def testHeatSeekingDiscreteDeterministicPolicyOnChangableIntention(self, imaginedWeId, intentionPrior, state, groundTruthCentralControlAction):
getStateForPolicyGivenIntention = GetStateForPolicyGivenIntention(imaginedWeId)
policy = PolicyOnChangableIntention(self.perceptAction, intentionPrior, self.updateIntentionDistribution,
self.chooseIntention, getStateForPolicyGivenIntention, self.policyGivenIntention)
centralControlActionDist = policy(state)
action = maxFromDistribution(centralControlActionDist)
self.assertTrue(np.allclose(action, groundTruthCentralControlAction))
def sampleAction(state):
actionDist = mctsSelectAction(state)
action = maxFromDistribution(actionDist)
return action
def wolfTransit(state, action):
return transitionFunction(
state, [action, maxFromDistribution(stagPolicy(state))] + [
maxFromDistribution(rabbitPolicy(state))
for rabbitPolicy in rabbitPolicies
])
def testHeatSeekingDiscreteDeterministicPolicy(self, state, groundTruthAction):
heatSeekingPolicy = HeatSeekingDiscreteDeterministicPolicy(self.actionSpace, self.getPredatorPos, self.getPreyPos, computeAngleBetweenVectors)
action = maxFromDistribution(heatSeekingPolicy(state))
truthValue = np.allclose(action, groundTruthAction)
self.assertTrue(truthValue)
def testHeatSeekingContinuesDeterministicPolicy(self, state, actionMagnitude, groundTruthWolfAction):
heatSeekingPolicy = HeatSeekingContinuesDeterministicPolicy(self.getSheepXPos, self.getWolfXPos,
actionMagnitude)
action = maxFromDistribution(heatSeekingPolicy(state))
truthValue = np.allclose(action, groundTruthWolfAction)
self.assertTrue(truthValue)
def testStationaryAgentPolicy(self, state, groundTruthAction):
action = maxFromDistribution(stationaryAgentPolicy(state))
truthValue = np.array_equal(action, groundTruthAction)
self.assertTrue(truthValue)
def testMaximumFromDistribution(self, actionDist, groundTruthAction):
sampledAction = maxFromDistribution(actionDist)
self.assertEqual(sampledAction, groundTruthAction)
