def experiment_overlapping(expert_feature = toy_problem_simple,apprentice_feature = toy_problem_simple,name = "simple_feature",iterations_per_run=60,steps=15,runs=20): direc = "results/aamas" #initial_states = [disc.quantityToState([0,0,1,2,2]),disc.quantityToState([0,0,3,4,1]),disc.quantityToState([0,1,2,2,2]),disc.quantityToState([0,0,3,2,1])] #test_states =[disc.quantityToState([0,0,2,2,1]),disc.quantityToState([0,0,2,4,2]),disc.quantityToState([0,0,3,1,3]),disc.quantityToState([0,0,3,2,1])] fn.make_dir(direc+"/"+name) results_array = [] disc = DiscModel(target = [4,4],boundaries = [4,4],feature = expert_feature) disc_a = DiscModel(target = [4,4],boundaries = [4,4],feature = apprentice_feature) expert2 = Model(disc,"obstacle2_reach", load_saved = False) expert1 = Model(disc,"avoid_reach", load_saved = True) test_states = np.random.randint(0,disc.tot_states,10) bad_states = np.random.randint(0,disc.tot_states,5) for i in range(runs): apprentice = Model(disc_a,"dual_reward", load_saved = True) initial_states = np.random.randint(0,disc.tot_states,5) results_failure = learn_from_failure(expert1,expert2,apprentice,iterations_per_run,steps,initial_states,test_states,failure = "L1",initial_bad_states = bad_states) if i ==0: apprentice.visualise_reward() apprentice = Model(disc_a,"dual_reward", load_saved = True) results_normal = learn_from_failure(expert1,expert2,apprentice,iterations_per_run,steps,initial_states,test_states,failure = "false",initial_bad_states = bad_states) if i ==0: apprentice.visualise_reward() apprentice = Model(disc_a,"dual_reward", load_saved = True) results_slow = learn_from_failure(expert1,expert2,apprentice,iterations_per_run,steps,initial_states,test_states,failure = "slow",initial_bad_states = bad_states) if i ==0: apprentice.visualise_reward() results_array.append([results_failure,results_normal,results_slow]) fn.pickle_saver(results_array,direc+"/"+name+".pkl")
def experiment_contrasting(expert_feature = toy_problem_simple,apprentice_feature = toy_problem_simple,name = "simple_feature",iterations_per_run=60,steps=15,runs=20): direc = "results/aamas" #initial_states = [disc.quantityToState([0,0,1,2,2]),disc.quantityToState([0,0,3,4,1]),disc.quantityToState([0,1,2,2,2]),disc.quantityToState([0,0,3,2,1])] #test_states =[disc.quantityToState([0,0,2,2,1]),disc.quantityToState([0,0,2,4,2]),disc.quantityToState([0,0,3,1,3]),disc.quantityToState([0,0,3,2,1])] fn.make_dir(direc+"/"+name) results_array = [] disc = DiscModel(target = [4,4],boundaries = [4,4],feature = expert_feature) disc_a = DiscModel(target = [4,4],boundaries = [4,4],feature = apprentice_feature) expert2 = Model(disc,"obstacle2", load_saved = False) expert1 = Model(disc,"avoid_reach", load_saved = True) test_states = np.random.randint(0,disc.tot_states,100) bad_states = np.random.randint(0,disc.tot_states,5) for i in range(runs): apprentice = Model(disc_a,"dual_reward", load_saved = True) initial_states = np.random.randint(0,disc.tot_states,10) results_failure = learn_from_failure(expert1,expert2,apprentice,iterations_per_run,steps,initial_states,test_states,failure = "L1",initial_bad_states = bad_states) if i ==0: apprentice.visualise_reward() apprentice = Model(disc_a,"uniform", load_saved = True) results_normal = learn_from_failure(expert1,expert2,apprentice,iterations_per_run,steps,initial_states,test_states,failure = "false",initial_bad_states = bad_states) if i ==0: apprentice.visualise_reward() apprentice = Model(disc_a,"dual_reward", load_saved = True) results_slow = learn_from_failure(expert1,expert2,apprentice,iterations_per_run,steps,initial_states,test_states,failure = "slow",initial_bad_states = bad_states) if i ==0: apprentice.visualise_reward() results_array.append([results_failure,results_normal,results_slow]) fn.pickle_saver(results_array,direc+"/"+name+".pkl")
def __init__(self,discretisation,reward_type,load_saved = False):
self.disc = discretisation#discretisation model
if load_saved == True:
self.feature_f = fn.pickle_loader("saved/feature_f")
self.transition = Transition()
self.transition = fn.pickle_loader("saved/transition_f")
else:
self.buildTransitionFunction(1)
self.buildFeatureFunction()
fn.pickle_saver(self.feature_f,"saved/feature_f")
fn.pickle_saver(self.transition,"saved/transition_f")
self.choose_reward_function(reward_type)
self.reward_f_initial = self.buildRewardFunction()
self.reward_f = self.buildRewardFunction()
def __init__(self,discretisation,reward_type,load_saved = False):
self.disc = discretisation#discretisation model
if load_saved == True:
self.feature_f = fn.pickle_loader("saved/feature_f")
self.transition = Transition()
self.transition = fn.pickle_loader("saved/transition_f")
else:
self.buildTransitionFunction(1)
self.buildFeatureFunction()
fn.pickle_saver(self.feature_f,"saved/feature_f")
fn.pickle_saver(self.transition,"saved/transition_f")
self.choose_reward_function(reward_type)
self.reward_f_initial = self.buildRewardFunction()
self.reward_f = self.buildRewardFunction()
def experiment_data_size(expert_feature = toy_problem_simple,apprentice_feature = toy_problem_simple,name = "simple_feature",iterations_per_run=50,steps=15,runs=6): direc = "results/aamas" #initial_states = [disc.quantityToState([0,0,1,2,2]),disc.quantityToState([0,0,3,4,1]),disc.quantityToState([0,1,2,2,2]),disc.quantityToState([0,0,3,2,1])] #test_states =[disc.quantityToState([0,0,2,2,1]),disc.quantityToState([0,0,2,4,2]),disc.quantityToState([0,0,3,1,3]),disc.quantityToState([0,0,3,2,1])] fn.make_dir(direc+"/"+name) results_array = [] disc = DiscModel(target = [4,4],boundaries = [4,4],feature = expert_feature) disc_a = DiscModel(target = [4,4],boundaries = [4,4],feature = apprentice_feature) training_sizes = [2,5,25,50,100] fail = np.zeros([len(training_sizes),runs]);normal = np.zeros([len(training_sizes),runs]);slow = np.zeros([len(training_sizes),runs]) if expert_feature != apprentice_feature: expert_2_test = Model(disc,"obstacle2", load_saved = False) expert_1_test = Model(disc,"avoid_reach", load_saved = True) expert2 = Model(disc_a,"obstacle2", load_saved = False) expert2.reward_f = expert_2_test.reward_f expert1 = Model(disc_a,"avoid_reach", load_saved = True) expert1.reward_f = expert_1_test.reward_f else: expert2 = Model(disc,"obstacle2", load_saved = False) expert1 = Model(disc,"avoid_reach", load_saved = True) test_states = np.random.randint(0,disc.tot_states,10) bad_states = np.random.randint(0,disc.tot_states,5) for enn,size in enumerate(training_sizes): print "SIZE=",size print "============================================================================" for n,i in enumerate(range(runs)): print "RUN",i apprentice = Model(disc_a,"dual_reward", load_saved = True) #initial_states = np.random.randint(0,disc.tot_states,5) initial_states = np.random.randint(0,disc.tot_states,size) results_failure = learn_from_failure(expert1,expert2,apprentice,iterations_per_run,steps,initial_states,test_states,failure = "L1",initial_bad_states = bad_states) fail[enn,i] = results_failure.e_on_e - results_failure.a_o_e[-1] apprentice = Model(disc_a,"uniform", load_saved = True) results_normal = learn_from_failure(expert1,expert2,apprentice,iterations_per_run,steps,initial_states,test_states,failure = "false",initial_bad_states = bad_states) normal[enn,i] = results_normal.e_on_e - results_normal.a_o_e[-1] apprentice = Model(disc_a,"dual_reward", load_saved = True) results_slow = learn_from_failure(expert1,expert2,apprentice,iterations_per_run,steps,initial_states,test_states,failure = "slow",initial_bad_states = bad_states) slow[enn,i] = results_slow.e_on_e - results_slow.a_o_e[-1] results_array.append([results_failure,results_normal,results_slow]) fn.pickle_saver((results_array,fail,normal,slow),direc+"/"+name+".pkl")
def experiment_data_size(expert_feature = toy_problem_simple,apprentice_feature = toy_problem_simple,name = "simple_feature",iterations_per_run=50,steps=15,runs=6): direc = "results/aamas" #initial_states = [disc.quantityToState([0,0,1,2,2]),disc.quantityToState([0,0,3,4,1]),disc.quantityToState([0,1,2,2,2]),disc.quantityToState([0,0,3,2,1])] #test_states =[disc.quantityToState([0,0,2,2,1]),disc.quantityToState([0,0,2,4,2]),disc.quantityToState([0,0,3,1,3]),disc.quantityToState([0,0,3,2,1])] fn.make_dir(direc+"/"+name) results_array = [] disc = DiscModel(target = [4,4],boundaries = [4,4],feature = expert_feature) disc_a = DiscModel(target = [4,4],boundaries = [4,4],feature = apprentice_feature) training_sizes = [2,5,25,50,100] fail = np.zeros([len(training_sizes),runs]);normal = np.zeros([len(training_sizes),runs]);slow = np.zeros([len(training_sizes),runs]) if expert_feature != apprentice_feature: expert_2_test = Model(disc,"obstacle2", load_saved = False) expert_1_test = Model(disc,"avoid_reach", load_saved = True) expert2 = Model(disc_a,"obstacle2", load_saved = False) expert2.reward_f = expert_2_test.reward_f expert1 = Model(disc_a,"avoid_reach", load_saved = True) expert1.reward_f = expert_1_test.reward_f else: expert2 = Model(disc,"obstacle2", load_saved = False) expert1 = Model(disc,"avoid_reach", load_saved = True) test_states = np.random.randint(0,disc.tot_states,10) bad_states = np.random.randint(0,disc.tot_states,5) for enn,size in enumerate(training_sizes): print "SIZE=",size print "============================================================================" for n,i in enumerate(range(runs)): print "RUN",i apprentice = Model(disc_a,"dual_reward", load_saved = True) #initial_states = np.random.randint(0,disc.tot_states,5) initial_states = np.random.randint(0,disc.tot_states,size) results_failure = learn_from_failure(expert1,expert2,apprentice,iterations_per_run,steps,initial_states,test_states,failure = "sign",initial_bad_states = bad_states) fail[enn,i] = results_failure.e_on_e - results_failure.a_o_e[-1] apprentice = Model(disc_a,"uniform", load_saved = True) results_normal = learn_from_failure(expert1,expert2,apprentice,iterations_per_run,steps,initial_states,test_states,failure = "false",initial_bad_states = bad_states) normal[enn,i] = results_normal.e_on_e - results_normal.a_o_e[-1] apprentice = Model(disc_a,"dual_reward", load_saved = True) results_slow = learn_from_failure(expert1,expert2,apprentice,iterations_per_run,steps,initial_states,test_states,failure = "L1",initial_bad_states = bad_states) slow[enn,i] = results_slow.e_on_e - results_slow.a_o_e[-1] results_array.append([results_failure,results_normal,results_slow]) fn.pickle_saver((results_array,fail,normal,slow),direc+"/"+name+".pkl")
