@@ -131,7 +131,16 @@ def program(percept):
131131
132132
133133def RandomAgentProgram (actions ):
134- """An agent that chooses an action at random, ignoring all percepts."""
134+ """An agent that chooses an action at random, ignoring all percepts.
135+ >>> list = ['Right', 'Left', 'Suck', 'NoOp']
136+ >>> program = RandomAgentProgram(list)
137+ >>> agent = Agent(program)
138+ >>> environment = TrivialVacuumEnvironment()
139+ >>> environment.add_thing(agent)
140+ >>> environment.run()
141+ >>> environment.status == {(1, 0): 'Clean' , (0, 0): 'Clean'}
142+ True
143+ """
135144 return lambda percept : random .choice (actions )
136145
137146# ______________________________________________________________________________
@@ -171,7 +180,14 @@ def rule_match(state, rules):
171180
172181
173182def RandomVacuumAgent ():
174- """Randomly choose one of the actions from the vacuum environment."""
183+ """Randomly choose one of the actions from the vacuum environment.
184+ >>> agent = RandomVacuumAgent()
185+ >>> environment = TrivialVacuumEnvironment()
186+ >>> environment.add_thing(agent)
187+ >>> environment.run()
188+ >>> environment.status == {(1,0):'Clean' , (0,0) : 'Clean'}
189+ True
190+ """
175191 return Agent (RandomAgentProgram (['Right' , 'Left' , 'Suck' , 'NoOp' ]))
176192
177193
@@ -192,7 +208,14 @@ def TableDrivenVacuumAgent():
192208
193209
194210def ReflexVacuumAgent ():
195- """A reflex agent for the two-state vacuum environment. [Figure 2.8]"""
211+ """A reflex agent for the two-state vacuum environment. [Figure 2.8]
212+ >>> agent = ReflexVacuumAgent()
213+ >>> environment = TrivialVacuumEnvironment()
214+ >>> environment.add_thing(agent)
215+ >>> environment.run()
216+ >>> environment.status == {(1,0):'Clean' , (0,0) : 'Clean'}
217+ True
218+ """
196219 def program (percept ):
197220 location , status = percept
198221 if status == 'Dirty' :
@@ -205,7 +228,14 @@ def program(percept):
205228
206229
207230def ModelBasedVacuumAgent ():
208- """An agent that keeps track of what locations are clean or dirty."""
231+ """An agent that keeps track of what locations are clean or dirty.
232+ >>> agent = ModelBasedVacuumAgent()
233+ >>> environment = TrivialVacuumEnvironment()
234+ >>> environment.add_thing(agent)
235+ >>> environment.run()
236+ >>> environment.status == {(1,0):'Clean' , (0,0) : 'Clean'}
237+ True
238+ """
209239 model = {loc_A : None , loc_B : None }
210240
211241 def program (percept ):
@@ -342,6 +372,22 @@ def __init__(self, direction):
342372 self .direction = direction
343373
344374 def __add__ (self , heading ):
375+ """
376+ >>> d = Direction('right')
377+ >>> l1 = d.__add__(Direction.L)
378+ >>> l2 = d.__add__(Direction.R)
379+ >>> l1.direction
380+ 'up'
381+ >>> l2.direction
382+ 'down'
383+ >>> d = Direction('down')
384+ >>> l1 = d.__add__('right')
385+ >>> l2 = d.__add__('left')
386+ >>> l1.direction == Direction.L
387+ True
388+ >>> l2.direction == Direction.R
389+ True
390+ """
345391 if self .direction == self .R :
346392 return {
347393 self .R : Direction (self .D ),
@@ -364,6 +410,16 @@ def __add__(self, heading):
364410 }.get (heading , None )
365411
366412 def move_forward (self , from_location ):
413+ """
414+ >>> d = Direction('up')
415+ >>> l1 = d.move_forward((0, 0))
416+ >>> l1
417+ (0, -1)
418+ >>> d = Direction(Direction.R)
419+ >>> l1 = d.move_forward((0, 0))
420+ >>> l1
421+ (1, 0)
422+ """
367423 x , y = from_location
368424 if self .direction == self .R :
369425 return (x + 1 , y )
@@ -940,14 +996,30 @@ def compare_agents(EnvFactory, AgentFactories, n=10, steps=1000):
940996 """See how well each of several agents do in n instances of an environment.
941997 Pass in a factory (constructor) for environments, and several for agents.
942998 Create n instances of the environment, and run each agent in copies of
943- each one for steps. Return a list of (agent, average-score) tuples."""
999+ each one for steps. Return a list of (agent, average-score) tuples.
1000+ >>> environment = TrivialVacuumEnvironment
1001+ >>> agents = [ModelBasedVacuumAgent, ReflexVacuumAgent]
1002+ >>> result = compare_agents(environment, agents)
1003+ >>> performance_ModelBasedVacummAgent = result[0][1]
1004+ >>> performance_ReflexVacummAgent = result[1][1]
1005+ >>> performance_ReflexVacummAgent <= performance_ModelBasedVacummAgent
1006+ True
1007+ """
9441008 envs = [EnvFactory () for i in range (n )]
9451009 return [(A , test_agent (A , steps , copy .deepcopy (envs )))
9461010 for A in AgentFactories ]
9471011
9481012
9491013def test_agent (AgentFactory , steps , envs ):
950- """Return the mean score of running an agent in each of the envs, for steps"""
1014+ """Return the mean score of running an agent in each of the envs, for steps
1015+ >>> def constant_prog(percept):
1016+ ... return percept
1017+ ...
1018+ >>> agent = Agent(constant_prog)
1019+ >>> result = agent.program(5)
1020+ >>> result == 5
1021+ True
1022+ """
9511023 def score (env ):
9521024 agent = AgentFactory ()
9531025 env .add_thing (agent )
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