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_cvx_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="cvx",
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")
