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")
experiment_contrasting(name="contrasting",
steps=15,
iterations_per_run=40,
runs=2)
experiment_overlapping(name="overlapping",
steps=15,
iterations_per_run=40,
runs=2)
experiment_complementary(name="complementary",
steps=15,
iterations_per_run=40,
runs=2)
#experiment_cvx_contrasting(name = "cvx_contrasting",steps =15,iterations_per_run= 40,runs = 2)
# plot_results("cvx_contrasting","results/aamas",2)
plot_results("complementary", "results/aamas", 2)
plot_results("contrasting", "results/aamas", 2)
#plot_results("complementary","results/aamas",2)
#experiment_complementary(name = "complementary",steps =15,iterations_per_run=60 ,runs = 2)
#experiment_overlapping(name = "overlapping",steps =15,iterations_per_run= 60,runs = 2)
#plot_results("complementary","results/aamas",2)
plot_results("overlapping", "results/aamas", 2)
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")
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 = "false")
results_array.append([results_failure,results_normal,results_slow])
fn.pickle_saver(results_array,direc+"/"+name+".pkl")
#experiment_overlapping(name = "overlapping",steps =15,iterations_per_run= 40,runs = 1)
#experiment_complementary(name = "overlapping",steps =15,iterations_per_run= 40,runs = 1)
#experiment_constrasting(name = "contrasting",steps =15,iterations_per_run= 40,runs = 1)
#plot_results("cvx_contrasting","results/aamas",1)
#plot_results("complementary","results/aamas",2)
#plot_results("constrasting","results/aamas",2)
experiment_contrasting(name = "contrasting",steps =15,iterations_per_run= 60,runs = 2)
experiment_complementary(name = "complementary",steps =15,iterations_per_run=60 ,runs = 2)
experiment_overlapping(name = "overlapping",steps =15,iterations_per_run= 60,runs = 2)
plot_results("complementary","results/aamas",2)
plot_results("contrasting","results/aamas",2)
plot_results("overlapping","results/aamas",2)
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")
experiment_contrasting(name = "contrasting",steps =15,iterations_per_run= 40,runs = 2)
experiment_overlapping(name = "overlapping",steps =15,iterations_per_run= 40,runs = 2)
experiment_complementary(name = "complementary",steps =15,iterations_per_run= 40,runs = 2)
#experiment_cvx_contrasting(name = "cvx_contrasting",steps =15,iterations_per_run= 40,runs = 2)
plot_results("cvx_contrasting","results/aamas",2)
plot_results("complementary","results/aamas",2)
#plot_results("contrasting","results/aamas",2)
plot_results("complementary","results/aamas",2)
#experiment_complementary(name = "complementary",steps =15,iterations_per_run=60 ,runs = 2)
#xperiment_overlapping(name = "overlapping",steps =15,iterations_per_run= 60,runs = 2)
#plot_results("complementary","results/aamas",2)
#plot_results("overlapping","results/aamas",2)
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")
experiment_overlapping(name="overlapping", steps=15, iterations_per_run=40, runs=2)
experiment_complementary(name="complementary", steps=15, iterations_per_run=40, runs=2)
experiment_contrasting(name="contrasting", steps=15, iterations_per_run=40, runs=2)
experiment_cvx_contrasting(name="cvx_contrasting", steps=15, iterations_per_run=40, runs=2)
plot_results("cvx_contrasting", "results/aamas", 2)
plot_results("complementary", "results/aamas", 2)
plot_results("contrasting", "results/aamas", 2)
plot_results("complementary", "results/aamas", 2)
# experiment_complementary(name = "complementary",steps =15,iterations_per_run=60 ,runs = 2)
# xperiment_overlapping(name = "overlapping",steps =15,iterations_per_run= 60,runs = 2)
# plot_results("complementary","results/aamas",2)
# plot_results("overlapping","results/aamas",2)
