def main():
tbx_writer = SummaryWriter(comment="alpha_" + str(args.log_alpha))
env = gym.make("LunarLanderContinuous-v2")
replay_buffer = ReplayBuffer(args.replay_buffer_size)
feature_extractor = IdentityFeatureExtractor()
soft_ac = SoftActorCritic(
env,
replay_buffer,
args.max_episode_length,
feature_extractor,
buffer_sample_size=args.replay_buffer_sample_size,
tbx_writer=tbx_writer,
tau=0.005,
log_alpha=args.log_alpha,
entropy_tuning=True,
entropy_target=args.entropy_target,
render=args.render,
)
soft_ac.train(
args.training_steps, args.play_interval, halt_at_end=args.halt_at_end
)
soft_ac.policy.save("./lunar_lander_policy.to")
def main():
tbx_writer = SummaryWriter(comment='_alpha_' + str(args.log_alpha))
env = EwapGridworld(ped_id=1, render=args.render, vision_radius=40)
state_size = env.reset().shape[0]
map_side = (1 + env.vision_radius * 2)
conv_q_net = ConvQNet(state_size,
env.action_space.n,
256,
map_side,
kernel_shape=(3, 3))
conv_policy_net = ConvPiNet(state_size,
env.action_space.n,
256,
map_side,
kernel_shape=(3, 3))
replay_buffer = ReplayBuffer(args.replay_buffer_size)
soft_ac = SoftActorCritic(
env,
replay_buffer=replay_buffer,
buffer_sample_size=args.replay_buffer_sample_size,
tbx_writer=tbx_writer,
tau=0.005,
log_alpha=args.log_alpha,
entropy_tuning=True,
entropy_target=args.entropy_target,
q_net=conv_q_net,
policy_net=conv_policy_net)
soft_ac.train(args.max_episodes, args.play_interval)
def main(args):
root = pathlib.Path(args.root_folder)
run_folders = list(root.glob("*"))
for run_folder in tqdm(run_folders):
policy_dir = run_folder / "policy"
saved_policies = policy_dir.glob("*")
if not policy_dir.exists():
continue
latest_policy = max(
saved_policies,
key=lambda x: int(str(x).split("/")[-1].split(".")[0]),
)
env = UCYWorld()
fe = load_feature_extractor("GoalConditionedFahad")
rl_method = QSAC(env, ReplayBuffer(1000), fe)
rl_method.policy.load(str(latest_policy.resolve()))
metric_applicator = metric_utils.LTHMP2020()
results = metric_utils.collect_trajectories_and_metrics(
env, fe, rl_method.policy, 430, 1000, metric_applicator)
pd_metrics = pd.DataFrame(results).T
pd_metrics = pd_metrics.applymap(lambda x: x[0])
pd_metrics.to_pickle(str((run_folder / "metrics.pkl").resolve()))
def main():
tbx_writer = SummaryWriter(comment='alpha_' + str(args.log_alpha))
env = gym.make('Pendulum-v0')
replay_buffer = ReplayBuffer(args.replay_buffer_size)
feature_extractor = IdentityFeatureExtractor()
soft_ac = SoftActorCritic(
env,
replay_buffer,
feature_extractor,
buffer_sample_size=args.replay_buffer_sample_size,
tbx_writer=tbx_writer,
tau=0.005,
log_alpha=args.log_alpha,
entropy_tuning=True,
entropy_target=args.entropy_target,
render=args.render,
play_interval=args.play_interval,
)
soft_ac.train(args.training_steps, args.max_episode_length)
soft_ac.policy.save("./pendulum_policies")
def main():
"""Runs experiment"""
args = parser.parse_args()
utils.seed_all(args.seed)
ts = time.time()
st = datetime.datetime.fromtimestamp(ts).strftime("%Y-%m-%d_%H:%M:%S")
to_save = pathlib.Path(args.save_dir)
dir_name = args.save_folder + "_" + st
to_save = to_save / dir_name
to_save = str(to_save.resolve())
log_file = "Experiment_info.txt"
experiment_logger = Logger(to_save, log_file)
experiment_logger.log_header("Arguments for the experiment :")
experiment_logger.log_info(vars(args))
feat_ext = fe_utils.load_feature_extractor(args.feat_extractor, obs_width=args.pedestrian_width, agent_width=args.pedestrian_width)
experiment_logger.log_header("Parameters of the feature extractor :")
experiment_logger.log_info(feat_ext.__dict__)
env = GridWorld(
display=args.render,
is_random=False,
rows=576,
cols=720,
agent_width=args.pedestrian_width,
step_size=2,
obs_width=args.pedestrian_width,
width=10,
subject=args.subject,
annotation_file=args.annotation_file,
goal_state=None,
step_wrapper=utils.step_wrapper,
seed=args.seed,
replace_subject=args.replace_subject,
segment_size=args.segment_size,
external_control=True,
continuous_action=False,
reset_wrapper=utils.reset_wrapper,
consider_heading=True,
is_onehot=False,
show_orientation=True,
show_comparison=True,
show_trail=True,
)
experiment_logger.log_header("Environment details :")
experiment_logger.log_info(env.__dict__)
if args.rl_method == "ActorCritic":
rl_method = ActorCritic(
env,
feat_extractor=feat_ext,
gamma=1,
log_interval=args.rl_log_intervals,
max_episode_length=args.rl_ep_length,
hidden_dims=args.policy_net_hidden_dims,
save_folder=to_save,
lr=args.lr_rl,
max_episodes=args.rl_episodes,
)
if args.rl_method == "SAC":
if not env.continuous_action:
print("The action space needs to be continuous for SAC to work.")
exit()
replay_buffer = ReplayBuffer(args.replay_buffer_size)
rl_method = SoftActorCritic(
env,
replay_buffer,
feat_ext,
play_interval=500,
learning_rate=args.lr_rl,
buffer_sample_size=args.replay_buffer_sample_size,
)
if args.rl_method == "discrete_QSAC":
if not isinstance(env.action_space, gym.spaces.Discrete):
print("discrete SAC requires a discrete action space to work.")
exit()
replay_buffer = ReplayBuffer(args.replay_buffer_size)
rl_method = QSAC(
env,
replay_buffer,
feat_ext,
args.replay_buffer_sample_size,
learning_rate=args.lr_rl,
entropy_tuning=True,
entropy_target=args.entropy_target,
play_interval=args.play_interval,
tau=args.tau,
gamma=args.gamma,
)
if args.rl_method == "discrete_SAC":
if not isinstance(env.action_space, gym.spaces.Discrete):
print("discrete SAC requires a discrete action space to work.")
exit()
replay_buffer = ReplayBuffer(args.replay_buffer_size)
rl_method = DiscreteSAC(
env,
replay_buffer,
feat_ext,
args.replay_buffer_sample_size,
learning_rate=args.lr_rl,
entropy_tuning=True,
entropy_target=args.entropy_target,
play_interval=args.play_interval,
tau=args.tau,
gamma=args.gamma,
)
print("RL method initialized.")
print(rl_method.policy)
if args.policy_path is not None:
rl_method.policy.load(args.policy_path)
experiment_logger.log_header("Details of the RL method :")
experiment_logger.log_info(rl_method.__dict__)
expert_trajectories = read_expert_trajectories(args.exp_trajectory_path)
irl_method = PerTrajGCL(
rl=rl_method,
env=env,
expert_trajectories=expert_trajectories,
learning_rate=args.lr_irl,
l2_regularization=args.regularizer,
save_folder=to_save,
saving_interval=args.saving_interval,
)
print("IRL method intialized.")
print(irl_method.reward_net)
experiment_logger.log_header("Details of the IRL method :")
experiment_logger.log_info(irl_method.__dict__)
irl_method.pre_train(
args.pre_train_iterations,
args.num_expert_samples,
account_for_terminal_state=args.account_for_terminal_state,
gamma=args.gamma,
)
rl_method.train(
args.pre_train_rl_iterations,
args.rl_ep_length,
reward_network=irl_method.reward_net,
)
# save intermediate RL result
rl_method.policy.save(to_save + "/policy")
irl_method.train(
args.irl_iterations,
args.rl_episodes,
args.rl_ep_length,
args.rl_ep_length,
reset_training=args.reset_training,
account_for_terminal_state=args.account_for_terminal_state,
gamma=args.gamma,
stochastic_sampling=args.stochastic_sampling,
num_expert_samples=args.num_expert_samples,
num_policy_samples=args.num_policy_samples,
)
metric_applicator = metric_utils.LTHMP2020()
metric_results = metric_utils.collect_trajectories_and_metrics(
env,
feat_ext,
rl_method.policy,
len(expert_trajectories),
args.rl_ep_length,
metric_applicator,
disregard_collisions=True,
)
pd_metrics = pd.DataFrame(metric_results).T
pd_metrics = pd_metrics.applymap(lambda x: x[0])
pd_metrics.to_pickle(to_save + "/metrics.pkl")
with open(to_save + "/rl_data.csv", "a") as f:
rl_method.data_table.write_csv(f)
with open(to_save + "/irl_data.csv", "a") as f:
irl_method.data_table.write_csv(f)
with open(to_save + "/pre_irl_data.csv", "a") as f:
irl_method.pre_data_table.write_csv(f)
def main():
args = parser.parse_args()
if args.on_server:
# matplotlib without monitor
matplotlib.use('Agg')
# pygame without monitor
os.environ['SDL_VIDEODRIVER'] = 'dummy'
#####for the logger
ts = time.time()
st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d_%H:%M:%S')
###################
if not args.save_folder:
print('Provide save folder.')
exit()
policy_net_dims = '-policy_net-'
for dim in args.policy_net_hidden_dims:
policy_net_dims += str(dim)
policy_net_dims += '-'
reward_net_dims = '-reward_net-'
for dim in args.reward_net_hidden_dims:
reward_net_dims += str(dim)
reward_net_dims += '-'
parent_dir = './results/' + str(
args.save_folder) + st + policy_net_dims + reward_net_dims
to_save = './results/'+str(args.save_folder)+st+policy_net_dims + reward_net_dims + \
'-reg-'+str(args.regularizer)+ \
'-seed-'+str(args.seed)+'-lr-'+str(args.lr_irl)
log_file = 'Experiment_info.txt'
experiment_logger = Logger(to_save, log_file)
experiment_logger.log_header('Arguments for the experiment :')
experiment_logger.log_info(vars(args))
#from rlmethods.rlutils import LossBasedTermination
#for rl
from rlmethods.b_actor_critic import ActorCritic
from rlmethods.soft_ac_pi import SoftActorCritic
from rlmethods.rlutils import ReplayBuffer
#for irl
from irlmethods.deep_maxent import DeepMaxEnt
import irlmethods.irlUtils as irlUtils
from featureExtractor.gridworld_featureExtractor import OneHot, LocalGlobal, SocialNav, FrontBackSideSimple
agent_width = 10
step_size = 2
obs_width = 10
grid_size = 10
if args.feat_extractor is None:
print('Feature extractor missing.')
exit()
#check for the feature extractor being used
#initialize feature extractor
if args.feat_extractor == 'Onehot':
feat_ext = OneHot(grid_rows=10, grid_cols=10)
if args.feat_extractor == 'SocialNav':
feat_ext = SocialNav()
if args.feat_extractor == 'FrontBackSideSimple':
feat_ext = FrontBackSideSimple(
thresh1=1,
thresh2=2,
thresh3=3,
thresh4=4,
step_size=step_size,
agent_width=agent_width,
obs_width=obs_width,
)
if args.feat_extractor == 'LocalGlobal':
feat_ext = LocalGlobal(
window_size=5,
grid_size=grid_size,
agent_width=agent_width,
obs_width=obs_width,
step_size=step_size,
)
if args.feat_extractor == 'DroneFeatureSAM1':
feat_ext = DroneFeatureSAM1(agent_width=agent_width,
obs_width=obs_width,
step_size=step_size,
grid_size=grid_size,
thresh1=5,
thresh2=10)
if args.feat_extractor == 'DroneFeatureRisk':
feat_ext = DroneFeatureRisk(agent_width=agent_width,
obs_width=obs_width,
step_size=step_size,
grid_size=grid_size,
thresh1=15,
thresh2=30)
if args.feat_extractor == 'DroneFeatureRisk_v2':
feat_ext = DroneFeatureRisk_v2(agent_width=agent_width,
obs_width=obs_width,
step_size=step_size,
grid_size=grid_size,
thresh1=15,
thresh2=30)
if args.feat_extractor == 'DroneFeatureRisk_speed':
feat_ext = DroneFeatureRisk_speed(agent_width=agent_width,
obs_width=obs_width,
step_size=step_size,
grid_size=grid_size,
thresh1=10,
thresh2=15)
if args.feat_extractor == 'DroneFeatureRisk_speedv2':
feat_ext = DroneFeatureRisk_speedv2(agent_width=agent_width,
obs_width=obs_width,
step_size=step_size,
grid_size=grid_size,
thresh1=18,
thresh2=30)
experiment_logger.log_header('Parameters of the feature extractor :')
experiment_logger.log_info(feat_ext.__dict__)
#initialize the environment
if not args.dont_save and args.save_folder is None:
print('Specify folder to save the results.')
exit()
'''
environment can now initialize without an annotation file
if args.annotation_file is None:
print('Specify annotation file for the environment.')
exit()
'''
if args.exp_trajectory_path is None:
print('Specify expert trajectory folder.')
exit()
#**set is_onehot to false
goal_state = np.asarray([1, 5])
'''
env = GridWorld(display=args.render, is_onehot= False,is_random=False,
rows =10,
cols =10,
seed = 7,
obstacles = [np.asarray([5,5])],
goal_state = np.asarray([1,5]))
'''
env = GridWorld(display=args.render,
is_random=True,
rows=576,
cols=720,
agent_width=agent_width,
step_size=step_size,
obs_width=obs_width,
width=grid_size,
subject=args.subject,
annotation_file=args.annotation_file,
goal_state=goal_state,
step_wrapper=utils.step_wrapper,
seed=args.seed,
replace_subject=args.replace_subject,
segment_size=args.segment_size,
external_control=True,
continuous_action=True,
reset_wrapper=utils.reset_wrapper,
consider_heading=True,
is_onehot=False)
experiment_logger.log_header('Environment details :')
experiment_logger.log_info(env.__dict__)
#CHANGE HEREq
#CHANGE HERE
#initialize loss based termination
# intialize RL method
#CHANGE HERE
replay_buffer = ReplayBuffer(args.replay_buffer_size)
tbx_writer = SummaryWriter(to_save)
rl_method = SoftActorCritic(
env,
replay_buffer,
feat_ext,
buffer_sample_size=args.replay_buffer_sample_size,
tbx_writer=tbx_writer,
entropy_tuning=True,
tau=0.005,
log_alpha=args.log_alpha,
entropy_target=args.entropy_target,
render=args.render,
checkpoint_interval=100000000,
play_interval=args.play_interval,
)
print("RL method initialized.")
print(rl_method.policy)
experiment_logger.log_header('Details of the RL method :')
experiment_logger.log_info(rl_method.__dict__)
# initialize IRL method
#CHANGE HERE
trajectory_path = args.exp_trajectory_path
if args.scale_svf is None:
scale = False
if args.scale_svf:
scale = args.scale_svf
irl_method = DeepMaxEnt(trajectory_path,
rlmethod=rl_method,
rl_episodes=args.rl_episodes,
env=env,
iterations=args.irl_iterations,
on_server=args.on_server,
l1regularizer=args.regularizer,
learning_rate=args.lr_irl,
seed=args.seed,
graft=False,
scale_svf=scale,
rl_max_ep_len=args.max_episode_length,
hidden_dims=args.reward_net_hidden_dims,
clipping_value=args.clipping_value,
enumerate_all=True,
save_folder=parent_dir)
print("IRL method intialized.")
print(irl_method.reward)
experiment_logger.log_header('Details of the IRL method :')
experiment_logger.log_info(irl_method.__dict__)
irl_method.train()
if not args.dont_save:
pass
def main():
""" Run the experiment. """
# TensorboardX
tbx_writer = SummaryWriter(comment="pendulum_naive_AIRL")
tbx_writer.add_hparams(vars(args), {})
# env related
env = gym.make("Pendulum-v0")
feature_extractor = IdentityFeatureExtractor()
state_size = feature_extractor.extract_features(env.reset()).shape[0]
# rl related
replay_buffer = ReplayBuffer(args.replay_buffer_length)
rl = SoftActorCritic(
env,
replay_buffer,
feature_extractor,
args.replay_buffer_sample_size,
entropy_target=args.entropy_target,
entropy_tuning=args.disable_entropy_tuning,
tau=args.tau,
log_alpha=args.log_alpha,
play_interval=args.play_interval,
tbx_writer=tbx_writer,
learning_rate=1e-3,
)
# irl related
expert_policy = PolicyNetwork(state_size, env.action_space,
NN_HIDDEN_WIDTH)
expert_policy.load("../pendulum_policies/pendulum_expert.pt")
expert = PolicyExpert(expert_policy, env, args.num_expert_trajs,
args.max_env_steps)
expert_states = expert.get_expert_states()
expert_actions = expert.get_expert_actions()
irl = NaiveAIRL(
rl,
env,
expert_states,
expert_actions,
tbx_writer=tbx_writer,
learning_rate=1e-3,
)
irl.train(
args.irl_episodes,
args.irl_traj_per_ep,
args.max_env_steps,
args.irl_num_policy_updates,
)
import pdb
pdb.set_trace()
def main():
args = parser.parse_args()
utils.seed_all(args.seed)
if args.on_server:
# matplotlib without monitor
matplotlib.use("Agg")
# pygame without monitor
os.environ["SDL_VIDEODRIVER"] = "dummy"
#####for the logger
ts = time.time()
st = datetime.datetime.fromtimestamp(ts).strftime("%Y-%m-%d_%H:%M:%S")
###################
if not args.save_folder:
print("Provide save folder.")
exit()
policy_net_dims = "-policy_net-"
for dim in args.policy_net_hidden_dims:
policy_net_dims += str(dim)
policy_net_dims += "-"
reward_net_dims = "-reward_net-"
for dim in args.reward_net_hidden_dims:
reward_net_dims += str(dim)
reward_net_dims += "-"
parent_dir = ("./results/" + str(args.save_folder) + st + policy_net_dims +
reward_net_dims)
to_save = ("./results/" + str(args.save_folder) + st + policy_net_dims +
reward_net_dims + "-reg-" + str(args.regularizer) + "-seed-" +
str(args.seed) + "-lr-" + str(args.lr_irl))
log_file = "Experiment_info.txt"
experiment_logger = Logger(to_save, log_file)
experiment_logger.log_header("Arguments for the experiment :")
repo = git.Repo(search_parent_directories=True)
experiment_logger.log_info({'From branch : ': repo.active_branch.name})
experiment_logger.log_info({'Commit number : ': repo.head.object.hexsha})
experiment_logger.log_info(vars(args))
# from rlmethods.rlutils import LossBasedTermination
# for rl
from rlmethods.b_actor_critic import ActorCritic
from rlmethods.soft_ac_pi import SoftActorCritic
from rlmethods.soft_ac import SoftActorCritic as QSAC
from rlmethods.rlutils import ReplayBuffer
# for irl
from irlmethods.deep_maxent import DeepMaxEnt
import irlmethods.irlUtils as irlUtils
from featureExtractor.gridworld_featureExtractor import (
OneHot,
LocalGlobal,
SocialNav,
FrontBackSideSimple,
)
agent_width = 10
step_size = 2
obs_width = 10
grid_size = 10
if args.feat_extractor is None:
print("Feature extractor missing.")
exit()
# check for the feature extractor being used
# initialize feature extractor
if args.feat_extractor == "Onehot":
feat_ext = OneHot(grid_rows=10, grid_cols=10)
if args.feat_extractor == "SocialNav":
feat_ext = SocialNav()
if args.feat_extractor == "FrontBackSideSimple":
feat_ext = FrontBackSideSimple(
thresh1=1,
thresh2=2,
thresh3=3,
thresh4=4,
step_size=step_size,
agent_width=agent_width,
obs_width=obs_width,
)
if args.feat_extractor == "LocalGlobal":
feat_ext = LocalGlobal(
window_size=5,
grid_size=grid_size,
agent_width=agent_width,
obs_width=obs_width,
step_size=step_size,
)
if args.feat_extractor == "DroneFeatureSAM1":
feat_ext = DroneFeatureSAM1(
agent_width=agent_width,
obs_width=obs_width,
step_size=step_size,
grid_size=grid_size,
thresh1=5,
thresh2=10,
)
if args.feat_extractor == "DroneFeatureRisk":
feat_ext = DroneFeatureRisk(
agent_width=agent_width,
obs_width=obs_width,
step_size=step_size,
grid_size=grid_size,
thresh1=15,
thresh2=30,
)
if args.feat_extractor == "DroneFeatureRisk_v2":
feat_ext = DroneFeatureRisk_v2(
agent_width=agent_width,
obs_width=obs_width,
step_size=step_size,
grid_size=grid_size,
thresh1=15,
thresh2=30,
)
if args.feat_extractor == "DroneFeatureRisk_speed":
feat_ext = DroneFeatureRisk_speed(
agent_width=agent_width,
obs_width=obs_width,
step_size=step_size,
grid_size=grid_size,
thresh1=10,
thresh2=15,
)
if args.feat_extractor == "DroneFeatureRisk_speedv2":
feat_ext = DroneFeatureRisk_speedv2(
agent_width=agent_width,
obs_width=obs_width,
step_size=step_size,
grid_size=grid_size,
thresh1=18,
thresh2=30,
)
if args.feat_extractor == 'VasquezF1':
feat_ext = VasquezF1(agent_width * 6, 0.5, 1.0)
if args.feat_extractor == 'VasquezF2':
feat_ext = VasquezF1(agent_width * 6, 0.5, 1.0)
if args.feat_extractor == 'VasquezF3':
feat_ext = VasquezF3(agent_width)
if args.feat_extractor == "Fahad":
feat_ext = Fahad(36, 60, 0.5, 1.0)
if args.feat_extractor == "GoalConditionedFahad":
feat_ext = GoalConditionedFahad(36, 60, 0.5, 1.0)
experiment_logger.log_header("Parameters of the feature extractor :")
experiment_logger.log_info(feat_ext.__dict__)
# initialize the environment
if not args.dont_save and args.save_folder is None:
print("Specify folder to save the results.")
exit()
"""
environment can now initialize without an annotation file
if args.annotation_file is None:
print('Specify annotation file for the environment.')
exit()
"""
if args.exp_trajectory_path is None:
print("Specify expert trajectory folder.")
exit()
"""
env = GridWorld(display=args.render, is_onehot= False,is_random=False,
rows =10,
cols =10,
seed = 7,
obstacles = [np.asarray([5,5])],
goal_state = np.asarray([1,5]))
"""
env = GridWorld(
display=args.render,
is_random=True,
rows=576,
cols=720,
agent_width=agent_width,
step_size=step_size,
obs_width=obs_width,
width=grid_size,
subject=args.subject,
annotation_file=args.annotation_file,
goal_state=None,
step_wrapper=utils.step_wrapper,
seed=args.seed,
replace_subject=args.replace_subject,
segment_size=args.segment_size,
external_control=True,
continuous_action=False,
reset_wrapper=utils.reset_wrapper,
consider_heading=True,
is_onehot=False,
)
experiment_logger.log_header("Environment details :")
experiment_logger.log_info(env.__dict__)
# CHANGE HEREq
# CHANGE HERE
# initialize loss based termination
# intialize RL method
# CHANGE HERE
if args.rl_method == "ActorCritic":
rl_method = ActorCritic(
env,
feat_extractor=feat_ext,
gamma=1,
log_interval=args.rl_log_intervals,
max_episode_length=args.rl_ep_length,
hidden_dims=args.policy_net_hidden_dims,
save_folder=to_save,
lr=args.lr_rl,
max_episodes=args.rl_episodes,
)
if args.rl_method == "SAC":
if not env.continuous_action:
print("The action space needs to be continuous for SAC to work.")
exit()
replay_buffer = ReplayBuffer(args.replay_buffer_size)
rl_method = SoftActorCritic(
env,
replay_buffer,
feat_ext,
play_interval=500,
learning_rate=args.lr_rl,
buffer_sample_size=args.replay_buffer_sample_size,
)
if args.rl_method == "discrete_SAC":
if not isinstance(env.action_space, gym.spaces.Discrete):
print(
"discrete SAC requires a discrete action space environmnet to work."
)
exit()
replay_buffer = ReplayBuffer(args.replay_buffer_size)
rl_method = QSAC(
env,
replay_buffer,
feat_ext,
args.replay_buffer_sample_size,
learning_rate=args.lr_rl,
entropy_tuning=True,
entropy_target=0.3,
play_interval=args.play_interval,
)
print("RL method initialized.")
print(rl_method.policy)
if args.policy_path is not None:
rl_method.policy.load(args.policy_path)
experiment_logger.log_header("Details of the RL method :")
experiment_logger.log_info(rl_method.__dict__)
# initialize IRL method
# CHANGE HERE
trajectory_path = args.exp_trajectory_path
if args.scale_svf is None:
scale = False
if args.scale_svf:
scale = args.scale_svf
irl_method = DeepMaxEnt(
trajectory_path,
rlmethod=rl_method,
env=env,
iterations=args.irl_iterations,
on_server=args.on_server,
l1regularizer=args.regularizer,
learning_rate=args.lr_irl,
seed=args.seed,
graft=False,
scale_svf=scale,
hidden_dims=args.reward_net_hidden_dims,
clipping_value=args.clipping_value,
enumerate_all=True,
save_folder=parent_dir,
rl_max_ep_len=args.rl_ep_length,
rl_episodes=args.rl_episodes,
)
print("IRL method intialized.")
print(irl_method.reward)
experiment_logger.log_header("Details of the IRL method :")
experiment_logger.log_info(irl_method.__dict__)
smoothing_flag = False
if args.svf_smoothing:
smoothing_flag = True
irl_method.train(smoothing=smoothing_flag)
if not args.dont_save:
pass
def main():
#####for the logger
ts = time.time()
st = datetime.datetime.fromtimestamp(ts).strftime("%Y-%m-%d %H:%M:%S")
###################
args = parser.parse_args()
seed_all(args.seed)
if args.on_server:
matplotlib.use("Agg")
# pygame without monitor
os.environ["SDL_VIDEODRIVER"] = "dummy"
from matplotlib import pyplot as plt
mp.set_start_method("spawn")
from rlmethods.b_actor_critic import ActorCritic
from rlmethods.soft_ac import SoftActorCritic, QSoftActorCritic
from rlmethods.rlutils import ReplayBuffer
from envs.gridworld_drone import GridWorldDrone
from featureExtractor.drone_feature_extractor import (
DroneFeatureSAM1,
DroneFeatureOccup,
DroneFeatureRisk,
DroneFeatureRisk_v2,
VasquezF1,
VasquezF2,
VasquezF3,
Fahad,
GoalConditionedFahad,
)
from featureExtractor.gridworld_featureExtractor import (
FrontBackSide,
LocalGlobal,
OneHot,
SocialNav,
FrontBackSideSimple,
)
from featureExtractor.drone_feature_extractor import (
DroneFeatureRisk_speed,
DroneFeatureRisk_speedv2,
)
from featureExtractor.drone_feature_extractor import VasquezF1
save_folder = None
if not args.dont_save and not args.play:
if not args.save_folder:
print("Provide save folder.")
exit()
policy_net_dims = "-policy_net-"
for dim in args.policy_net_hidden_dims:
policy_net_dims += str(dim)
policy_net_dims += "-"
reward_net_dims = "-reward_net-"
for dim in args.reward_net_hidden_dims:
reward_net_dims += str(dim)
reward_net_dims += "-"
save_folder = (
"./results/"
+ args.save_folder
+ st
+ args.feat_extractor
+ "-seed-"
+ str(args.seed)
+ policy_net_dims
+ reward_net_dims
+ "-total-ep-"
+ str(args.total_episodes)
+ "-max-ep-len-"
+ str(args.max_ep_length)
)
experiment_logger = Logger(save_folder, "experiment_info.txt")
experiment_logger.log_header("Arguments for the experiment :")
repo = git.Repo(search_parent_directories=True)
experiment_logger.log_info({'From branch : ' : repo.active_branch.name})
experiment_logger.log_info({'Commit number : ' : repo.head.object.hexsha})
experiment_logger.log_info(vars(args))
window_size = 9
step_size = 2
agent_width = 10
obs_width = 10
grid_size = 10
feat_ext = None
# initialize the feature extractor to be used
if args.feat_extractor == "Onehot":
feat_ext = OneHot(grid_rows=10, grid_cols=10)
if args.feat_extractor == "SocialNav":
feat_ext = SocialNav(fieldList=["agent_state", "goal_state"])
if args.feat_extractor == "FrontBackSideSimple":
feat_ext = FrontBackSideSimple(
thresh1=1,
thresh2=2,
thresh3=3,
thresh4=4,
step_size=step_size,
agent_width=agent_width,
obs_width=obs_width,
)
if args.feat_extractor == "LocalGlobal":
feat_ext = LocalGlobal(
window_size=11,
grid_size=grid_size,
agent_width=agent_width,
obs_width=obs_width,
step_size=step_size,
)
if args.feat_extractor == "DroneFeatureSAM1":
feat_ext = DroneFeatureSAM1(
agent_width=agent_width,
obs_width=obs_width,
step_size=step_size,
grid_size=grid_size,
thresh1=15,
thresh2=30,
)
if args.feat_extractor == "DroneFeatureOccup":
feat_ext = DroneFeatureOccup(
agent_width=agent_width,
obs_width=obs_width,
step_size=step_size,
grid_size=grid_size,
window_size=window_size,
)
if args.feat_extractor == "DroneFeatureRisk":
feat_ext = DroneFeatureRisk(
agent_width=agent_width,
obs_width=obs_width,
step_size=step_size,
grid_size=grid_size,
show_agent_persp=False,
thresh1=15,
thresh2=30,
)
if args.feat_extractor == "DroneFeatureRisk_v2":
feat_ext = DroneFeatureRisk_v2(
agent_width=agent_width,
obs_width=obs_width,
step_size=step_size,
grid_size=grid_size,
show_agent_persp=False,
thresh1=15,
thresh2=30,
)
if args.feat_extractor == "DroneFeatureRisk_speed":
feat_ext = DroneFeatureRisk_speed(
agent_width=agent_width,
obs_width=obs_width,
step_size=step_size,
grid_size=grid_size,
show_agent_persp=False,
return_tensor=False,
thresh1=10,
thresh2=15,
)
if args.feat_extractor == "DroneFeatureRisk_speedv2":
feat_ext = DroneFeatureRisk_speedv2(
agent_width=agent_width,
obs_width=obs_width,
step_size=step_size,
grid_size=grid_size,
show_agent_persp=False,
return_tensor=False,
thresh1=18,
thresh2=30,
)
if args.feat_extractor == "VasquezF1":
feat_ext = VasquezF1(agent_width * 6, 0.5, 1.0)
if args.feat_extractor == "VasquezF2":
feat_ext = VasquezF1(agent_width * 6, 0.5, 1.0)
if args.feat_extractor == "VasquezF3":
feat_ext = VasquezF3(agent_width)
if args.feat_extractor == "Fahad":
feat_ext = Fahad(36, 60, 0.5, 1.0)
if args.feat_extractor == "GoalConditionedFahad":
feat_ext = GoalConditionedFahad(36, 60, 0.5, 1.0)
if feat_ext is None:
print("Please enter proper feature extractor!")
exit()
# log feature extractor info
if not args.dont_save and not args.play:
experiment_logger.log_header("Parameters of the feature extractor :")
experiment_logger.log_info(feat_ext.__dict__)
# initialize the environment
if args.replace_subject:
replace_subject = True
else:
replace_subject = False
env = GridWorldDrone(
display=args.render,
is_onehot=False,
seed=args.seed,
obstacles=None,
show_trail=False,
is_random=True,
annotation_file=args.annotation_file,
subject=args.subject,
tick_speed=60,
obs_width=10,
step_size=step_size,
agent_width=agent_width,
replace_subject=replace_subject,
segment_size=args.segment_size,
external_control=True,
step_reward=0.001,
show_comparison=True,
consider_heading=True,
show_orientation=True,
# rows=200, cols=200, width=grid_size)
rows=576,
cols=720,
width=grid_size,
)
# env = gym.make('Acrobot-v1')
# log environment info
if not args.dont_save and not args.play:
experiment_logger.log_header("Environment details :")
experiment_logger.log_info(env.__dict__)
# initialize RL
if args.rl_method == "ActorCritic":
model = ActorCritic(
env,
feat_extractor=feat_ext,
gamma=1,
log_interval=100,
max_episode_length=args.max_ep_length,
hidden_dims=args.policy_net_hidden_dims,
save_folder=save_folder,
lr=args.lr,
entropy_coeff=args.entropy_coeff,
max_episodes=args.total_episodes,
)
if args.rl_method == "SAC":
replay_buffer = ReplayBuffer(args.replay_buffer_size)
model = SoftActorCritic(
env,
replay_buffer,
feat_ext,
buffer_sample_size=args.replay_buffer_sample_size,
entropy_tuning=True,
play_interval=args.play_interval,
entropy_target=args.entropy_target,
gamma=args.gamma,
learning_rate=args.lr,
)
if args.rl_method == "discrete_QSAC":
replay_buffer = ReplayBuffer(args.replay_buffer_size)
model = QSoftActorCritic(
env,
replay_buffer,
feat_ext,
buffer_sample_size=args.replay_buffer_sample_size,
entropy_tuning=True,
play_interval=args.play_interval,
entropy_target=args.entropy_target,
gamma=args.gamma,
learning_rate=args.lr,
)
# log RL info
if not args.dont_save and not args.play:
experiment_logger.log_header("Details of the RL method :")
experiment_logger.log_info(model.__dict__)
if args.policy_path is not None:
from debugtools import numericalSort
policy_file_list = []
reward_across_models = []
# print(args.policy_path)
if os.path.isfile(args.policy_path):
policy_file_list.append(args.policy_path)
if os.path.isdir(args.policy_path):
policy_names = glob.glob(os.path.join(args.policy_path, "*.pt"))
policy_file_list = sorted(policy_names, key=numericalSort)
xaxis = np.arange(len(policy_file_list))
if not args.play and not args.play_user:
# no playing of any kind, so training
if args.reward_path is None:
if args.policy_path:
model.policy.load(args.policy_path)
if args.rl_method == "SAC" or args.rl_method == "discrete_QSAC":
model.train(args.total_episodes, args.max_ep_length)
else:
model.train()
else:
from irlmethods.deep_maxent import RewardNet
state_size = feat_ext.extract_features(env.reset()).shape[0]
reward_net = RewardNet(state_size, args.reward_net_hidden_dims)
reward_net.load(args.reward_path)
print(next(reward_net.parameters()).is_cuda)
model.train(reward_net=reward_net)
if not args.dont_save:
model.policy.save(save_folder + "/policy-models/")
if args.play:
# env.tickSpeed = 15
from debugtools import compile_results
xaxis = []
counter = 1
plt.figure(0)
avg_reward_list = []
frac_good_run_list = []
print(policy_file_list)
for policy_file in policy_file_list:
print("Playing for policy :", policy_file)
model.policy.load(policy_file)
policy_folder = policy_file.strip().split("/")[0:-2]
save_folder = ""
for p in policy_folder:
save_folder = save_folder + p + "/"
print("The final save folder ", save_folder)
# env.tickSpeed = 10
assert args.policy_path is not None, "pass a policy to play from!"
if args.exp_trajectory_path is not None:
from irlmethods.irlUtils import calculate_expert_svf
expert_svf = calculate_expert_svf(
args.exp_trajectory_path,
max_time_steps=args.max_ep_length,
feature_extractor=feat_ext,
gamma=1,
)
# reward_across_models.append(model.generate_trajectory(args.num_trajs, args.render))
if args.exp_trajectory_path is None:
if args.dont_save:
rewards, state_info, sub_info = model.generate_trajectory(
args.num_trajs, args.render
)
else:
rewards, state_info, sub_info = model.generate_trajectory(
args.num_trajs,
args.render,
store_raw=args.store_raw_states,
path=save_folder + "/agent_generated_trajectories/",
)
else:
if args.dont_save:
rewards, state_info, sub_info = model.generate_trajectory(
args.num_trajs, args.render, expert_svf=expert_svf
)
else:
rewards, state_info, sub_info = model.generate_trajectory(
args.num_trajs,
args.render,
path=save_folder + "/agent_generated_trajectories/",
expert_svf=expert_svf,
)
avg_reward, good_run_frac = compile_results(
rewards, state_info, sub_info
)
avg_reward_list.append(avg_reward)
frac_good_run_list.append(good_run_frac)
plt.plot(avg_reward_list, c="r")
plt.plot(frac_good_run_list, c="g")
plt.draw()
plt.show()
if args.play_user:
env.tickSpeed = 200
model.generate_trajectory_user(
args.num_trajs, args.render, path="./user_generated_trajectories/"
)
def main():
# initalize summary writer
tbx_writer = SummaryWriter(comment="_alpha_" + str(args.log_alpha))
# initialize replay buffer
replay_buffer = ReplayBuffer(args.replay_buffer_size)
# initialize feature extractor
feature_extractor = DroneFeatureRisk_speed(
agent_width=agent_width,
obs_width=obs_width,
step_size=step_size,
grid_size=grid_size,
thresh1=18,
thresh2=30,
)
# initialize checkpoint
if args.checkpoint_path:
checkpointer = Checkpointer.load_checkpointer(args.checkpoint_path)
else:
checkpointer = None
# initialize environment
env = GridWorldDrone(
display=args.render,
is_random=True,
rows=576,
cols=720,
agent_width=agent_width,
step_size=step_size,
obs_width=obs_width,
width=grid_size,
annotation_file=args.annotation_file,
external_control=True,
continuous_action=True,
consider_heading=True,
is_onehot=False,
)
# initialize the reward network
state_size = feature_extractor.extract_features(env.reset()).shape[0]
reward_net = None
if args.reward_path is not None:
reward_net = RewardNet(state_size, args.reward_net_hidden_dims)
reward_net.load(args.reward_path)
# intialize the RL method
soft_ac = SoftActorCritic(
env,
replay_buffer,
feature_extractor,
buffer_sample_size=args.replay_buffer_sample_size,
tbx_writer=tbx_writer,
tau=0.005,
log_alpha=args.log_alpha,
entropy_tuning=True,
entropy_target=args.entropy_target,
render=args.render,
play_interval=args.play_interval,
checkpointer=checkpointer,
)
soft_ac.train(
args.rl_episodes, args.max_episode_length, reward_network=reward_net
)
soft_ac.policy.save("./cont_world_policies")