import random
import time
class Thing:
"""
This represents any physical object that can appear in an Environment.
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.")


class Agent(Thing):


"""
An Agent is a subclass of Thing """
def init(self, program=None):
self.alive = True
self.performance = 0
self.program = program
def can_grab(self, thing):
"""Return True if this agent can grab this thing. Override for appr
return False
In [8]:
def TableDrivenAgentProgram(table):
"""


This agent selects an action based on the percept sequence. It is pract
To customize it, provide as table a dictionary of all
{percept_sequence:action} pairs. """
percepts = []


def program(percept):
action = None
percepts.append(percept)
action = table.get(tuple(percepts))
return action
return program


loc_A, loc_B = (0,0), (1,0) # The two locations for the Vaccum cleaning


def TableDrivenVaccumAgent():
"""
Tabular approach towards Vaccum cleaning
"""


table = {
((loc_A, "Clean"),): "Right",
((loc_A, "Dirty"),): "Suck",
((loc_B, "Clean"),): "Left",
((loc_B, "Dirty"),): "Suck",
((loc_A, "Dirty"), (loc_A, "Clean")): "Right",
((loc_A, "Clean"), (loc_B, "Dirty")): "Suck",
((loc_B, "Clean"), (loc_A, "Dirty")): "Suck",
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((loc_B, "Dirty"), (loc_B, "Clean")): "Left",
((loc_A, "Dirty"), (loc_A, "Clean"), (loc_B, "Dirty")): "Suck",
((loc_B, "Dirty"), (loc_B, "Clean"), (loc_A, "Dirty")): "Suck",
}
return Agent(TableDrivenAgentProgram(table))


class Environment:
"""Abstract class representing an Environment. 'Real' Environment classe
percept: Define the percept that an agent sees. execute_action: Def
Also update the agent.performance slot.
The environment keeps a list of .things and .agents (which is a subset
Each thing has a .location slot, even though some environments may not
def init(self):
self.things = []
self.agents = []


def percept(self, agent):
"""Return the percept that the agent sees at this point. (Implement
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 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 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)
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 add_thing(self, thing, location=None):
"""Add a thing to the environment, setting its location. For conven
if not isinstance(thing, Thing):
thing = Agent(thing)
if thing in self.things:
print("Can't add the same thing twice")
else:
thing.location = (location if location is not None else self.de
self.things.append(thing)
if isinstance(thing, Agent):
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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.locat
print(" from list: {}".format([(thing, thing.location) for thing
if thing in self.agents:
self.agents.remove(thing)


class TrivialVaccumEnvironment(Environment):
"""This environment has two locations, A and B. Each can be clean or di
def init(self):
super().init()
#loc_A, loc_B = (0,0), (1,0) # The two locations for the Vaccum clea
self.status = {loc_A: random.choice(["Clean", "Dirty"]), loc_B: rand
def thing_classes(self):
return [TableDrivenVaccumAgent]
def percept(self, agent):
"""Returns the agent's location, and the location status (Dirty/Cle
return agent.location, self.status[agent.location]
def execute_action(self, agent, action):
"""Change agent's location and/or location's status; track performa
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):


return random.choice([loc_A, loc_B])


if name == "main":


agent = TableDrivenVaccumAgent()
environment = TrivialVaccumEnvironment()
#print(environment)
environment.add_thing(agent)
print(environment.status)
environment.run(steps=10)
print(environment.status)
print(agent.performance)