def call(self,req_type,params):
"""
Calls CLARK with different types of inputs and parameters.
"""
req_type = req_type.lower()
if req_type == 'pddl':
parser = pddlc.get_argument_parser()
call_func = pddlc.call_clark
elif req_type in ['crmpl','rmpyl']:
parser = rmpylc.get_argument_parser()
call_func = rmpylc.call_clark
elif req_type == 'ccpomdp':
parser = ccpomdpc.get_argument_parser()
call_func = ccpomdpc.call_clark
else:
print(req_type+' is an invalid request type.')
_print_clark_usage()
_add_common_parsing(parser) #Adds common arguments
try:
args = parser.parse_args(params.split())
success,output_dict = call_func(args)
#If requested, writes the policy in graphical SVG format.
if args.svg:
policy_file = args.output[:args.output.rfind('.')]
dot_policy = policy_to_dot(output_dict['explicit'],output_dict['policy'])
dot_policy.write(policy_file+'.svg',format='svg')
#Generates output tpn
output_dict['rmpyl'].to_ptpn(filename=args.output)
except:
success,output_dict = False,None
print('\n##### ERROR: could not process request\n\tType: %s\n\tParams: %s\n'%(req_type,params))
if self.debug:
import ipdb; ipdb.set_trace()
raise
return success,output_dict
init_tcs.append(TemporalConstraint(start=cc_fp.global_start_event,
end=cc_fp.global_end_event,
ctype='controllable',lb=0.0, ub=1000.0))
b0 = cc_fp.get_initial_belief(prior=prior,initial_site='start_point',goal_site='end_point',
init_tcs=init_tcs)
planner = RAOStar(cc_fp,node_name='id',cc=0.01,cc_type='overall',
terminal_prob=1.0,randomization=0.0,propagate_risk=True,
halt_on_violation=False,verbose=1)
#Searches for the optimal policy
policy,explicit,performance = planner.search(b0)
#Converts policy to graphical SVG format
dot_policy = policy_to_dot(explicit,policy)
dot_policy.write('flightgear_policy.svg',format='svg')
#Converts optimal exploration policy into an RMPyL program
exploration_policy = policy_to_rmpyl(explicit,policy)
#The flight policy has the additional actions of taking off and landing.
flight_policy = RMPyL(name='run()')
flight_policy *= flight_policy.sequence(Episode(action='(takeoff plane)'),
exploration_policy,
Episode(action='(land plane)'))
#Eliminates probabilistic choices from the policy, since Pike (in fact, the
#Lisp tpn package) cannot properly handle them.
for obs in flight_policy.observations:
if obs.type=='probabilistic':
# time_limit=5,drop_penalty=10.0,p_fail=0.1,verbose=0)
# Diagnostic demo, where the robot has to figure out if it is fit for the task
# With this setting of parameters, the robot tries an action a couple of times
# before bugging the human for help.
param_dict = {'p_fail_fit':0.2, #Prob. of failing a task, while being fit
'p_fail_unfit':0.999, #Prob. of failing a task, while not being fit
'p_fit_fit':1.0, #Prob. of remaining fit, if fit before
'p_fit_unfit':0.0, #Prob. becoming fit, if unfit before
'goal_drop_penalty':100.0, #Penalty for not achieving a goal
'robot_action_cost':1.0, #Cost of robot performing an action
'human_action_cost':5.0} #Cost of human performing an action
mitsu_model = DiagnosticMitsubishi(domain_file=dom_file,prob_file=prob_file,
time_limit=5,verbose=1,param_dict=param_dict)
b0 = mitsu_model.get_initial_belief()
planner = RAOStar(mitsu_model,node_name='id',cc=0.3,cc_type='overall',
terminal_prob=1.0,randomization=0.0,propagate_risk=True,
verbose=1,log=False)
policy,explicit,performance = planner.search(b0)
dot_policy = policy_to_dot(explicit,policy)
rmpyl_policy = policy_to_rmpyl(explicit,policy)
dot_policy.write('mitsubishi_policy.svg',format='svg')
rmpyl_policy.to_ptpn(filename='mitsubishi_policy_rmpyl_ptpn.tpn')
def search(self, b0, time_limit=np.infty, iter_limit=np.infty):
"""Searches for the optimal path from start to goal on the hypergraph."""
if self._verbose >= 1:
print('\n##### Starting RAO* search!\n')
self._start_time = time.time()
self._init_search(b0)
count = 0
root = self._explicit.root
#Initial objective at the root, which is the best possible (it can
#only degrade with an admissible heuristic).
prev_root_value = np.infty if self._model.is_maximization else -np.infty
if self._log: #Creates a log file, if necessary
filename = time.strftime("%d_%m_%Y_%H_%M_%S") + '_log_rao.txt'
if not os.path.exists('./log'):
os.makedirs('./log')
self._log_f = open('./log/' + filename, 'w')
print("\nLogging into " + filename)
if self._animation:
if not os.path.exists('./animation'):
os.makedirs('./animation')
print("\nCreated animaition directory")
interrupted = False
try:
while len(self._opennodes) > 0 and (count <= iter_limit) and (
time.time() - self._start_time <= time_limit):
count += 1
########### CORE FUNCTIONS
#Expands the current best solution
expanded_nodes = self._expand_best_partial_solution()
#Updates the value estimates and policy
self._update_values_and_best_actions(expanded_nodes)
#NOTE: the value inconsistency here was coming from nodes that
#were not contained in the best partial policy graph, but contained
#children in it.
#debug_all_hypegraph_values(self,'After value update')
#Updates the mapping of ancestors on the best policy graph and also
#the list of open nodes to be expanded.
self._update_policy_open_nodes()
#debug_policy_values(self,'Policy values after policy update')
#debug_all_hypegraph_values(self,'After policy update')
#######################################################
#Performance measures and info
root_value = root.value
self.performance['root_value_series'].append(root_value)
self.performance['root_exec_risk_series'].append(
root.exec_risk)
#Root node changed from its best value
if not np.isclose(root_value, prev_root_value):
#If the heuristic is really admissible, the root value can
#only degrade (decrease for maximization, or increase for
#minimization).
if self._is_better(root_value, prev_root_value):
print(
'WARNING: root value improved, which might indicate inadmissibility.'
)
else:
self.performance['time_to_best_value'] = time.time(
) - self._start_time
self.performance['iter_to_best_value'] = count
prev_root_value = root_value
if self._verbose >= 2:
print("Expanded nodes [%s]" %
(' '.join([str(n.name) for n in expanded_nodes])))
if self._verbose >= 1:
total_states = sum(
[len(node.state.belief) for node in expanded_nodes])
print(
"Iter: %d, Open nodes: %d, States evaluted: %d, Root value: %.4f, Root ER: %.4f"
% (count, len(self._opennodes), total_states,
root.value, root.exec_risk))
if self._animation: #If an animation should be generated
partial_policy = self._extract_policy(partial=True)
dot_policy = policy_to_dot(self._explicit, partial_policy)
dot_policy.write('./animation/%d_rao.svg' % (count),
format='svg')
except KeyboardInterrupt:
interrupted = True
print(
"\n\n***** EXECUTION TERMINATED BY USER AT ITERATION %d. *****"
% (count))
self.performance['total_elapsed_time'] = time.time() - self._start_time
self.performance['optimal_value'] = root.value
self.performance['exec_risk_for_optimal_value'] = root.exec_risk
if self._log: #Closes the log file, if one has been created
self._log_f.close()
print("\nClosed " + filename)
print("\nTotal elapsed time: %f s" %
(self.performance['total_elapsed_time']))
print("Time to optimal value: %f s" %
(self.performance['time_to_best_value']))
print("Iterations till optimal value: %d" %
(self.performance['iter_to_best_value']))
print("Number of expanded nodes: %d" %
(self.performance['expanded_nodes']))
print("Number of evaluated particles: %d" %
(self.performance['evaluated_particles']))
print("Optimal value: %f" % (self.performance['optimal_value']))
print("Execution risk for optimal value: %f" %
(self.performance['exec_risk_for_optimal_value']))
policy = self._extract_policy(partial=interrupted)
if len(policy) == 0:
print('\n##### Failed to find policy (it is empty)...\n')
elif self.performance['optimal_value'] == -float('inf'):
print(
'\n##### Failed to find policy (probably due to chance constraint violation)...\n'
)
else:
print('\n##### Policy found!\n')
return policy, self._explicit, self.performance
def search(self,b0,time_limit=np.infty,iter_limit=np.infty):
"""Searches for the optimal path from start to goal on the hypergraph."""
if self._verbose>=1:
print('\n##### Starting RAO* search!\n')
self._start_time = time.time()
self._init_search(b0)
count=0
root = self._explicit.root
#Initial objective at the root, which is the best possible (it can
#only degrade with an admissible heuristic).
prev_root_value = np.infty if self._model.is_maximization else -np.infty
if self._log: #Creates a log file, if necessary
filename = time.strftime("%d_%m_%Y_%H_%M_%S")+'_log_rao.txt'
if not os.path.exists('./log'):
os.makedirs('./log')
self._log_f=open('./log/'+filename,'w')
print("\nLogging into "+filename)
if self._animation:
if not os.path.exists('./animation'):
os.makedirs('./animation')
print("\nCreated animaition directory")
interrupted=False
try:
while len(self._opennodes)>0 and (count<=iter_limit) and (time.time()-self._start_time<=time_limit):
count+=1
########### CORE FUNCTIONS
#Expands the current best solution
expanded_nodes = self._expand_best_partial_solution()
#Updates the value estimates and policy
self._update_values_and_best_actions(expanded_nodes)
#NOTE: the value inconsistency here was coming from nodes that
#were not contained in the best partial policy graph, but contained
#children in it.
#debug_all_hypegraph_values(self,'After value update')
#Updates the mapping of ancestors on the best policy graph and also
#the list of open nodes to be expanded.
self._update_policy_open_nodes()
#debug_policy_values(self,'Policy values after policy update')
#debug_all_hypegraph_values(self,'After policy update')
#######################################################
#Performance measures and info
root_value = root.value
self.performance['root_value_series'].append(root_value)
self.performance['root_exec_risk_series'].append(root.exec_risk)
#Root node changed from its best value
if not np.isclose(root_value,prev_root_value):
#If the heuristic is really admissible, the root value can
#only degrade (decrease for maximization, or increase for
#minimization).
if self._is_better(root_value,prev_root_value):
print('WARNING: root value improved, which might indicate inadmissibility.')
else:
self.performance['time_to_best_value'] = time.time()-self._start_time
self.performance['iter_to_best_value'] = count
prev_root_value = root_value
if self._verbose>=2:
print("Expanded nodes [%s]"%(' '.join([str(n.name) for n in expanded_nodes])))
if self._verbose>=1:
total_states=sum([len(node.state.belief) for node in expanded_nodes])
print("Iter: %d, Open nodes: %d, States evaluted: %d, Root value: %.4f, Root ER: %.4f"%(count,
len(self._opennodes),
total_states,
root.value,
root.exec_risk))
if self._animation: #If an animation should be generated
partial_policy = self._extract_policy(partial=True)
dot_policy = policy_to_dot(self._explicit,partial_policy)
dot_policy.write('./animation/%d_rao.svg'%(count),format='svg')
except KeyboardInterrupt:
interrupted=True
print("\n\n***** EXECUTION TERMINATED BY USER AT ITERATION %d. *****"%(count))
self.performance['total_elapsed_time'] = time.time()-self._start_time
self.performance['optimal_value'] = root.value
self.performance['exec_risk_for_optimal_value'] = root.exec_risk
if self._log: #Closes the log file, if one has been created
self._log_f.close()
print("\nClosed "+filename)
print("\nTotal elapsed time: %f s"%(self.performance['total_elapsed_time']))
print("Time to optimal value: %f s"%(self.performance['time_to_best_value']))
print("Iterations till optimal value: %d"%(self.performance['iter_to_best_value']))
print("Number of expanded nodes: %d"%(self.performance['expanded_nodes']))
print("Number of evaluated particles: %d"%(self.performance['evaluated_particles']))
print("Optimal value: %f"%(self.performance['optimal_value']))
print("Execution risk for optimal value: %f"%(self.performance['exec_risk_for_optimal_value']))
policy = self._extract_policy(partial=interrupted)
if len(policy)==0:
print('\n##### Failed to find policy (it is empty)...\n')
elif self.performance['optimal_value']==-float('inf'):
print('\n##### Failed to find policy (probably due to chance constraint violation)...\n')
else:
print('\n##### Policy found!\n')
return policy, self._explicit, self.performance