def make_sa(mdp,
indic_func=ind_funcs._q_eps_approx_indicator,
state_class=State,
epsilon=0.0):
'''
Args:
mdp (MDP)
state_class (Class)
epsilon (float)
Summary:
Creates and saves a state abstraction.
'''
print " Making state abstraction... "
new_sa = StateAbstraction(phi={})
if isinstance(mdp, MDPDistribution):
new_sa = make_multitask_sa(mdp,
state_class=state_class,
indic_func=indic_func,
epsilon=epsilon)
else:
new_sa = make_singletask_sa(mdp,
state_class=state_class,
indic_func=indic_func,
epsilon=epsilon)
print " (final SA) Num abstract states:", new_sa.get_num_abstr_states()
return new_sa
def make_sa(mdp,
indic_func=ind_funcs._q_eps_approx_indicator,
state_class=State,
epsilon=0.0,
save=False,
track_act_opt_pr=False):
'''
Args:
mdp (MDP)
state_class (Class)
epsilon (float)
Summary:
Creates and saves a state abstraction.
'''
print " Making state abstraction... "
q_equiv_sa = StateAbstraction(phi={}, track_act_opt_pr=track_act_opt_pr)
if isinstance(mdp, MDPDistribution):
q_equiv_sa = make_multitask_sa(mdp,
state_class=state_class,
indic_func=indic_func,
epsilon=epsilon,
track_act_opt_pr=track_act_opt_pr)
else:
q_equiv_sa = make_singletask_sa(mdp,
state_class=state_class,
indic_func=indic_func,
epsilon=epsilon,
track_act_opt_pr=track_act_opt_pr)
if save:
save_sa(q_equiv_sa, str(mdp) + ".p")
return q_equiv_sa
def make_singletask_sa(mdp,
indic_func,
state_class,
epsilon=0.0,
aa_single_act=False,
prob_of_mdp=1.0,
track_act_opt_pr=False):
'''
Args:
mdp (MDP)
indic_func (S x S --> {0,1})
state_class (Class)
epsilon (float)
Returns:
(StateAbstraction)
'''
print "\tRunning VI...",
sys.stdout.flush()
# Run VI
if isinstance(mdp, MDPDistribution):
mdp = mdp.sample()
vi = ValueIteration(mdp)
iters, val = vi.run_vi()
print " done."
print "\tMaking state abstraction...",
sys.stdout.flush()
sa = StateAbstraction(phi={},
state_class=state_class,
track_act_opt_pr=track_act_opt_pr)
clusters = defaultdict(set)
num_states = len(vi.get_states())
actions = mdp.get_actions()
# Find state pairs that satisfy the condition.
for i, state_x in enumerate(vi.get_states()):
sys.stdout.flush()
clusters[state_x].add(state_x)
for state_y in vi.get_states()[i:]:
if not (state_x == state_y) and indic_func(
state_x, state_y, vi, actions, epsilon=epsilon):
clusters[state_x].add(state_y)
clusters[state_y].add(state_x)
print "making clusters...",
sys.stdout.flush()
# Build SA.
for i, state in enumerate(clusters.keys()):
new_cluster = clusters[state]
sa.make_cluster(new_cluster)
# Destroy old so we don't double up.
for s in clusters[state]:
if s in clusters.keys():
clusters.pop(s)
if aa_single_act:
# Put all optimal actions in a set associated with the ground state.
for ground_s in sa.get_ground_states():
a_star_set = set(vi.get_max_q_actions(ground_s))
sa.set_actions_state_opt_dict(ground_s, a_star_set, prob_of_mdp)
print " done."
print "\tGround States:", num_states
print "\tAbstract:", sa.get_num_abstr_states()
print
return sa