model_representation_fluent = "del_" + "_".join(
[eff.literal.predicate] + [action.name] + ["var" + str(action_params.index(eff.literal.args[i]) + 1) for i in
range(len(eff.literal.args))])
learning_task.init.append(pddl.conditions.Atom(model_representation_fluent, []))
model_size += 1
### Write the learning task domain and problem to pddl
fdomain = open("learning_domain.pddl", "w")
fdomain.write(fdtask_to_pddl.format_domain(learning_task, domain_pddl))
fdomain.close()
fdomain = open("learning_problem.pddl", "w")
fdomain.write(fdtask_to_pddl.format_problem(learning_task, domain_pddl))
fdomain.close()
### Solvie the learning task
# starting_horizon = str(2*TOTAL_STEPS + 3)
validation_steps = max(states_seen-1, total_actions_seen)*2 + 1
if action_observability == 1 and state_observability == 0:
validation_steps = states_seen + total_actions_seen
starting_horizon = str(validation_steps + 2)
if state_observability==1 or action_observability==1:
ending_horizon = " -T " + starting_horizon
else:
ending_horizon = ""
plan_type = ""
if i<len(plans)-1:
for j in range(0,len(plans[i+1])):
name = "plan-"+plans[i+1][j].replace("(","").replace(")","").split(" ")[0]
pars = ["i"+str(j+1)]+plans[i+1][j].replace("(","").replace(")","").split(" ")[1:]
eff = eff + [pddl.effects.Effect([],pddl.conditions.Truth(),pddl.conditions.Atom(name,pars))]
fd_task.actions.append(pddl.actions.Action("test_"+str(i+1),[],0,pddl.conditions.Conjunction(pre),eff,0))
# Writing the compilation output domain and problem
fdomain=open("aux_domain.pddl","w")
fdomain.write(fdtask_to_pddl.format_domain(fd_task,fd_domain))
fdomain.close()
fdomain=open("aux_problem.pddl","w")
fdomain.write(fdtask_to_pddl.format_problem(fd_task,fd_domain))
fdomain.close()
# Solving the compilation
cmd = "rm " + config.OUTPUT_FILENAME + " planner_out.log;"+config.PLANNER_PATH+"/M aux_domain.pddl aux_problem.pddl -F "+str(len(plans)+sum([len(p) for p in plans]))+" "+config.PLANNER_PARAMS+" > planner_out.log"
print("\n\nExecuting... " + cmd)
os.system(cmd)
# Reading the plan output by the compilation
pres = [[] for _ in xrange(len(actions))]
dels = [[] for _ in xrange(len(actions))]
adds = [[] for _ in xrange(len(actions))]
file = open(config.OUTPUT_FILENAME, 'r')
for line in file:
keys="(program_pre_"