# >>> code
if __name__ == '__main__':
# partially observable model
pomdp = cs.tigerPomdp()
# initial belief
prior = Distribution(set(pomdp.states.keys()), lambda el,dom: \
Distribution.restrictedUniformPdf(el,dom,\
#{pomdp.inv_states['tl'],pomdp.inv_states['tr']}))
{pomdp.inv_states['tl'],pomdp.inv_states['tr']}))
# generate belief MDP
bmdp = BeliefMarkovDecisionProcess(pomdp,10,prior)
# export to PRISM
e2p.mdp2sta(bmdp,'test.sta')
e2p.mdp2tra(bmdp,'test.tra')
e2p.mdp2lab(bmdp,bmdp.inv_states[prior],'test.lab')
# compute initial, accepting and refusing states
accept = []
refuse = []
for k,v in bmdp.states.iteritems():
if pomdp.inv_states['win'] in v.distr:
if v.distr[pomdp.inv_states['win']] > 0.85:
accept.append(k)
if pomdp.inv_states['lose'] in v.distr:
if v.distr[pomdp.inv_states['lose']] > 0.85:
refuse.append(k)
# export to DOT
if s2 not in pdf: pdf[s2] = belief.distr[s1] * prob * \ pomdp.observationFunction[s2][observation] else: pdf[s2] += belief.distr[s1] * prob * \ pomdp.observationFunction[s2][observation] Distribution.normalize(pdf) return Distribution(belief.support,\ lambda e,domain : Distribution.customPdf(e,pdf)) # >>> main test if __name__ == "__main__": pomdp = cs.tigerPomdp() prior = Distribution(pomdp.states,Distribution.uniformPdf) bmdp = BeliefMarkovDecisionProcess(pomdp,3,prior) print 'print bmdp:',bmdp import exportToPrism as exp exp.mdp2sta(bmdp,'test.sta') exp.mdp2tra(bmdp,'test.tra') # >>> authorship information __author__ = "Marco Tinacci" __copyright__ = "Copyright 2016" __credits__ = ["Marco Tinacci"] __license__ = "ASF" __version__ = "2.0" __maintainer__ = "Marco Tinacci" __email__ = "*****@*****.**" __status__ = "Production"