def test_Agent():
def constant_prog(percept):
return percept
agent = Agent(constant_prog)
result = agent.program(5)
assert result == 5
def test_TableDrivenAgent():
random.seed(10)
loc_A, loc_B = (0, 0), (1, 0)
# table defining all the possible states of the agent
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',
((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'
}
# create an program and then an object of the TableDrivenAgent
program = TableDrivenAgentProgram(table)
agent = Agent(program)
# create an object of TrivialVacuumEnvironment
environment = TrivialVacuumEnvironment()
# initializing some environment status
environment.status = {loc_A: 'Dirty', loc_B: 'Dirty'}
# add agent to the environment
environment.add_thing(agent, location=(1, 0))
# run the environment by single step everytime to check how environment evolves using TableDrivenAgentProgram
environment.run(steps=1)
assert environment.status == {(1, 0): 'Clean', (0, 0): 'Dirty'}
environment.run(steps=1)
assert environment.status == {(1, 0): 'Clean', (0, 0): 'Dirty'}
environment.run(steps=1)
assert environment.status == {(1, 0): 'Clean', (0, 0): 'Clean'}
def test_ModelBasedReflexAgentProgram():
class Rule:
def __init__(self, state, action):
self.__state = state
self.action = action
def matches(self, state):
return self.__state == state
loc_A = (0, 0)
loc_B = (1, 0)
# create rules for a two-state Vacuum Environment
rules = [
Rule((loc_A, "Dirty"), "Suck"),
Rule((loc_A, "Clean"), "Right"),
Rule((loc_B, "Dirty"), "Suck"),
Rule((loc_B, "Clean"), "Left")
]
def update_state(state, action, percept, transition_model, sensor_model):
return percept
# create a program and then an object of the ModelBasedReflexAgentProgram class
program = ModelBasedReflexAgentProgram(rules, update_state, None, None)
agent = Agent(program)
# create an object of TrivialVacuumEnvironment
environment = TrivialVacuumEnvironment()
# add agent to the environment
environment.add_thing(agent)
# run the environment
environment.run()
# check final status of the environment
assert environment.status == {(1, 0): 'Clean', (0, 0): 'Clean'}
def test_SimpleReflexAgentProgram():
class Rule:
def __init__(self, state, action):
self.__state = state
self.action = action
def matches(self, state):
return self.__state == state
loc_A = (0, 0)
loc_B = (1, 0)
# create rules for a two state Vacuum Environment
rules = [
Rule((loc_A, "Dirty"), "Suck"),
Rule((loc_A, "Clean"), "Right"),
Rule((loc_B, "Dirty"), "Suck"),
Rule((loc_B, "Clean"), "Left")
]
def interpret_input(state):
return state
# create a program and then an object of the SimpleReflexAgentProgram
program = SimpleReflexAgentProgram(rules, interpret_input)
agent = Agent(program)
# create an object of TrivialVacuumEnvironment
environment = TrivialVacuumEnvironment()
# add agent to the environment
environment.add_thing(agent)
# run the environment
environment.run()
# check final status of the environment
assert environment.status == {(1, 0): 'Clean', (0, 0): 'Clean'}
def test_RandomAgentProgram():
# create a list of all the actions a Vacuum cleaner can perform
list = ['Right', 'Left', 'Suck', 'NoOp']
# create a program and then an object of the RandomAgentProgram
program = RandomAgentProgram(list)
agent = Agent(program)
# create an object of TrivialVacuumEnvironment
environment = TrivialVacuumEnvironment()
# add agent to the environment
environment.add_thing(agent)
# run the environment
environment.run()
# check final status of the environment
assert environment.status == {(1, 0): 'Clean', (0, 0): 'Clean'}