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spcrl_pmm_mj_point.py
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spcrl_pmm_mj_point.py
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# A Performance-Based Start State Curriculum Framework for Reinforcement Learning
# Copyright (c) 2021 Robert Bosch GmbH
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
# IMPORT #
import os
import copy
import argparse
import random
import time
import numpy as np
import torch
from lib.trpo.trpo_mujoco_point_fine import TRPOTrainer
from lib.mujoco.spatial_gradient import update_reach_prob_map_fine
from lib.start_selection.select_start import start_state_sampling_probabilities_from_rp
def run_fun(seed_list,
num_inner_episodes,
num_outer_steps,
policy_hidden_sizes_list,
max_kl_val,
damp_val,
max_iter_val,
batch_size_val,
start_mode,
lr_arg,
cluster=False,
num_eval=10
):
# INITIALIZATION
start_time = time.time()
prefix = 'spcrl_pmm_mj_point'
suffix = '%s' % lr_arg
config_dict = {
'prefix': prefix,
'suffix': suffix,
'barcode': int(start_time),
'seed_list': seed_list,
'num_inner_episodes': num_inner_episodes,
'num_outer_steps': num_outer_steps,
'policy_hidden_sizes_list': policy_hidden_sizes_list,
'max_kl_val': max_kl_val,
'damp_val': damp_val,
'max_iter_val': max_iter_val,
'batch_size_val': batch_size_val,
'start_mode': start_mode
}
if cluster:
save_path = os.environ.get('SSD') + "/" + os.environ.get('EXP') + "/"
else:
save_path = ""
f = open(save_path + "config_" + prefix + "_" + str(config_dict['barcode']) + "_" + suffix + '.txt', 'w')
f.write(str(config_dict))
f.close()
print("CONFIG")
print(config_dict)
# LOOP OVER SEEDS #
for seed_val in seed_list:
# SEED #
torch.manual_seed(seed_val)
random.seed(seed_val)
np.random.seed(seed_val)
# INITIALIZATION #
myTRPO = TRPOTrainer(state_dim=12,
action_dim=2,
hidden_sizes=policy_hidden_sizes_list,
max_kl=max_kl_val,
damping=damp_val,
batch_size=batch_size_val,
inner_episodes=num_inner_episodes,
max_iter=max_iter_val
)
eps_tot = 0
acc_eps_list = [0]
acc_result_list_ust = [0]
start_save = []
rpm_est_save = []
rp_grad_map_save = []
print("\n")
print("SEED")
print(seed_val)
print("\n")
train_starts = [(43, 43), (43, 44), (44, 43), (44, 44)]
sampling_prob_list = [0.25, 0.25, 0.25, 0.25]
i = 0
while eps_tot < num_outer_steps:
i += 1
print(eps_tot)
print(not train_starts)
if not train_starts:
_, result, s_list, a_list, r_list, st_list = myTRPO.train(start_p_list=[],
goal_p=myTRPO.grid.goal,
sampling_probs=[],
sampling_mode=False)
else:
_, result, s_list, a_list, r_list, st_list = myTRPO.train(start_p_list=train_starts,
goal_p=myTRPO.grid.goal,
sampling_probs=sampling_prob_list,
sampling_mode=True)
start_save.append(st_list)
rpm_est = update_reach_prob_map_fine(reach_prob_map=myTRPO.grid.rp_map,
path_trajectories=s_list,
path_successes=r_list)
# print(rpm_est)
rpmest2save = copy.deepcopy(rpm_est)
rpm_est_save.append(rpmest2save)
train_starts, sampling_prob_list = start_state_sampling_probabilities_from_rp(
trpo=myTRPO,
start_candidates=myTRPO.grid.default_starts,
rpm=rpm_est,
step=eps_tot,
temp_param_mode='exp')
print("TRAIN STARTS")
print(train_starts)
print(len(train_starts))
print(sampling_prob_list)
print("RESULT")
print(result)
eps_tot += num_inner_episodes
if eps_tot % 5 == 0:
print("TEST UNIFORM")
test_starts = myTRPO.grid.sample_random_pos(number=num_eval)
print("TEST STARTS")
print(test_starts)
reach_prob_ust = 0
for start_state in test_starts:
_, result_ust, _, _, _ = myTRPO.test(start_p_list=start_state, goal_p=myTRPO.grid.goal)
reach_prob_ust += result_ust
# print(result_ust)
reach_prob_ust = reach_prob_ust / len(test_starts)
print("Iteration: %i" % i)
print("Time passed (in min): %.2f" % ((time.time() - start_time) / 60))
print("Reach Prob UST: %.2f" % reach_prob_ust)
acc_eps_list.append(eps_tot)
acc_result_list_ust.append(reach_prob_ust)
reach_prob_list_ust = acc_result_list_ust
# SAVE RESULTS #
episodes_np = np.array(acc_eps_list)
np.save(save_path + "episodes_" + prefix + "_" + str(seed_val) + "_" + suffix + "_" +
str(config_dict['barcode']), episodes_np)
results_ust_np = np.array(reach_prob_list_ust)
np.save(save_path + "results_ust_" + prefix + "_" + str(seed_val) + "_" + suffix + "_" +
str(config_dict['barcode']), results_ust_np)
st_np = np.array(start_save)
np.save(save_path + "st_" + prefix + "_" + str(seed_val) + "_" + suffix + "_" +
str(config_dict['barcode']), st_np)
rpm_est_np = np.array(rpm_est_save)
np.save(save_path + "rpm_est_" + prefix + "_" + str(seed_val) + "_" + suffix + "_" +
str(config_dict['barcode']), rpm_est_np)
rpgm_np = np.array(rp_grad_map_save)
np.save(save_path + "rpgm_" + prefix + "_" + str(seed_val) + "_" + suffix + "_" +
str(config_dict['barcode']), rpgm_np)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--seed", type=str, default="s1")
parser.add_argument("--cluster", type=bool, default=False)
args = parser.parse_args()
if args.seed == "s1":
seed_l = [1535719580]
elif args.seed == "s2":
seed_l = [1535720536]
elif args.seed == "s3":
seed_l = [1535721129]
elif args.seed == "s4":
seed_l = [1535721985]
elif args.seed == "s5":
seed_l = [1535723522]
elif args.seed == "s6":
seed_l = [1535724275]
elif args.seed == "s7":
seed_l = [1535726291]
elif args.seed == "s8":
seed_l = [1535954757]
elif args.seed == "s9":
seed_l = [1535957367]
elif args.seed == "s10":
seed_l = [1535953242]
elif args.seed == "s11":
seed_l = [148222]
elif args.seed == "s12":
seed_l = [386772]
elif args.seed == "s13":
seed_l = [99394]
elif args.seed == "s14":
seed_l = [199662]
elif args.seed == "s15":
seed_l = [179852]
elif args.seed == "s16":
seed_l = [106425]
elif args.seed == "s17":
seed_l = [289031]
elif args.seed == "s18":
seed_l = [231042]
elif args.seed == "s19":
seed_l = [386786]
elif args.seed == "s20":
seed_l = [914963]
elif args.seed == "s21":
seed_l = [976132]
elif args.seed == "s22":
seed_l = [387836]
elif args.seed == "s23":
seed_l = [82692]
elif args.seed == "s24":
seed_l = [556318]
elif args.seed == "s25":
seed_l = [590610]
elif args.seed == "s26":
seed_l = [162444]
elif args.seed == "s27":
seed_l = [747881]
elif args.seed == "s28":
seed_l = [460457]
elif args.seed == "s29":
seed_l = [311988]
elif args.seed == "s30":
seed_l = [215173]
elif args.seed == "s31":
seed_l = [635399]
elif args.seed == "s32":
seed_l = [178712]
elif args.seed == "s33":
seed_l = [750458]
elif args.seed == "s34":
seed_l = [306262]
elif args.seed == "s35":
seed_l = [488445]
elif args.seed == "ps1":
seed_l = [1535719580, 1535720536, 1535721129]
elif args.seed == "ps2":
seed_l = [1535721985, 1535723522, 1535724275]
elif args.seed == "ps3":
seed_l = [1535726291, 1535954757]
elif args.seed == "ps4":
seed_l = [1535957367, 1535953242]
elif args.seed == "p1":
seed_l = [1535719580, 1535720536, 1535721129, 1535721985, 1535723522]
elif args.seed == "p2":
seed_l = [1535724275, 1535726291, 1535954757, 1535957367, 1535953242]
elif args.seed == "p3":
seed_l = [148222, 386772, 99394, 199662, 179852]
elif args.seed == "p4":
seed_l = [106425, 289031, 231042, 386786, 914963]
elif args.seed == "p5":
seed_l = [976132, 387836, 82692, 556318, 590610]
elif args.seed == "p6":
seed_l = [162444, 747881, 460457, 311988, 215173]
elif args.seed == "p7":
seed_l = [635399, 178712, 750458, 306262, 488445]
elif args.seed == "all":
seed_l = [1535719580, 1535720536, 1535721129, 1535721985, 1535723522,
1535724275, 1535726291, 1535954757, 1535957367, 1535953242]
else:
seed_l = [0]
run_fun(seed_list=seed_l,
num_inner_episodes=5,
num_outer_steps=1500,
policy_hidden_sizes_list=[64, 64, 64],
max_kl_val=5e-4,
damp_val=5e-3,
max_iter_val=500,
batch_size_val=20000,
start_mode="uniform",
lr_arg="llr",
cluster=args.cluster
)