def run():
trained_policy_env = "DartWalker2d-v1"
# trained_policy_env = "DartSnake7Link-v1"
# trained_policy_env = "DartHopper-v1"
trained_policy_num_timesteps = 5000000
policy_run_nums = [1]
policy_seeds = [4]
eval_seed = 4
eval_run_num = 4
aug_num_timesteps = 1500000
additional_note = "augment_neurons_threshold_and_ignore_dup_neurons_metric_param_0.5"
# trained_policy_num_timesteps = 2000000
# policy_run_nums = [0]
# policy_seeds = [0]
# eval_seed = 3
# eval_run_num = 3
# aug_num_timesteps=1500000
# additional_note = " (copy)"
# # additional_note = "fixed_filter_too_strict_and_made_all_zeros_and_plots"
for policy_run_num in policy_run_nums:
for policy_seed in policy_seeds:
result_dir = get_result_dir(trained_policy_env,
trained_policy_num_timesteps,
policy_run_num,
policy_seed,
eval_seed,
eval_run_num,
additional_note=additional_note)
plot(result_dir, aug_num_timesteps)
except Exception as e:
continue
if ":" not in old_top_num_to_include:
top_num_to_include = f"0:{old_top_num_to_include}"
new_name = label.replace(f"top_num_to_include_{old_top_num_to_include}", f"top_num_to_include_{top_num_to_include}")
new_names.append((label,new_name))
for label, new_name in new_names:
os.rename(src=f"{result_dir}/{label}", dst=f"{result_dir}/{new_name}")
if __name__ == "__main__":
trained_policy_env = "DartWalker2d-v1"
trained_policy_num_timesteps = 2000000
policy_run_nums = [0]
policy_seeds = [0]
eval_seed = 3
eval_run_num = 3
aug_num_timesteps = 1500000
additional_note = " (copy)"
for policy_run_num in policy_run_nums:
for policy_seed in policy_seeds:
result_dir = get_result_dir(trained_policy_env, trained_policy_num_timesteps, policy_run_num, policy_seed,
eval_seed, eval_run_num, additional_note=additional_note)
change_name(result_dir)
new_x = xy_sublist_sub[amin][0]
new_y = np.mean([xy_item[1][:min_len] for xy_item in xy_sublist_sub], axis=0)
all_subsample_means_xy.append((new_x, new_y))
xy_list.append(all_subsample_means_xy[0])
xy_list_detail.append(all_subsample_means_xy[1:])
if include_details:
return plot_curves(xy_list, new_labels, xaxis, task_name, xy_list_detail)
else:
return plot_curves(xy_list, new_labels, xaxis, task_name, None)
if __name__ == "__main__":
env = 'DartWalker2d_aug_input_current_trial-v1'
trained_policy_env = "DartWalker2d-v1"
trained_policy_num_timesteps = 2000000
policy_run_nums = [0]
policy_seeds = [0]
eval_seed = 3
eval_run_num = 3
aug_num_timesteps = 1500000
subsample_size=9
for policy_run_num in policy_run_nums:
for policy_seed in policy_seeds:
result_dir = get_result_dir(trained_policy_env, trained_policy_num_timesteps, policy_run_num, policy_seed,
eval_seed, eval_run_num)
plot(subsample_size, result_dir)
def main():
import multiprocessing as mp
policy_num_timesteps = 5000000
policy_env = "DartWalker2d-v1"
policy_seeds = [3]
policy_run_nums = [1]
eval_seeds = [4]
eval_run_nums = [4]
augment_seeds = range(30)
augment_run_nums = [0]
augment_num_timesteps = 1500000
network_sizes = [64]
additional_note = "check_these_working_Ms_against_those_other_envs"
policy_num_timesteps = 9000000
policy_envs = ["DartWalker2d-v1", "DartSnake7Link-v1", "DartHopper-v1"]
policy_seeds = [0]
policy_run_nums = [0]
eval_seeds = [3]
eval_run_nums = [3]
augment_seeds = range(30)
augment_run_nums = [0]
augment_num_timesteps = 1500000
network_sizes = [64]
additional_note = "reconfirm_check_these_working_Ms_against_those_other_envs"
# policy_num_timesteps = 5000000
# policy_env = "DartWalker2d-v1"
# policy_seeds = [4]
# policy_run_nums = [0]
#
# eval_seeds = [4]
# eval_run_nums = [4]
#
# augment_seeds = range(1)
# augment_run_nums = [0]
# augment_num_timesteps = 1500000
# top_num_to_includes = [slice(0,20)]
# network_sizes = [64]
# additional_note = "tee"
# policy_seeds = [3, 4]
# policy_run_nums = [0]
# policy_num_timesteps = 5000
# policy_env = "DartWalker2d-v1"
#
# eval_seeds = [4]
# eval_run_nums = [4]
#
# augment_seeds = [0]
# augment_run_nums = range(2)
# augment_num_timesteps = 5000
# top_num_to_includes = [slice(0,10)]
# network_sizes = [16]
# additional_note = "largebatchtestforotherruns"
test_or_train=False
# policy_num_timesteps = 2000000
# policy_env = "DartWalker2d-v1"
# policy_seeds = [0]
# policy_run_nums = [0]
#
# eval_seeds = [3]
# eval_run_nums = [3]
#
# augment_seeds = range(1)
# augment_run_nums = [0]
# augment_num_timesteps = 5000
# top_num_to_includes = [slice(0, 10), slice(0,0)]
# network_sizes = [64]
# additional_note = "non_linear"
with mp.Pool(mp.cpu_count()) as pool:
#============================================================
checks = [(slice(0,0), []),
(slice(0,10), [("M",6), ("M",7), ("M",8), ("M",12), ("M",16), ("M",20), ("M",24), ("M",25), ("M",26), ("COM", 1)]),
(slice(0,20), [("M",2),("M",3),("M",6),("M",7),("M",8),("M",11),("M",12),("M",15),("M",16),("M",20),("M",21),("M",22),("M",24),("M",25),("M",26),("M",30),("M",31),("M",40),("M",41), ("COM", 1)])]
for policy_env in policy_envs:
for policy_seed in policy_seeds:
for policy_run_num in policy_run_nums:
for eval_seed in eval_seeds:
for eval_run_num in eval_run_nums:
# if not test:
result_dir = get_result_dir(policy_env, policy_num_timesteps, policy_run_num,
policy_seed, eval_seed, eval_run_num, additional_note)
# else:
# result_dir = get_test_dir(policy_env, policy_num_timesteps, policy_run_num, policy_seed,
# eval_seed, eval_run_num, augment_seed, additional_note)
create_dir_if_not(result_dir)
for check in checks:
top_num_to_include, lagrangian_inds_to_include = check
run_experiment_args = [(augment_num_timesteps, top_num_to_include, augment_seed,
augment_run_num, network_size,
policy_env, policy_num_timesteps, policy_run_num, policy_seed, eval_seed,
eval_run_num, learning_rate, additional_note, result_dir,
lagrangian_inds_to_include)
for augment_seed in augment_seeds
for augment_run_num in augment_run_nums
for network_size in network_sizes
for learning_rate in
[64 / network_size * 3e-4]]
pool.starmap(run_experiment, run_experiment_args)
try:
plot(result_dir, augment_num_timesteps)
except Exception as e:
print(e)