the argument to __add__ must be a string and not a Direction object.
Also, it (the argument) can only be right or left. '''
R
=
"right"
L
=
"left"
U
=
"up"
D
=
"down"
def
__init__
(
self
,
direction
):
self
.
direction
=
direction
def
__add__
(
self
,
heading
):
if
self
.
direction
==
self
.
R
:
return
{
self
.
R
:
Direction
(
self
.
D
),
self
.
L
:
Direction
(
self
.
U
),
}.
get
(
heading
,
None
)
elif
self
.
direction
==
self
.
L
:
return
{
self
.
R
:
Direction
(
self
.
U
),
self
.
L
:
Direction
(
self
.
L
),
}.
get
(
heading
,
None
)
elif
self
.
direction
==
self
.
U
:
return
{
self
.
R
:
Direction
(
self
.
R
),
self
.
L
:
Direction
(
self
.
L
),
}.
get
(
heading
,
None
)
elif
self
.
direction
==
self
.
D
:
return
{
self
.
R
:
Direction
(
self
.
L
),
self
.
L
:
Direction
(
self
.
R
),
}.
get
(
heading
,
None
)
def
move_forward
(
self
,
from_location
):
x
,
y
=
from_location
if
self
.
direction
==
self
.
R
:
return
(
x
+
1
,
y
)
elif
self
.
direction
==
self
.
L
:
return
(
x
-
1
,
y
)
elif
self
.
direction
==
self
.
U
:
return
(
x
,
y
-
1
)
elif
self
.
direction
==
self
.
D
:
return
(
x
,
y
+
1
)
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 things 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 things
that are held."""
def
__init__
(
self
,
width
=
10
,
height
=
10
):
super
(
XYEnvironment
,
self
).
__init__
()
self
.
width
=
width
self
.
height
=
height
self
.
observers
=
[]
# Sets iteration start and end (no walls).
self
.
x_start
,
self
.
y_start
=
(
0
,
0
)
self
.
x_end
,
self
.
y_end
=
(
self
.
width
,
self
.
height
)
perceptible_distance
=
1
def
things_near
(
self
,
location
,
radius
=
None
):
"Return all things within radius of location."
if
radius
is
None
:
radius
=
self
.
perceptible_distance
radius2
=
radius
*
radius
return
[(
thing
,
radius2
-
distance2
(
location
,
thing
.
location
))
for
thing
in
self
.
things
if
distance2
(
location
,
thing
.
location
)
<=
radius2
]
def
percept
(
self
,
agent
):
'''By default, agent perceives things within a default radius.'''
return
self
.
things_near
(
agent
.
location
)
def
execute_action
(
self
,
agent
,
action
):
agent
.
bump
=
False
if
action
==
'TurnRight'
:
agent
.
direction
=
agent
.
direction
+
Direction
.
R
elif
action
==
'TurnLeft'
:
agent
.
direction
=
agent
.
direction
+
Direction
.
L
elif
action
==
'Forward'
:
agent
.
bump
=
self
.
move_to
(
agent
,
agent
.
direction
.
move_forward
(
agent
.
location
))
# elif action == 'Grab':
# things = [thing for thing in self.list_things_at(agent.location)
# if agent.can_grab(thing)]
# if things:
# agent.holding.append(things[0])
elif
action
==
'Release'
:
if
agent
.
holding
:
agent
.
holding
.
pop
()
def
default_location
(
self
,
thing
):
return
(
random
.
choice
(
self
.
width
),
random
.
choice
(
self
.
height
))
def
move_to
(
self
,
thing
,
destination
):
'''Move a thing to a new location. Returns True on success or False if there is an Obstacle
If thing is grabbing anything, they move with him '''
thing
.
bump
=
self
.
some_things_at
(
destination
,
Obstacle
)
if
not
thing
.
bump
:
thing
.
location
=
destination
for
o
in
self
.
observers
:
o
.
thing_moved
(
thing
)
for
t
in
thing
.
holding
:
self
.
delete_thing
(
t
)
self
.
add_thing
(
t
,
destination
)
t
.
location
=
destination
return
thing
.
bump
# def add_thing(self, thing, location=(1, 1)):
# super(XYEnvironment, self).add_thing(thing, location)
# thing.holding = []
# thing.held = None
# for obs in self.observers:
# obs.thing_added(thing)
def
add_thing
(
self
,
thing
,
location
=
(
1
,
1
),
exclude_duplicate_class_items
=
False
):
'''Adds things to the world.
If (exclude_duplicate_class_items) then the item won't be added if the location
has at least one item of the same class'''
if
(
self
.
is_inbounds
(
location
)):
if
(
exclude_duplicate_class_items
and
any
(
isinstance
(
t
,
thing
.
__class__
)
for
t
in
self
.
list_things_at
(
location
))):
return
super
(
XYEnvironment
,
self
).
add_thing
(
thing
,
location
)
def
is_inbounds
(
self
,
location
):
'''Checks to make sure that the location is inbounds (within walls if we have walls)'''
x
,
y
=
location
return
not
(
x
<
self
.
x_start
or
x
>=
self
.
x_end
or
y
<
self
.
y_start
or
y
>=
self
.
y_end
)
def
random_location_inbounds
(
self
,
exclude
=
None
):
'''Returns a random location that is inbounds (within walls if we have walls)'''
location
=
(
random
.
randint
(
self
.
x_start
,
self
.
x_end
),
random
.
randint
(
self
.
y_start
,
self
.
y_end
))
if
exclude
is
not
None
:
while
(
location
==
exclude
):
location
=
(
random
.
randint
(
self
.
x_start
,
self
.
x_end
),
random
.
randint
(
self
.
y_start
,
self
.
y_end
))
return
location
def
delete_thing
(
self
,
thing
):
'''Deletes thing, and everything it is holding (if thing is an agent)'''
if
isinstance
(
thing
,
Agent
):
for
obj
in
thing
.
holding
:
super
(
XYEnvironment
,
self
).
delete_thing
(
obj
)
for
obs
in
self
.
observers
:
obs
.
thing_deleted
(
obj
)
super
(
XYEnvironment
,
self
).
delete_thing
(
thing
)
for
obs
in
self
.
observers
:
obs
.
thing_deleted
(
thing
)
def
add_walls
(
self
):
'''Put walls around the entire perimeter of the grid.'''
for
x
in
range
(
self
.
width
):
self
.
add_thing
(
Wall
(), (
x
,
0
))
self
.
add_thing
(
Wall
(), (
x
,
self
.
height
-
1
))
for
y
in
range
(
self
.
height
):
self
.
add_thing
(
Wall
(), (
0
,
y
))
self
.
add_thing
(
Wall
(), (
self
.
width
-
1
,
y
))
# Updates iteration start and end (with walls).
self
.
x_start
,
self
.
y_start
=
(
1
,
1
)
self
.
x_end
,
self
.
y_end
=
(
self
.
width
-
1
,
self
.
height
-
1
)
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_thing,
by calling the observer's methods thing_moved(thing)
and thing_added(thing, 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
(
Thing
):
"""Something that can cause a bump, preventing an agent from
moving into the same square it's in."""
pass
class
Wall
(
Obstacle
):
pass
# ______________________________________________________________________________
# Continuous environment
class
ContinuousWorld
(
Environment
):
""" Model for Continuous World. """
def
__init__
(
self
,
width
=
10
,
height
=
10
):
super
(
ContinuousWorld
,
self
).
__init__
()
self
.
width
=
width
self
.
height
=
height
def
add_obstacle
(
self
,
coordinates
):
self
.
things
.
append
(
PolygonObstacle
(
coordinates
))
class
PolygonObstacle
(
Obstacle
):
def
__init__
(
self
,
coordinates
):
""" Coordinates is a list of tuples. """
super
(
PolygonObstacle
,
self
).
__init__
()
self
.
coordinates
=
coordinates
# ______________________________________________________________________________
# Vacuum environment
class
Dirt
(
Thing
):
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
thing_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
=
(
'Dirty'
if
self
.
some_things_at
(
agent
.
location
,
Dirt
)
else
'Clean'
)
bump
=
(
'Bump'
if
agent
.
bump
else
'None'
)
return
(
status
,
bump
)
def
execute_action
(
self
,
agent
,
action
):
if
action
==
'Suck'
:
dirt_list
=
self
.
list_things_at
(
agent
.
location
,
Dirt
)
if
dirt_list
!=
[]:
dirt
=
dirt_list
[
0
]
agent
.
performance
+=
100
self
.
delete_thing
(
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
thing_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
,
thing
):
"Agents start in either location at random."
return
random
.
choice
([
loc_A
,
loc_B
])
# ______________________________________________________________________________
# The Wumpus World
class
Gold
(
Thing
):
def
__eq__
(
self
,
rhs
):
'''All Gold are equal'''
return
rhs
.
__class__
==
Gold
pass
class
Bump
(
Thing
):
pass
class
Glitter
(
Thing
):
pass
class
Pit
(
Thing
):
pass
class
Breeze
(
Thing
):
pass
class
Arrow
(
Thing
):
pass
class
Scream
(
Thing
):
pass
class
Wumpus
(
Agent
):
screamed
=
False
pass
class
Stench
(
Thing
):
pass
class
Explorer
(
Agent
):
holding
=
[]
has_arrow
=
True
killed_by
=
""
direction
=
Direction
(
"right"
)
def
can_grab
(
self
,
thing
):
'''Explorer can only grab gold'''
return
thing
.
__class__
==
Gold
class
WumpusEnvironment
(
XYEnvironment
):
pit_probability
=
0.2
# Probability to spawn a pit in a location. (From Chapter 7.2)
# Room should be 4x4 grid of rooms. The extra 2 for walls
def
__init__
(
self
,
agent_program
,
width
=
6
,
height
=
6
):
super
(
WumpusEnvironment
,
self
).
__init__
(
width
,
height
)
self
.
init_world
(
agent_program
)
def
init_world
(
self
,
program
):
'''Spawn items to the world based on probabilities from the book'''
"WALLS"
self
.
add_walls
()
"PITS"
for
x
in
range
(
self
.
x_start
,
self
.
x_end
):
for
y
in
range
(
self
.
y_start
,
self
.
y_end
):
if
random
.
random
()
<
self
.
pit_probability
:
self
.
add_thing
(
Pit
(), (
x
,
y
),
True
)
self
.
add_thing
(
Breeze
(), (
x
-
1
,
y
),
True
)
self
.
add_thing
(
Breeze
(), (
x
,
y
-
1
),
True
)
self
.
add_thing
(
Breeze
(), (
x
+
1
,
y
),
True
)
self
.
add_thing
(
Breeze
(), (
x
,
y
+
1
),
True
)
"WUMPUS"
w_x
,
w_y
=
self
.
random_location_inbounds
(
exclude
=
(
1
,
1
))
self
.
add_thing
(
Wumpus
(
lambda
x
:
""
), (
w_x
,
w_y
),
True
)
self
.
add_thing
(
Stench
(), (
w_x
-
1
,
w_y
),
True
)
self
.
add_thing
(
Stench
(), (
w_x
+
1
,
w_y
),
True
)
self
.
add_thing
(
Stench
(), (
w_x
,
w_y
-
1
),
True
)
self
.
add_thing
(
Stench
(), (
w_x
,
w_y
+
1
),
True
)
"GOLD"
self
.
add_thing
(
Gold
(),
self
.
random_location_inbounds
(
exclude
=
(
1
,
1
)),
True
)
#self.add_thing(Gold(), (2,1), True) Making debugging a whole lot easier
"AGENT"
self
.
add_thing
(
Explorer
(
program
), (
1
,
1
),
True
)
def
get_world
(
self
,
show_walls
=
True
):
'''returns the items in the world'''
result
=
[]
x_start
,
y_start
=
(
0
,
0
)
if
show_walls
else
(
1
,
1
)
x_end
,
y_end
=
(
self
.
width
,
self
.
height
)
if
show_walls
else
(
self
.
width
-
1
,
self
.
height
-
1
)
for
x
in
range
(
x_start
,
x_end
):
row
=
[]
for
y
in
range
(
y_start
,
y_end
):
row
.
append
(
self
.
list_things_at
((
x
,
y
)))
result
.
append
(
row
)
return
result
def
percepts_from
(
self
,
agent
,
location
,
tclass
=
Thing
):
'''Returns percepts from a given location, and replaces some items with percepts from chapter 7.'''
thing_percepts
=
{
Gold
:
Glitter
(),
Wall
:
Bump
(),
Wumpus
:
Stench
(),
Pit
:
Breeze
()}
'''Agents don't need to get their percepts'''
thing_percepts
[
agent
.
__class__
]
=
None
'''Gold only glitters in its cell'''
if
location
!=
agent
.
location
:
thing_percepts
[
Gold
]
=
None
result
=
[
thing_percepts
.
get
(
thing
.
__class__
,
thing
)
for
thing
in
self
.
things
if
thing
.
location
==
location
and
isinstance
(
thing
,
tclass
)]
return
result
if
len
(
result
)
else
[
None
]
def
percept
(
self
,
agent
):
'''Returns things in adjacent (not diagonal) cells of the agent.
Result format: [Left, Right, Up, Down, Center / Current location]'''
x
,
y
=
agent
.
location
result
=
[]
result
.
append
(
self
.
percepts_from
(
agent
, (
x
-
1
,
y
)))
result
.
append
(
self
.
percepts_from
(
agent
, (
x
+
1
,
y
)))
result
.
append
(
self
.
percepts_from
(
agent
, (
x
,
y
-
1
)))
result
.
append
(
self
.
percepts_from
(
agent
, (
x
,
y
+
1
)))
result
.
append
(
self
.
percepts_from
(
agent
, (
x
,
y
)))
'''The wumpus gives out a a loud scream once it's killed.'''
wumpus
=
[
thing
for
thing
in
self
.
things
if
isinstance
(
thing
,
Wumpus
)]
if
len
(
wumpus
)
and
not
wumpus
[
0
].
alive
and
not
wumpus
[
0
].
screamed
:
result
[
-
1
].
append
(
Scream
())
wumpus
[
0
].
screamed
=
True
return
result
def
execute_action
(
self
,
agent
,
action
):
'''Modify the state of the environment based on the agent's actions
Performance score taken directly out of the book'''
if
isinstance
(
agent
,
Explorer
)
and
self
.
in_danger
(
agent
):
return
agent
.
bump
=
False
if
action
==
'TurnRight'
:
agent
.
direction
=
agent
.
direction
+
Direction
.
R
agent
.
performance
-=
1
elif
action
==
'TurnLeft'
:
agent
.
direction
=
agent
.
direction
+
Direction
.
L
agent
.
performance
-=
1
elif
action
==
'Forward'
:
agent
.
bump
=
self
.
move_to
(
agent
,
agent
.
direction
.
move_forward
(
agent
.
location
))
agent
.
performance
-=
1
elif
action
==
'Grab'
:
things
=
[
thing
for
thing
in
self
.
list_things_at
(
agent
.
location
)
if
agent
.
can_grab
(
thing
)]
if
len
(
things
):
print
(
"Grabbing"
,
things
[
0
].
__class__
.
__name__
)
if
len
(
things
):
agent
.
holding
.
append
(
things
[
0
])
agent
.
performance
-=
1
elif
action
==
'Climb'
:
if
agent
.
location
==
(
1
,
1
):
# Agent can only climb out of (1,1)
agent
.
performance
+=
1000
if
Gold
()
in
agent
.
holding
else
0
self
.
delete_thing
(
agent
)
elif
action
==
'Shoot'
:
'''The arrow travels straight down the path the agent is facing'''
if
agent
.
has_arrow
:
arrow_travel
=
agent
.
direction
.
move_forward
(
agent
.
location
)
while
(
self
.
is_inbounds
(
arrow_travel
)):
wumpus
=
[
thing
for
thing
in
self
.
list_things_at
(
arrow_travel
)
if
isinstance
(
thing
,
Wumpus
)]
if
len
(
wumpus
):
wumpus
[
0
].
alive
=
False
break
arrow_travel
=
agent
.
direction
.
move_forward
(
agent
.
location
)
agent
.
has_arrow
=
False
def
in_danger
(
self
,
agent
):
'''Checks if Explorer is in danger (Pit or Wumpus), if he is, kill him'''
for
thing
in
self
.
list_things_at
(
agent
.
location
):
if
isinstance
(
thing
,
Pit
)
or
(
isinstance
(
thing
,
Wumpus
)
and
thing
.
alive
):
agent
.
alive
=
False
agent
.
performance
-=
1000
agent
.
killed_by
=
thing
.
__class__
.
__name__
return
True
return
False
def
is_done
(
self
):
'''The game is over when the Explorer is killed
or if he climbs out of the cave only at (1,1)'''
explorer
=
[
agent
for
agent
in
self
.
agents
if
isinstance
(
agent
,
Explorer
) ]
if
len
(
explorer
):
if
explorer
[
0
].
alive
:
return
False
else
:
print
(
"Death by {} [-1000]."
.
format
(
explorer
[
0
].
killed_by
))
else
:
print
(
"Explorer climbed out {}."
.
format
(
"with Gold [+1000]!"
if
Gold
()
not
in
self
.
things
else
"without Gold [+0]"
))
return
True
#Almost done. Arrow needs to be implemented
# ______________________________________________________________________________
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_thing
(
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_thing(ModelBasedVacuumAgent())
>>> e.run(5)
"""
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