class TestAgents(unittest.TestCase):
def setUp(self):
# Create world
self.world = World()
# Create agents
self.tom = Agent('Tom')
self.world.addAgent(self.tom)
self.jerry = Agent('Jerry')
self.world.addAgent(self.jerry)
def addStates(self):
"""Create state features"""
self.world.defineState(self.tom.name,'health',int,lo=0,hi=100,
description='%s\'s wellbeing' % (self.tom.name))
self.world.setState(self.tom.name,'health',50)
self.world.defineState(self.jerry.name,'health',int,lo=0,hi=100,
description='%s\'s wellbeing' % (self.jerry.name))
self.world.setState(self.jerry.name,'health',50)
def addActions(self):
"""Create actions"""
self.chase = self.tom.addAction({'verb': 'chase','object': self.jerry.name})
self.hit = self.tom.addAction({'verb': 'hit','object': self.jerry.name})
self.run = self.jerry.addAction({'verb': 'run away'})
self.trick = self.jerry.addAction({'verb': 'trick','object': self.tom.name})
def addDynamics(self):
"""Create dynamics"""
tree = makeTree(incrementMatrix(stateKey(self.jerry.name,'health'),-10))
self.world.setDynamics(stateKey(self.jerry.name,'health'),self.hit,tree,enforceMin=True)
def addModels(self,rationality=1.):
self.tom.addModel('friend',rationality=rationality,parent=True)
self.tom.setReward(maximizeFeature(stateKey(self.jerry.name,'health')),1.,'friend')
self.tom.addModel('foe',rationality=rationality,parent=True)
self.tom.setReward(minimizeFeature(stateKey(self.jerry.name,'health')),1.,'foe')
def saveload(self):
"""Write scenario to file and then load from scratch"""
self.world.save('/tmp/psychsim_test.psy')
self.world = World('/tmp/psychsim_test.psy')
self.tom = self.world.agents[self.tom.name]
self.jerry = self.world.agents[self.jerry.name]
def testEnumeratedState(self):
self.addActions()
self.world.defineVariable(self.tom.name,ActionSet)
self.world.defineState(self.tom.name,'status',list,['dead','injured','healthy'])
self.world.setState(self.tom.name,'status','healthy')
goal = achieveFeatureValue(stateKey(self.tom.name,'status'),'healthy')
self.tom.setReward(goal,1.)
goal = achieveFeatureValue(stateKey(self.tom.name,'status'),'injured')
self.jerry.setReward(goal,1.)
self.saveload()
self.assertEqual(len(self.world.state[None]),1)
vector = self.world.state[None].domain()[0]
tVal = self.tom.reward(vector)
self.assertAlmostEqual(tVal,1.,8)
jVal = self.jerry.reward(vector)
self.assertAlmostEqual(jVal,0.,8)
for action in self.tom.actions:
encoding = self.world.value2float(self.tom.name,action)
self.assertEqual(action,self.world.float2value(self.tom.name,encoding))
def testBeliefModels(self):
self.addStates()
self.addActions()
self.addDynamics()
self.world.setOrder([self.tom.name])
self.tom.addModel('optimist')
self.tom.setBelief(stateKey(self.jerry.name,'health'),20,'optimist')
self.tom.addModel('pessimist')
self.world.setModel(self.jerry.name,True)
self.world.setMentalModel(self.jerry.name,self.tom.name,{'optimist': 0.5,'pessimist': 0.5})
actions = {self.tom.name: self.hit}
self.world.step(actions)
vector = self.world.state[None].domain()[0]
beliefs = self.jerry.getAttribute('beliefs',self.world.getModel(self.jerry.name,vector))
for belief in beliefs.domain():
model = self.world.getModel(self.tom.name,belief)
if self.tom.models[model].has_key('beliefs'):
nested = self.tom.models[model]['beliefs']
self.assertEqual(len(nested),1)
nested = nested.domain()[0]
self.assertEqual(len(nested),1)
self.assertAlmostEqual(nested[stateKey(self.jerry.name,'health')],10.,8)
def testObservation(self):
self.addStates()
self.addActions()
self.addDynamics()
self.world.setOrder([self.tom.name])
self.world.setModel(self.jerry.name,True)
key = stateKey(self.jerry.name,'health')
self.jerry.setBelief(key,Distribution({20: 0.5, 50: 0.5}))
tree = makeTree({'if': thresholdRow(key,40),
True: {'distribution': [(KeyedVector({CONSTANT: 50}),.8),
(KeyedVector({CONSTANT: 20}),.2)]},
False: {'distribution': [(KeyedVector({CONSTANT: 50}),.2),
(KeyedVector({CONSTANT: 20}),.8)]}})
self.jerry.defineObservation(key,tree)
actions = {self.tom.name: self.hit}
vector = self.world.state[None].domain()[0]
omegaDist = self.jerry.observe(vector,actions)
for omega in omegaDist.domain():
new = KeyedVector(vector)
model = self.jerry.index2model(self.jerry.stateEstimator(vector,new,omega))
beliefs = self.jerry.models[model]['beliefs']
if omega[key] > 30:
# We observed a high value, so we should have a stronger belief in the higher value
# which is now 40 after the hit
for belief in beliefs.domain():
if beliefs[belief] > 0.5:
self.assertAlmostEqual(belief[key],40,8)
else:
self.assertAlmostEqual(belief[key],10,8)
else:
# We observed a low value, so we should have a stronger belief in the lower value
# which is now 10 after the hit
for belief in beliefs.domain():
if beliefs[belief] < 0.5:
self.assertAlmostEqual(belief[key],40,8)
else:
self.assertAlmostEqual(belief[key],10,8)
def testUnobservedAction(self):
self.addStates()
self.addActions()
self.addDynamics()
self.addModels()
self.world.setOrder([self.tom.name])
self.world.setModel(self.jerry.name,True)
self.jerry.setBelief(stateKey(self.jerry.name,'health'),50)
self.world.setMentalModel(self.jerry.name,self.tom.name,{'friend': 0.5,'foe': 0.5})
tree = makeTree(True)
self.jerry.defineObservation(self.tom.name,tree,self.hit,domain=ActionSet)
tree = makeTree({'distribution': [(True,0.25),(False,0.75)]})
self.jerry.defineObservation(self.tom.name,tree,self.chase,domain=ActionSet)
vector = self.world.state[None].domain()[0]
self.saveload()
self.world.step({self.tom.name: self.hit})
vector = self.world.state[None].domain()[0]
def testRewardModels(self):
self.addStates()
self.addActions()
self.addDynamics()
self.addModels()
self.world.setOrder([self.tom.name])
# Add Jerry's model to the world (so that it gets updated)
self.world.setModel(self.jerry.name,True)
# Give Jerry uncertainty about Tom
self.world.setMentalModel(self.jerry.name,self.tom.name,{'friend': 0.5,'foe': 0.5})
self.saveload()
# Hitting should make Jerry think Tom is more of a foe
actions = {self.tom.name: self.hit}
self.world.step(actions)
vector = self.world.state[None].domain()[0]
belief01 = self.jerry.getAttribute('beliefs',self.world.getModel(self.jerry.name,vector))
key = modelKey(self.tom.name)
for belief in belief01.domain():
if self.tom.index2model(belief[key]) == 'foe':
prob01 = belief01[belief]
break
self.assertGreater(prob01,0.5)
# If we think of Tom as even more of an optimizer, then our update should be stronger
self.tom.setAttribute('rationality',10.,'foe')
self.tom.setAttribute('rationality',10.,'friend')
self.world.setMentalModel(self.jerry.name,self.tom.name,{'friend': 0.5,'foe': 0.5})
self.world.step(actions)
vector = self.world.state[None].domain()[0]
model = self.world.getModel(self.jerry.name,vector)
belief10 = self.jerry.getAttribute('beliefs',model)
key = modelKey(self.tom.name)
for belief in belief10.domain():
if self.tom.index2model(belief[key]) == 'foe':
prob10 = belief10[belief]
break
self.assertGreater(prob10,prob01)
# If we keep the same models, but get another observation, we should update even more
self.world.step(actions)
vector = self.world.state[None].domain()[0]
model = self.world.getModel(self.jerry.name,vector)
belief1010 = self.jerry.getAttribute('beliefs',model)
key = modelKey(self.tom.name)
for belief in belief1010.domain():
if self.tom.index2model(belief[key]) == 'foe':
prob1010 = belief1010[belief]
break
self.assertGreater(prob1010,prob10)
def testDynamics(self):
self.world.setOrder([self.tom.name])
self.addStates()
self.addActions()
self.addDynamics()
key = stateKey(self.jerry.name,'health')
self.assertEqual(len(self.world.state[None]),1)
vector = self.world.state[None].domain()[0]
self.assertTrue(vector.has_key(stateKey(self.tom.name,'health')))
self.assertTrue(vector.has_key(turnKey(self.tom.name)))
self.assertTrue(vector.has_key(key))
self.assertTrue(vector.has_key(CONSTANT))
self.assertEqual(len(vector),4)
self.assertEqual(vector[stateKey(self.tom.name,'health')],50)
self.assertEqual(vector[key],50)
outcome = self.world.step({self.tom.name: self.chase})
for i in range(7):
self.assertEqual(len(self.world.state[None]),1)
vector = self.world.state[None].domain()[0]
self.assertTrue(vector.has_key(stateKey(self.tom.name,'health')))
self.assertTrue(vector.has_key(turnKey(self.tom.name)))
self.assertTrue(vector.has_key(key))
self.assertTrue(vector.has_key(CONSTANT))
self.assertEqual(len(vector),4)
self.assertEqual(vector[stateKey(self.tom.name,'health')],50)
self.assertEqual(vector[key],max(50-10*i,0))
outcome = self.world.step({self.tom.name: self.hit})
self.saveload()
def testRewardOnOthers(self):
self.addStates()
self.addActions()
self.addDynamics()
self.world.setOrder([self.tom.name])
vector = self.world.state[None].domain()[0]
# Create Jerry's goals
goal = maximizeFeature(stateKey(self.jerry.name,'health'))
self.jerry.setReward(goal,1.)
jVal = -self.jerry.reward(vector)
# Create Tom's goals from scratch
minGoal = minimizeFeature(stateKey(self.jerry.name,'health'))
self.tom.setReward(minGoal,1.)
self.saveload()
tRawVal = self.tom.reward(vector)
self.assertAlmostEqual(jVal,tRawVal,8)
# Create Tom's goals as a function of Jerry's
self.tom.models[True]['R'].clear()
self.tom.setReward(self.jerry.name,-1.)
self.saveload()
tFuncVal = self.tom.reward(vector)
self.assertAlmostEqual(tRawVal,tFuncVal,8)
# Test effect of functional reward on value function
self.tom.setHorizon(1)
self.saveload()
vHit = self.tom.value(vector,self.hit)['V']
vChase = self.tom.value(vector,self.chase)['V']
self.assertAlmostEqual(vHit,vChase+.1,8)
def testReward(self):
self.addStates()
key = stateKey(self.jerry.name,'health')
goal = makeTree({'if': thresholdRow(key,5),
True: KeyedVector({key: -2}),
False: KeyedVector({key: -1})})
goal = goal.desymbolize(self.world.symbols)
self.jerry.setReward(goal,1.)
R = self.jerry.models[True]['R']
self.assertEqual(len(R),1)
newGoal = R.keys()[0]
self.assertEqual(newGoal,goal)
self.assertAlmostEqual(R[goal],1.,8)
self.jerry.setReward(goal,2.)
self.assertEqual(len(R),1)
self.assertEqual(R.keys()[0],goal)
self.assertAlmostEqual(R[goal],2.,8)
def testTurnDynamics(self):
self.addStates()
self.addActions()
self.world.setOrder([self.tom.name,self.jerry.name])
self.assertEqual(self.world.maxTurn,1)
self.saveload()
vector = self.world.state[None].domain()[0]
jTurn = turnKey(self.jerry.name)
tTurn = turnKey(self.tom.name)
self.assertEqual(self.world.next(),[self.tom.name])
self.assertEqual(vector[tTurn],0)
self.assertEqual(vector[jTurn],1)
self.world.step()
vector = self.world.state[None].domain()[0]
self.assertEqual(self.world.next(),[self.jerry.name])
self.assertEqual(vector[tTurn],1)
self.assertEqual(vector[jTurn],0)
self.world.step()
vector = self.world.state[None].domain()[0]
self.assertEqual(self.world.next(),[self.tom.name])
self.assertEqual(vector[tTurn],0)
self.assertEqual(vector[jTurn],1)
# Try some custom dynamics
self.world.setTurnDynamics(self.tom.name,self.hit,makeTree(noChangeMatrix(tTurn)))
self.world.setTurnDynamics(self.jerry.name,self.hit,makeTree(noChangeMatrix(tTurn)))
self.world.step()
vector = self.world.state[None].domain()[0]
self.assertEqual(self.world.next(),[self.tom.name])
self.assertEqual(vector[tTurn],0)
self.assertEqual(vector[jTurn],1)
self.world.step({self.tom.name: self.chase})
vector = self.world.state[None].domain()[0]
self.assertEqual(self.world.next(),[self.jerry.name])
self.assertEqual(vector[tTurn],1)
self.assertEqual(vector[jTurn],0)
def testStatic(self):
self.addStates()
self.addActions()
self.addDynamics()
self.addModels()
self.world.setModel(self.jerry.name,True)
self.world.setMentalModel(self.jerry.name,self.tom.name,{'friend': 0.5,'foe': 0.5})
self.world.setOrder([self.tom.name])
vector = self.world.state[None].domain()[0]
model = self.world.getModel(self.jerry.name,vector)
belief0 = self.jerry.models[model]['beliefs']
result = self.world.step({self.tom.name: self.hit})
vector = self.world.state[None].domain()[0]
model = self.world.getModel(self.jerry.name,vector)
belief1 = self.jerry.models[model]['beliefs']
key = modelKey(self.tom.name)
for vector in belief0.domain():
if self.tom.index2model(vector[key]) == 'friend':
self.assertGreater(belief0[vector],belief1[vector])
else:
self.assertGreater(belief1[vector],belief0[vector])
# Now with the static beliefs
self.jerry.setAttribute('static',True,model)
self.saveload()
self.world.step()
vector = self.world.state[None].domain()[0]
model = self.world.getModel(self.jerry.name,vector)
belief2 = self.jerry.models[model]['beliefs']
for vector in belief1.domain():
self.assertAlmostEqual(belief1[vector],belief2[vector],8)
# Movement dynamics
world.setDynamics(location,behaviors['leave']['action'],makeTree(setToConstantMatrix(location,'beyond')))
world.setDynamics(location,behaviors['return']['action'],makeTree(setToConstantMatrix(location,'Seattle')))
# Phase dynamics
world.setDynamics('phase',True,makeTree({'if': equalRow('phase','where'),
True: setToConstantMatrix('phase','how'),
False: setToConstantMatrix('phase','where')}))
# Decision-making parameters
resident.setAttribute('horizon',2)
resident.setAttribute('selection','distribution')
world.save('anthrax.psy')
# world.printState()
for tree,weight in resident.getAttribute('R').items():
print weight,tree
decision = Distribution()
for vector in world.state[None].domain():
world.printVector(vector)
result = resident.decide(vector,selection='distribution')
for action in result['action'].domain():
decision.addProb(action,world.state[None][vector]*result['action'][action])
print 'Choice:',action
outcome = world.stepFromState(vector,action)
world.printState(outcome['new'])
print decision
# Phase dynamics
world.setDynamics(
'phase', True,
makeTree({
'if': equalRow('phase', 'where'),
True: setToConstantMatrix('phase', 'how'),
False: setToConstantMatrix('phase', 'where')
}))
# Decision-making parameters
resident.setAttribute('horizon', 2)
resident.setAttribute('selection', 'distribution')
world.save('anthrax.psy')
# world.printState()
for tree, weight in resident.getAttribute('R').items():
print weight, tree
decision = Distribution()
for vector in world.state[None].domain():
world.printVector(vector)
result = resident.decide(vector, selection='distribution')
for action in result['action'].domain():
decision.addProb(
action, world.state[None][vector] * result['action'][action])
print 'Choice:', action
outcome = world.stepFromState(vector, action)
world.printState(outcome['new'])
print decision
# Phase dynamics
world.setDynamics(
'phase', True,
makeTree({
'if': equalRow('phase', 'where'),
True: setToConstantMatrix('phase', 'how'),
False: setToConstantMatrix('phase', 'where')
}))
# Decision-making parameters
resident.setAttribute('horizon', 2)
resident.setAttribute('selection', 'distribution')
world.save('anthrax.psy')
# world.printState()
for tree, weight in list(resident.getAttribute('R').items()):
print(weight, tree)
decision = Distribution()
for vector in world.state[None].domain():
world.printVector(vector)
result = resident.decide(vector, selection='distribution')
for action in result['action'].domain():
decision.addProb(
action, world.state[None][vector] * result['action'][action])
print('Choice:', action)
outcome = world.stepFromState(vector, action)
world.printState(outcome['new'])
print(decision)
class TestAgents(unittest.TestCase):
def setUp(self):
# Create world
self.world = World()
# Create agents
self.tom = Agent('Tom')
self.world.addAgent(self.tom)
self.jerry = Agent('Jerry')
self.world.addAgent(self.jerry)
def addStates(self):
"""Create state features"""
self.world.defineState(self.tom.name,'health',int,lo=0,hi=100,
description='%s\'s wellbeing' % (self.tom.name))
self.world.setState(self.tom.name,'health',50)
self.world.defineState(self.jerry.name,'health',int,lo=0,hi=100,
description='%s\'s wellbeing' % (self.jerry.name))
self.world.setState(self.jerry.name,'health',50)
def addActions(self):
"""Create actions"""
self.chase = self.tom.addAction({'verb': 'chase','object': self.jerry.name})
self.hit = self.tom.addAction({'verb': 'hit','object': self.jerry.name})
self.run = self.jerry.addAction({'verb': 'run away'})
self.trick = self.jerry.addAction({'verb': 'trick','object': self.tom.name})
def addDynamics(self):
"""Create dynamics"""
tree = makeTree(incrementMatrix(stateKey(self.jerry.name,'health'),-10))
self.world.setDynamics(stateKey(self.jerry.name,'health'),self.hit,tree,enforceMin=True)
def addModels(self,rationality=1.):
self.tom.addModel('friend',rationality=rationality,parent=True)
self.tom.setReward(maximizeFeature(stateKey(self.jerry.name,'health')),1.,'friend')
self.tom.addModel('foe',rationality=rationality,parent=True)
self.tom.setReward(minimizeFeature(stateKey(self.jerry.name,'health')),1.,'foe')
def saveload(self):
"""Write scenario to file and then load from scratch"""
self.world.save('/tmp/psychsim_test.psy')
self.world = World('/tmp/psychsim_test.psy')
self.tom = self.world.agents[self.tom.name]
self.jerry = self.world.agents[self.jerry.name]
def testEnumeratedState(self):
self.addActions()
self.world.defineVariable(self.tom.name,ActionSet)
self.world.defineState(self.tom.name,'status',list,['dead','injured','healthy'])
self.world.setState(self.tom.name,'status','healthy')
goal = achieveFeatureValue(stateKey(self.tom.name,'status'),'healthy')
self.tom.setReward(goal,1.)
goal = achieveFeatureValue(stateKey(self.tom.name,'status'),'injured')
self.jerry.setReward(goal,1.)
self.saveload()
self.assertEqual(len(self.world.state),1)
vector = self.world.state.domain()[0]
tVal = self.tom.reward(vector)
self.assertAlmostEqual(tVal,1.,8)
jVal = self.jerry.reward(vector)
self.assertAlmostEqual(jVal,0.,8)
for action in self.tom.actions:
encoding = self.world.value2float(self.tom.name,action)
self.assertEqual(action,self.world.float2value(self.tom.name,encoding))
def testBeliefModels(self):
self.addStates()
self.addActions()
self.addDynamics()
self.world.setOrder([self.tom.name])
self.tom.addModel('optimist')
self.tom.setBelief(stateKey(self.jerry.name,'health'),20,'optimist')
self.tom.addModel('pessimist')
self.world.setModel(self.jerry.name,True)
self.world.setMentalModel(self.jerry.name,self.tom.name,{'optimist': 0.5,'pessimist': 0.5})
actions = {self.tom.name: self.hit}
self.world.step(actions)
vector = self.world.state.domain()[0]
beliefs = self.jerry.getAttribute('beliefs',self.world.getModel(self.jerry.name,vector))
for belief in beliefs.domain():
model = self.world.getModel(self.tom.name,belief)
if self.tom.models[model].has_key('beliefs'):
nested = self.tom.models[model]['beliefs']
self.assertEqual(len(nested),1)
nested = nested.domain()[0]
self.assertEqual(len(nested),1)
self.assertAlmostEqual(nested[stateKey(self.jerry.name,'health')],10.,8)
def testObservation(self):
self.addStates()
self.addActions()
self.addDynamics()
self.world.setOrder([self.tom.name])
self.world.setModel(self.jerry.name,True)
key = stateKey(self.jerry.name,'health')
self.jerry.setBelief(key,Distribution({20: 0.5, 50: 0.5}))
tree = makeTree({'if': thresholdRow(key,40),
True: {'distribution': [(KeyedVector({CONSTANT: 50}),.8),
(KeyedVector({CONSTANT: 20}),.2)]},
False: {'distribution': [(KeyedVector({CONSTANT: 50}),.2),
(KeyedVector({CONSTANT: 20}),.8)]}})
self.jerry.defineObservation(key,tree)
actions = {self.tom.name: self.hit}
vector = self.world.state.domain()[0]
omegaDist = self.jerry.observe(vector,actions)
for omega in omegaDist.domain():
new = KeyedVector(vector)
model = self.jerry.index2model(self.jerry.stateEstimator(vector,new,omega))
beliefs = self.jerry.models[model]['beliefs']
if omega[key] > 30:
# We observed a high value, so we should have a stronger belief in the higher value
# which is now 40 after the hit
for belief in beliefs.domain():
if beliefs[belief] > 0.5:
self.assertAlmostEqual(belief[key],40,8)
else:
self.assertAlmostEqual(belief[key],10,8)
else:
# We observed a low value, so we should have a stronger belief in the lower value
# which is now 10 after the hit
for belief in beliefs.domain():
if beliefs[belief] < 0.5:
self.assertAlmostEqual(belief[key],40,8)
else:
self.assertAlmostEqual(belief[key],10,8)
def testUnobservedAction(self):
self.addStates()
self.addActions()
self.addDynamics()
self.addModels()
self.world.setOrder([self.tom.name])
self.world.setModel(self.jerry.name,True)
self.jerry.setBelief(stateKey(self.jerry.name,'health'),50)
self.world.setMentalModel(self.jerry.name,self.tom.name,{'friend': 0.5,'foe': 0.5})
tree = makeTree(True)
self.jerry.defineObservation(self.tom.name,tree,self.hit,domain=ActionSet)
tree = makeTree({'distribution': [(True,0.25),(False,0.75)]})
self.jerry.defineObservation(self.tom.name,tree,self.chase,domain=ActionSet)
vector = self.world.state.domain()[0]
self.saveload()
self.world.step({self.tom.name: self.hit})
vector = self.world.state.domain()[0]
def testRewardModels(self):
self.addStates()
self.addActions()
self.addDynamics()
self.addModels()
self.world.setOrder([self.tom.name])
# Add Jerry's model to the world (so that it gets updated)
self.world.setModel(self.jerry.name,True)
# Give Jerry uncertainty about Tom
self.world.setMentalModel(self.jerry.name,self.tom.name,{'friend': 0.5,'foe': 0.5})
self.saveload()
# Hitting should make Jerry think Tom is more of a foe
actions = {self.tom.name: self.hit}
self.world.step(actions)
vector = self.world.state.domain()[0]
belief01 = self.jerry.getAttribute('beliefs',self.world.getModel(self.jerry.name,vector))
key = modelKey(self.tom.name)
for belief in belief01.domain():
if self.tom.index2model(belief[key]) == 'foe':
prob01 = belief01[belief]
break
self.assertGreater(prob01,0.5)
# If we think of Tom as even more of an optimizer, then our update should be stronger
self.tom.setAttribute('rationality',10.,'foe')
self.tom.setAttribute('rationality',10.,'friend')
self.world.setMentalModel(self.jerry.name,self.tom.name,{'friend': 0.5,'foe': 0.5})
self.world.step(actions)
vector = self.world.state.domain()[0]
model = self.world.getModel(self.jerry.name,vector)
belief10 = self.jerry.getAttribute('beliefs',model)
key = modelKey(self.tom.name)
for belief in belief10.domain():
if self.tom.index2model(belief[key]) == 'foe':
prob10 = belief10[belief]
break
self.assertGreater(prob10,prob01)
# If we keep the same models, but get another observation, we should update even more
self.world.step(actions)
vector = self.world.state.domain()[0]
model = self.world.getModel(self.jerry.name,vector)
belief1010 = self.jerry.getAttribute('beliefs',model)
key = modelKey(self.tom.name)
for belief in belief1010.domain():
if self.tom.index2model(belief[key]) == 'foe':
prob1010 = belief1010[belief]
break
self.assertGreater(prob1010,prob10)
def testDynamics(self):
self.world.setOrder([self.tom.name])
self.addStates()
self.addActions()
self.addDynamics()
key = stateKey(self.jerry.name,'health')
self.assertEqual(len(self.world.state),1)
vector = self.world.state.domain()[0]
self.assertTrue(vector.has_key(stateKey(self.tom.name,'health')))
self.assertTrue(vector.has_key(turnKey(self.tom.name)))
self.assertTrue(vector.has_key(key))
self.assertTrue(vector.has_key(CONSTANT))
self.assertEqual(len(vector),4)
self.assertEqual(vector[stateKey(self.tom.name,'health')],50)
self.assertEqual(vector[key],50)
outcome = self.world.step({self.tom.name: self.chase})
for i in range(7):
self.assertEqual(len(self.world.state),1)
vector = self.world.state.domain()[0]
self.assertTrue(vector.has_key(stateKey(self.tom.name,'health')))
self.assertTrue(vector.has_key(turnKey(self.tom.name)))
self.assertTrue(vector.has_key(key))
self.assertTrue(vector.has_key(CONSTANT))
self.assertEqual(len(vector),4)
self.assertEqual(vector[stateKey(self.tom.name,'health')],50)
self.assertEqual(vector[key],max(50-10*i,0))
outcome = self.world.step({self.tom.name: self.hit})
self.saveload()
def testRewardOnOthers(self):
self.addStates()
self.addActions()
self.addDynamics()
self.world.setOrder([self.tom.name])
vector = self.world.state.domain()[0]
# Create Jerry's goals
goal = maximizeFeature(stateKey(self.jerry.name,'health'))
self.jerry.setReward(goal,1.)
jVal = -self.jerry.reward(vector)
# Create Tom's goals from scratch
minGoal = minimizeFeature(stateKey(self.jerry.name,'health'))
self.tom.setReward(minGoal,1.)
self.saveload()
tRawVal = self.tom.reward(vector)
self.assertAlmostEqual(jVal,tRawVal,8)
# Create Tom's goals as a function of Jerry's
self.tom.models[True]['R'].clear()
self.tom.setReward(self.jerry.name,-1.)
self.saveload()
tFuncVal = self.tom.reward(vector)
self.assertAlmostEqual(tRawVal,tFuncVal,8)
# Test effect of functional reward on value function
self.tom.setHorizon(1)
self.saveload()
vHit = self.tom.value(vector,self.hit)['V']
vChase = self.tom.value(vector,self.chase)['V']
self.assertAlmostEqual(vHit,vChase+.1,8)
def testReward(self):
self.addStates()
key = stateKey(self.jerry.name,'health')
goal = makeTree({'if': thresholdRow(key,5),
True: KeyedVector({key: -2}),
False: KeyedVector({key: -1})})
self.jerry.setReward(goal,1.)
R = self.jerry.models[True]['R']
self.assertEqual(len(R),1)
self.assertEqual(R.keys()[0],goal)
self.assertAlmostEqual(R[goal],1.,8)
self.jerry.setReward(goal,2.)
self.assertEqual(len(R),1)
self.assertEqual(R.keys()[0],goal)
self.assertAlmostEqual(R[goal],2.,8)
def testTurnDynamics(self):
self.addStates()
self.addActions()
self.world.setOrder([self.tom.name,self.jerry.name])
self.assertEqual(self.world.maxTurn,1)
self.saveload()
vector = self.world.state.domain()[0]
jTurn = turnKey(self.jerry.name)
tTurn = turnKey(self.tom.name)
self.assertEqual(self.world.next(),[self.tom.name])
self.assertEqual(vector[tTurn],0)
self.assertEqual(vector[jTurn],1)
self.world.step()
vector = self.world.state.domain()[0]
self.assertEqual(self.world.next(),[self.jerry.name])
self.assertEqual(vector[tTurn],1)
self.assertEqual(vector[jTurn],0)
self.world.step()
vector = self.world.state.domain()[0]
self.assertEqual(self.world.next(),[self.tom.name])
self.assertEqual(vector[tTurn],0)
self.assertEqual(vector[jTurn],1)
# Try some custom dynamics
self.world.setTurnDynamics(self.tom.name,self.hit,makeTree(noChangeMatrix(tTurn)))
self.world.setTurnDynamics(self.jerry.name,self.hit,makeTree(noChangeMatrix(tTurn)))
self.world.step()
vector = self.world.state.domain()[0]
self.assertEqual(self.world.next(),[self.tom.name])
self.assertEqual(vector[tTurn],0)
self.assertEqual(vector[jTurn],1)
self.world.step({self.tom.name: self.chase})
vector = self.world.state.domain()[0]
self.assertEqual(self.world.next(),[self.jerry.name])
self.assertEqual(vector[tTurn],1)
self.assertEqual(vector[jTurn],0)
def testStatic(self):
self.addStates()
self.addActions()
self.addDynamics()
self.addModels()
self.world.setModel(self.jerry.name,True)
self.world.setMentalModel(self.jerry.name,self.tom.name,{'friend': 0.5,'foe': 0.5})
self.world.setOrder([self.tom.name])
vector = self.world.state.domain()[0]
model = self.world.getModel(self.jerry.name,vector)
belief0 = self.jerry.models[model]['beliefs']
self.world.step()
vector = self.world.state.domain()[0]
model = self.world.getModel(self.jerry.name,vector)
belief1 = self.jerry.models[model]['beliefs']
key = modelKey(self.tom.name)
for vector in belief0.domain():
if self.tom.index2model(vector[key]) == 'friend':
self.assertGreater(belief0[vector],belief1[vector])
else:
self.assertGreater(belief1[vector],belief0[vector])
# Now with the static beliefs
self.jerry.setAttribute('static',True,model)
self.saveload()
self.world.step()
vector = self.world.state.domain()[0]
model = self.world.getModel(self.jerry.name,vector)
belief2 = self.jerry.models[model]['beliefs']
for vector in belief1.domain():
self.assertAlmostEqual(belief1[vector],belief2[vector],8)