def __init__(self, load_dataset=True):
super(BehavioralDistAgent, self).__init__()
self.meta, self.data = preprocess_demonstrations()
if load_dataset:
# demonstration source
self.meta = divide_dataset(self.meta)
# datasets
self.train_dataset = DemonstrationMemory("train", self.meta,
self.data)
self.val_dataset = DemonstrationMemory("val", self.meta, self.data)
self.test_dataset = DemonstrationMemory("test", self.meta,
self.data)
self.full_dataset = DemonstrationMemory("full", self.meta,
self.data)
self.train_sampler = DemonstrationBatchSampler(self.train_dataset,
train=True)
self.val_sampler = DemonstrationBatchSampler(self.train_dataset,
train=False)
self.test_sampler = DemonstrationBatchSampler(self.test_dataset,
train=False)
self.episodic_sampler = SequentialDemonstrationSampler(
self.full_dataset)
self.train_loader = torch.utils.data.DataLoader(
self.train_dataset,
batch_sampler=self.train_sampler,
num_workers=args.cpu_workers,
pin_memory=True,
drop_last=False)
self.test_loader = torch.utils.data.DataLoader(
self.test_dataset,
batch_sampler=self.test_sampler,
num_workers=args.cpu_workers,
pin_memory=True,
drop_last=False)
self.val_loader = torch.utils.data.DataLoader(
self.val_dataset,
batch_sampler=self.val_sampler,
num_workers=args.cpu_workers,
pin_memory=True,
drop_last=False)
self.episodic_loader = torch.utils.data.DataLoader(
self.full_dataset,
sampler=self.episodic_sampler,
batch_size=self.batch,
num_workers=args.cpu_workers)
if not self.wasserstein:
self.loss_fn_vs = torch.nn.CrossEntropyLoss(size_average=True)
self.loss_fn_qs = torch.nn.CrossEntropyLoss(size_average=True)
self.loss_fn_vl = torch.nn.CrossEntropyLoss(size_average=True)
self.loss_fn_ql = torch.nn.CrossEntropyLoss(size_average=True)
else:
self.loss_fn_vs = wasserstein_metric(support=args.atoms_short, n=1)
self.loss_fn_qs = wasserstein_metric(support=args.atoms_short, n=1)
self.loss_fn_vl = wasserstein_metric(support=args.atoms_long, n=1)
self.loss_fn_ql = wasserstein_metric(support=args.atoms_long, n=1)
self.histogram = torch.from_numpy(self.meta['histogram']).float()
m = self.histogram.max()
self.histogram = m / self.histogram
self.histogram = torch.clamp(self.histogram, 0, 10).cuda()
self.loss_fn_beta = torch.nn.CrossEntropyLoss(size_average=True,
weight=self.histogram)
self.loss_fn_pi_s = torch.nn.CrossEntropyLoss(reduce=False,
size_average=True)
self.loss_fn_pi_l = torch.nn.CrossEntropyLoss(reduce=False,
size_average=True)
self.loss_fn_pi_s_tau = torch.nn.CrossEntropyLoss(reduce=False,
size_average=True)
self.loss_fn_pi_l_tau = torch.nn.CrossEntropyLoss(reduce=False,
size_average=True)
# alpha weighted sum
self.alpha_b = 1 # 1 / 0.7
self.alpha_vs = 1 # 1 / 0.02
self.alpha_qs = 1
self.alpha_vl = 1 # 1 / 0.02
self.alpha_ql = 1
self.alpha_pi_s = 1 # 1 / 0.02
self.alpha_pi_l = 1
self.alpha_pi_s_tau = 1 # 1 / 0.02
self.alpha_pi_l_tau = 1
self.model = BehavioralDistNet()
self.model.cuda()
# configure learning
net_parameters = [
p[1] for p in self.model.named_parameters() if "rn_" in p[0]
]
vl_params = [
p[1] for p in self.model.named_parameters() if "on_vl" in p[0]
]
ql_params = [
p[1] for p in self.model.named_parameters() if "on_ql" in p[0]
]
vs_params = [
p[1] for p in self.model.named_parameters() if "on_vs" in p[0]
]
qs_params = [
p[1] for p in self.model.named_parameters() if "on_qs" in p[0]
]
beta_params = [
p[1] for p in self.model.named_parameters() if "on_beta" in p[0]
]
pi_s_params = [
p[1] for p in self.model.named_parameters() if "on_pi_s" in p[0]
]
pi_l_params = [
p[1] for p in self.model.named_parameters() if "on_pi_l" in p[0]
]
pi_tau_s_params = [
p[1] for p in self.model.named_parameters()
if "on_pi_tau_s" in p[0]
]
pi_tau_l_params = [
p[1] for p in self.model.named_parameters()
if "on_pi_tau_l" in p[0]
]
self.parameters_group_a = net_parameters + vl_params + ql_params + vs_params + qs_params + beta_params
self.parameters_group_b = pi_s_params + pi_l_params + pi_tau_s_params + pi_tau_l_params
# IT IS IMPORTANT TO ASSIGN MODEL TO CUDA/PARALLEL BEFORE DEFINING OPTIMIZER
self.optimizer_vl = BehavioralDistAgent.set_optimizer(
net_parameters + vl_params, args.lr_vl)
self.scheduler_vl = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_vl, self.decay)
self.optimizer_beta = BehavioralDistAgent.set_optimizer(
net_parameters + beta_params, args.lr_beta)
self.scheduler_beta = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_beta, self.decay)
self.optimizer_vs = BehavioralDistAgent.set_optimizer(
net_parameters + vs_params, args.lr_vs)
self.scheduler_vs = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_vs, self.decay)
self.optimizer_qs = BehavioralDistAgent.set_optimizer(
net_parameters + qs_params, args.lr_qs)
self.scheduler_qs = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_qs, self.decay)
self.optimizer_ql = BehavioralDistAgent.set_optimizer(
net_parameters + ql_params, args.lr_ql)
self.scheduler_ql = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_ql, self.decay)
self.optimizer_pi_l = BehavioralDistAgent.set_optimizer(
pi_l_params, args.lr_pi_l)
self.scheduler_pi_l = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_pi_l, self.decay)
self.optimizer_pi_s = BehavioralDistAgent.set_optimizer(
pi_s_params, args.lr_pi_s)
self.scheduler_pi_s = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_pi_s, self.decay)
self.optimizer_pi_l_tau = BehavioralDistAgent.set_optimizer(
pi_tau_l_params, args.lr_pi_tau_l)
self.scheduler_pi_l_tau = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_pi_l_tau, self.decay)
self.optimizer_pi_s_tau = BehavioralDistAgent.set_optimizer(
pi_tau_s_params, args.lr_pi_tau_s)
self.scheduler_pi_s_tau = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_pi_s_tau, self.decay)
actions = torch.FloatTensor(consts.hotvec_matrix) / (3**(0.5))
actions = Variable(actions, requires_grad=False).cuda()
self.actions_matrix = actions.unsqueeze(0)
self.reverse_excitation_index = consts.hotvec_inv
self.short_bins = consts.short_bins[
args.game][:-1] / self.meta['avg_score']
# the long bins are already normalized
self.long_bins = consts.long_bins[args.game][:-1]
self.short_bins_torch = Variable(torch.from_numpy(
consts.short_bins[args.game] / self.meta['avg_score']),
requires_grad=False).cuda()
self.long_bins_torch = Variable(torch.from_numpy(
consts.long_bins[args.game]),
requires_grad=False).cuda()
self.batch_range = np.arange(self.batch)
self.zero = Variable(torch.zeros(1))
def __init__(self, load_dataset=True):
super(ACDQNLSTMAgent, self).__init__()
self.meta, self.data = preprocess_demonstrations()
if load_dataset:
# demonstration source
self.meta = divide_dataset(self.meta)
# datasets
self.train_dataset = DemonstrationMemory("train", self.meta, self.data)
self.test_dataset = DemonstrationMemory("test", self.meta, self.data)
self.train_sampler = DemonstrationBatchSampler(self.train_dataset, train=True)
self.test_sampler = DemonstrationBatchSampler(self.test_dataset, train=False)
self.train_loader = torch.utils.data.DataLoader(self.train_dataset, batch_sampler=self.train_sampler,
num_workers=args.cpu_workers, pin_memory=True, drop_last=False)
self.test_loader = torch.utils.data.DataLoader(self.test_dataset, batch_sampler=self.test_sampler,
num_workers=args.cpu_workers, pin_memory=True, drop_last=False)
self.loss_v_beta = torch.nn.L1Loss(size_average=True, reduce=True)
self.loss_q_beta = torch.nn.L1Loss(size_average=True, reduce=True)
self.loss_v_pi = torch.nn.L1Loss(size_average=True, reduce=True)
self.loss_q_pi = torch.nn.L1Loss(size_average=True, reduce=True)
self.loss_p = torch.nn.L1Loss(size_average=True, reduce=True)
self.histogram = torch.from_numpy(self.meta['histogram']).float()
weights = self.histogram.max() / self.histogram
weights = torch.clamp(weights, 0, 10).cuda()
self.loss_beta = torch.nn.CrossEntropyLoss(size_average=True)
self.loss_pi = torch.nn.CrossEntropyLoss(reduce=False)
# actor critic setting
self.model_b_single = ACDQNLSTM().cuda()
self.model_single = ACDQNLSTM().cuda()
self.target_single = ACDQNLSTM().cuda()
if self.parallel:
self.model_b = torch.nn.DataParallel(self.model_b_single)
self.model = torch.nn.DataParallel(self.model_single)
self.target = torch.nn.DataParallel(self.target_single)
else:
self.model_b = self.model_b_single
self.model = self.model_single
self.target = self.target_single
self.target_single.reset_target()
# configure learning
# IT IS IMPORTANT TO ASSIGN MODEL TO CUDA/PARALLEL BEFORE DEFINING OPTIMIZER
self.optimizer_q_pi = ACDQNLSTMAgent.set_optimizer(self.model.parameters(), 0.0002)
self.scheduler_q_pi = torch.optim.lr_scheduler.ExponentialLR(self.optimizer_q_pi, self.decay)
self.optimizer_pi = ACDQNLSTMAgent.set_optimizer(self.model.parameters(), 0.0002)
self.scheduler_pi = torch.optim.lr_scheduler.ExponentialLR(self.optimizer_pi, self.decay)
self.optimizer_q_beta = ACDQNLSTMAgent.set_optimizer(self.model_b.parameters(), 0.0002)
self.scheduler_q_beta = torch.optim.lr_scheduler.ExponentialLR(self.optimizer_q_beta, self.decay)
self.optimizer_beta = ACDQNLSTMAgent.set_optimizer(self.model_b.parameters(), 0.0008)
self.scheduler_beta = torch.optim.lr_scheduler.ExponentialLR(self.optimizer_beta, self.decay)
actions = torch.LongTensor(consts.hotvec_matrix).cuda()
self.actions_matrix = Variable(actions.unsqueeze(0), requires_grad=False)
self.batch_actions_matrix = self.actions_matrix.repeat(self.batch, 1, 1)
self.batch_range = np.arange(self.batch)
self.zero = Variable(torch.zeros(1))
self.a_post_mat = Variable(torch.from_numpy(consts.a_post_mat).long(), requires_grad=False).cuda()
self.a_post_mat = self.a_post_mat.unsqueeze(0).repeat(self.batch, 1, 1)
def __init__(self):
super(LfdAgent, self).__init__()
# demonstration source
self.meta, self.data = preprocess_demonstrations()
self.meta = divide_dataset(self.meta)
# datasets
self.train_dataset = DemonstrationMemory("train", self.meta, self.data)
self.val_dataset = DemonstrationMemory("val", self.meta, self.data)
self.test_dataset = DemonstrationMemory("test", self.meta, self.data)
self.full_dataset = DemonstrationMemory("full", self.meta, self.data)
self.train_sampler = DemonstrationBatchSampler(self.train_dataset, train=True)
self.val_sampler = DemonstrationBatchSampler(self.train_dataset, train=False)
self.test_sampler = DemonstrationBatchSampler(self.test_dataset, train=False)
self.episodic_sampler = SequentialDemonstrationSampler(self.full_dataset)
self.train_loader = torch.utils.data.DataLoader(self.train_dataset, batch_sampler=self.train_sampler,
num_workers=args.cpu_workers, pin_memory=True, drop_last=False)
self.test_loader = torch.utils.data.DataLoader(self.test_dataset, batch_sampler=self.test_sampler,
num_workers=args.cpu_workers, pin_memory=True, drop_last=False)
self.val_loader = torch.utils.data.DataLoader(self.val_dataset, batch_sampler=self.val_sampler,
num_workers=args.cpu_workers, pin_memory=True, drop_last=False)
self.episodic_loader = torch.utils.data.DataLoader(self.full_dataset, sampler=self.episodic_sampler,
batch_size=self.batch, num_workers=args.cpu_workers)
# set learn validate test play parameters based on arguments
# configure learning
if not args.value_advantage:
self.learn = self.learn_q
self.test = self.test_q
self.player = QPlayer
self.agent_type = 'q'
# loss function and optimizer
if self.l1_loss:
self.loss_fn = torch.nn.L1Loss(size_average=True)
self.individual_loss_fn = self.individual_loss_fn_l1
else:
self.loss_fn = torch.nn.MSELoss(size_average=True)
self.individual_loss_fn = self.individual_loss_fn_l2
# Choose a model acording to the configurations
models = {(0,): DQN, (1,): DQNDueling}
Model = models[(self.dueling,)]
self.model_single = Model(self.action_space)
self.target_single = Model(self.action_space)
else:
if args.value_only:
self.alpha_v, self.alpha_a = 1, 0
else:
self.alpha_v, self.alpha_a = 1, 1
if self.l1_loss:
self.loss_fn_value = torch.nn.L1Loss(size_average=True)
self.loss_fn_advantage = torch.nn.L1Loss(size_average=True)
self.individual_loss_fn = self.individual_loss_fn_l1
else:
self.loss_fn_value = torch.nn.MSELoss(size_average=True)
self.loss_fn_advantage = torch.nn.MSELoss(size_average=True)
self.individual_loss_fn = self.individual_loss_fn_l2
if not args.input_actions:
self.learn = self.learn_va
self.test = self.test_va
self.player = AVPlayer
self.agent_type = 'av'
self.model_single = DVAN_ActionOut(self.action_space)
self.target_single = DVAN_ActionOut(self.action_space)
else:
self.learn = self.learn_ava
self.test = self.test_ava
self.player = AVAPlayer
self.agent_type = 'ava'
self.model_single = DVAN_ActionIn(3)
self.target_single = DVAN_ActionIn(3)
# model specific parameters
self.action_space = consts.action_space
self.excitation = torch.LongTensor(consts.excitation_map)
self.excitation_length = self.excitation.shape[0]
self.mask = torch.LongTensor(consts.excitation_mask[args.game])
self.mask_dup = self.mask.unsqueeze(0).repeat(self.action_space, 1)
actions = Variable(self.mask_dup * self.excitation, requires_grad=False)
actions = actions.cuda()
self.actions_matrix = actions.unsqueeze(0)
self.actions_matrix = self.actions_matrix.repeat(self.batch, 1, 1).float()
self.reverse_excitation_index = consts.reverse_excitation_index
if not args.play:
self.play = self.dummy_play
elif args.gpu_workers == 0:
self.play = self.single_play
else:
self.play = self.multi_play
q_functions = {(0, 0): self.simple_q, (0, 1): self.double_q, (1, 0): self.simple_on_q,
(1, 1): self.simple_on_q}
self.q_estimator = q_functions[(self.double_q, self.on_policy)]
# configure learning
if args.cuda:
self.model_single = self.model_single.cuda()
self.target_single = self.target_single.cuda()
self.model = torch.nn.DataParallel(self.model_single)
self.target = torch.nn.DataParallel(self.target_single)
else:
self.model = self.model_single
self.target = self.target_single
# IT IS IMPORTANT TO ASSIGN MODEL TO CUDA/PARALLEL BEFORE DEFINING OPTIMIZER
self.optimizer = LfdAgent.set_optimizer(self.model.parameters())
self.scheduler = torch.optim.lr_scheduler.ExponentialLR(self.optimizer, self.decay)
def __init__(self, load_dataset=True):
super(BehavioralHotAgent, self).__init__()
self.meta, self.data = preprocess_demonstrations()
if load_dataset:
# demonstration source
self.meta = divide_dataset(self.meta)
# datasets
self.train_dataset = DemonstrationMemory("train", self.meta,
self.data)
self.val_dataset = DemonstrationMemory("val", self.meta, self.data)
self.test_dataset = DemonstrationMemory("test", self.meta,
self.data)
self.full_dataset = DemonstrationMemory("full", self.meta,
self.data)
self.train_sampler = DemonstrationBatchSampler(self.train_dataset,
train=True)
self.val_sampler = DemonstrationBatchSampler(self.train_dataset,
train=False)
self.test_sampler = DemonstrationBatchSampler(self.test_dataset,
train=False)
self.episodic_sampler = SequentialDemonstrationSampler(
self.full_dataset)
self.train_loader = torch.utils.data.DataLoader(
self.train_dataset,
batch_sampler=self.train_sampler,
num_workers=args.cpu_workers,
pin_memory=True,
drop_last=False)
self.test_loader = torch.utils.data.DataLoader(
self.test_dataset,
batch_sampler=self.test_sampler,
num_workers=args.cpu_workers,
pin_memory=True,
drop_last=False)
self.val_loader = torch.utils.data.DataLoader(
self.val_dataset,
batch_sampler=self.val_sampler,
num_workers=args.cpu_workers,
pin_memory=True,
drop_last=False)
self.episodic_loader = torch.utils.data.DataLoader(
self.full_dataset,
sampler=self.episodic_sampler,
batch_size=self.batch,
num_workers=args.cpu_workers)
if self.l1_loss:
self.loss_fn_value = torch.nn.L1Loss(size_average=True)
self.loss_fn_q = torch.nn.L1Loss(size_average=True)
else:
self.loss_fn_value = torch.nn.MSELoss(size_average=True)
self.loss_fn_q = torch.nn.MSELoss(size_average=True)
self.loss_fn_r = torch.nn.MSELoss(size_average=True)
self.loss_fn_p = torch.nn.L1Loss(size_average=True)
if self.weight_by_expert:
self.loss_fn_beta = torch.nn.CrossEntropyLoss(reduce=False)
else:
self.loss_fn_beta = torch.nn.CrossEntropyLoss(reduce=True)
# alpha weighted sum
self.alpha_v = 1 # 1 / 0.02
self.alpha_b = 1 # 1 / 0.7
self.alpha_r = 1 # 1 / 0.7
self.alpha_p = 0 # 1 / 0.7
self.alpha_q = 1
self.model = BehavioralHotNet()
self.model.cuda()
# configure learning
# IT IS IMPORTANT TO ASSIGN MODEL TO CUDA/PARALLEL BEFORE DEFINING OPTIMIZER
self.optimizer_v = BehavioralHotAgent.set_optimizer(
self.model.parameters(), args.lr)
self.scheduler_v = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_v, self.decay)
self.optimizer_beta = BehavioralHotAgent.set_optimizer(
self.model.parameters(), args.lr_beta)
self.scheduler_beta = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_beta, self.decay)
self.optimizer_q = BehavioralHotAgent.set_optimizer(
self.model.parameters(), args.lr_q)
self.scheduler_q = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_q, self.decay)
self.optimizer_r = BehavioralHotAgent.set_optimizer(
self.model.parameters(), args.lr_r)
self.scheduler_r = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_r, self.decay)
self.optimizer_p = BehavioralHotAgent.set_optimizer(
self.model.parameters(), args.lr_p)
self.scheduler_p = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_p, self.decay)
self.episodic_evaluator = self.dummy_episodic_evaluator
actions = torch.FloatTensor(consts.hotvec_matrix) / (3**(0.5))
actions = Variable(actions, requires_grad=False).cuda()
self.actions_matrix = actions.unsqueeze(0)
def __init__(self, load_dataset=True):
super(BehavioralAgent, self).__init__()
self.actions_transform = np.array(consts.action2activation[args.game])
self.meta, self.data = preprocess_demonstrations()
if load_dataset:
# demonstration source
self.meta = divide_dataset(self.meta)
# datasets
self.train_dataset = DemonstrationMemory("train", self.meta,
self.data)
self.val_dataset = DemonstrationMemory("val", self.meta, self.data)
self.test_dataset = DemonstrationMemory("test", self.meta,
self.data)
self.full_dataset = DemonstrationMemory("full", self.meta,
self.data)
self.train_sampler = DemonstrationBatchSampler(self.train_dataset,
train=True)
self.val_sampler = DemonstrationBatchSampler(self.train_dataset,
train=False)
self.test_sampler = DemonstrationBatchSampler(self.test_dataset,
train=False)
self.episodic_sampler = SequentialDemonstrationSampler(
self.full_dataset)
self.train_loader = torch.utils.data.DataLoader(
self.train_dataset,
batch_sampler=self.train_sampler,
num_workers=args.cpu_workers,
pin_memory=True,
drop_last=False)
self.test_loader = torch.utils.data.DataLoader(
self.test_dataset,
batch_sampler=self.test_sampler,
num_workers=args.cpu_workers,
pin_memory=True,
drop_last=False)
self.val_loader = torch.utils.data.DataLoader(
self.val_dataset,
batch_sampler=self.val_sampler,
num_workers=args.cpu_workers,
pin_memory=True,
drop_last=False)
self.episodic_loader = torch.utils.data.DataLoader(
self.full_dataset,
sampler=self.episodic_sampler,
batch_size=self.batch,
num_workers=args.cpu_workers)
if self.l1_loss:
self.loss_fn_value = torch.nn.L1Loss(size_average=True)
self.individual_loss_fn_value = self.individual_loss_fn_l1
else:
self.loss_fn_value = torch.nn.MSELoss(size_average=True)
self.individual_loss_fn_value = self.individual_loss_fn_l2
self.loss_fn_r = torch.nn.MSELoss(size_average=True)
self.individual_loss_fn_r = self.individual_loss_fn_l2
self.loss_fn_q = torch.nn.L1Loss(size_average=True)
self.individual_loss_fn_q = self.individual_loss_fn_l1
self.loss_fn_p = torch.nn.L1Loss(size_average=True)
self.individual_loss_fn_p = self.individual_loss_fn_l1
# self.target_single = BehavioralNet(self.global_action_space)
# alpha weighted sum
self.alpha_v = 1 # 1 / 0.02
self.alpha_b = 1 # 1 / 0.7
self.alpha_r = 1 # 1 / 0.7
self.alpha_p = 1 # 1 / 0.7
self.alpha_q = 1
if args.deterministic: # 1 / 0.02
self.loss_fn_beta = torch.nn.L1Loss(size_average=True)
self.learn = self.learn_deterministic
self.test = self.test_deterministic
self.play = self.play_deterministic
self.play_episode = self.play_episode_deterministic
self.model_single = BehavioralNetDeterministic(
self.global_action_space)
else:
self.loss_fn_beta = torch.nn.CrossEntropyLoss()
self.learn = self.learn_stochastic
self.test = self.test_stochastic
self.play = self.play_stochastic
self.play_episode = self.play_episode_stochastic
self.model_single = BehavioralNet(self.global_action_space)
# configure learning
if self.cuda:
self.model_single = self.model_single.cuda()
# self.model = torch.nn.DataParallel(self.model_single)
self.model = self.model_single
# self.target_single = self.target_single.cuda()
# self.target = torch.nn.DataParallel(self.target_single)
else:
self.model = self.model_single
# self.target = self.target_single
# IT IS IMPORTANT TO ASSIGN MODEL TO CUDA/PARALLEL BEFORE DEFINING OPTIMIZER
self.optimizer_v = BehavioralAgent.set_optimizer(
self.model.parameters(), args.lr)
self.scheduler_v = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_v, self.decay)
self.optimizer_beta = BehavioralAgent.set_optimizer(
self.model.parameters(), args.lr_beta)
self.scheduler_beta = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_beta, self.decay)
self.optimizer_q = BehavioralAgent.set_optimizer(
self.model.parameters(), args.lr_q)
self.scheduler_q = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_q, self.decay)
self.optimizer_r = BehavioralAgent.set_optimizer(
self.model.parameters(), args.lr_r)
self.scheduler_r = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_r, self.decay)
self.optimizer_p = BehavioralAgent.set_optimizer(
self.model.parameters(), args.lr_p)
self.scheduler_p = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_p, self.decay)
self.episodic_evaluator = self.dummy_episodic_evaluator
# build the action matrix
# excitation = torch.LongTensor(consts.game_excitation_map[args.game])
excitation = torch.LongTensor(consts.excitation_map)
mask = torch.LongTensor(consts.excitation_mask[args.game])
mask_dup = mask.unsqueeze(0).repeat(consts.action_space, 1)
actions = Variable(mask_dup * excitation, requires_grad=False)
actions = Variable(excitation, requires_grad=False)
if args.cuda:
actions = actions.cuda()
self.actions_matrix = actions.unsqueeze(0)
self.actions_matrix = self.actions_matrix.repeat(1, 1, 1).float()
self.go_to_max = np.inf # 4096 * 8 * 4
self.reverse_excitation_index = consts.reverse_excitation_index
def __init__(self, load_dataset=True):
super(BehavioralEmbeddedAgent, self).__init__()
self.meta, self.data = preprocess_demonstrations()
if load_dataset:
# demonstration source
self.meta = divide_dataset(self.meta)
# datasets
self.train_dataset = DemonstrationMemory("train", self.meta,
self.data)
self.val_dataset = DemonstrationMemory("val", self.meta, self.data)
self.test_dataset = DemonstrationMemory("test", self.meta,
self.data)
self.full_dataset = DemonstrationMemory("full", self.meta,
self.data)
self.train_sampler = DemonstrationBatchSampler(self.train_dataset,
train=True)
self.val_sampler = DemonstrationBatchSampler(self.train_dataset,
train=False)
self.test_sampler = DemonstrationBatchSampler(self.test_dataset,
train=False)
self.episodic_sampler = SequentialDemonstrationSampler(
self.full_dataset)
self.train_loader = torch.utils.data.DataLoader(
self.train_dataset,
batch_sampler=self.train_sampler,
num_workers=args.cpu_workers,
pin_memory=True,
drop_last=False)
self.test_loader = torch.utils.data.DataLoader(
self.test_dataset,
batch_sampler=self.test_sampler,
num_workers=args.cpu_workers,
pin_memory=True,
drop_last=False)
self.loss_v_beta = torch.nn.KLDivLoss()
self.loss_q_beta = torch.nn.KLDivLoss()
self.loss_v_pi = torch.nn.KLDivLoss()
self.loss_q_pi = torch.nn.KLDivLoss()
self.histogram = torch.from_numpy(self.meta['histogram']).float()
w_f, w_v, w_h = calc_hist_weights(self.histogram)
w_f = torch.clamp(w_f, 0, 10).cuda()
w_v = torch.clamp(w_v, 0, 10).cuda()
w_h = torch.clamp(w_h, 0, 10).cuda()
self.loss_beta_f = torch.nn.CrossEntropyLoss(size_average=True,
weight=w_f)
self.loss_beta_v = torch.nn.CrossEntropyLoss(size_average=True,
weight=w_v)
self.loss_beta_h = torch.nn.CrossEntropyLoss(size_average=True,
weight=w_h)
self.loss_pi_f = torch.nn.CrossEntropyLoss(size_average=False)
self.loss_pi_v = torch.nn.CrossEntropyLoss(size_average=False)
self.loss_pi_h = torch.nn.CrossEntropyLoss(size_average=False)
self.behavioral_model = BehavioralDistEmbedding()
self.behavioral_model.cuda()
# actor critic setting
self.actor_critic_model = ActorCritic()
self.actor_critic_model.cuda()
self.actor_critic_target = ActorCritic()
self.actor_critic_target.cuda()
# configure learning
cnn_params = [
p[1] for p in self.behavioral_model.named_parameters()
if "cnn" in p[0]
]
emb_params = [
p[1] for p in self.behavioral_model.named_parameters()
if "emb" in p[0]
]
v_beta_params = [
p[1] for p in self.behavioral_model.named_parameters()
if "fc_v" in p[0]
]
a_beta_params = [
p[1] for p in self.behavioral_model.named_parameters()
if "fc_adv" in p[0]
]
beta_f_params = [
p[1] for p in self.behavioral_model.named_parameters()
if "fc_beta_f" in p[0]
]
beta_v_params = [
p[1] for p in self.behavioral_model.named_parameters()
if "fc_beta_v" in p[0]
]
beta_h_params = [
p[1] for p in self.behavioral_model.named_parameters()
if "fc_beta_h" in p[0]
]
v_pi_params = [
p[1] for p in self.actor_critic_model.named_parameters()
if "critic_v" in p[0]
]
a_pi_params = [
p[1] for p in self.actor_critic_model.named_parameters()
if "critic_adv" in p[0]
]
pi_f_params = [
p[1] for p in self.actor_critic_model.named_parameters()
if "fc_actor_f" in p[0]
]
pi_v_params = [
p[1] for p in self.actor_critic_model.named_parameters()
if "fc_actor_v" in p[0]
]
pi_h_params = [
p[1] for p in self.actor_critic_model.named_parameters()
if "fc_actor_h" in p[0]
]
# IT IS IMPORTANT TO ASSIGN MODEL TO CUDA/PARALLEL BEFORE DEFINING OPTIMIZER
self.optimizer_critic_v = BehavioralEmbeddedAgent.set_optimizer(
v_pi_params, 0.0008)
self.scheduler_critic_v = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_critic_v, self.decay)
self.optimizer_critic_q = BehavioralEmbeddedAgent.set_optimizer(
v_pi_params + a_pi_params, 0.0008)
self.scheduler_critic_q = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_critic_q, self.decay)
self.optimizer_v_beta = BehavioralEmbeddedAgent.set_optimizer(
cnn_params + emb_params + v_beta_params, 0.0008)
self.scheduler_v_beta = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_v_beta, self.decay)
self.optimizer_q_beta = BehavioralEmbeddedAgent.set_optimizer(
cnn_params + emb_params + v_beta_params + a_beta_params, 0.0008)
self.scheduler_q_beta = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_q_beta, self.decay)
self.optimizer_beta_f = BehavioralEmbeddedAgent.set_optimizer(
cnn_params + emb_params + beta_f_params, 0.0008)
self.scheduler_beta_f = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_beta_f, self.decay)
self.optimizer_beta_v = BehavioralEmbeddedAgent.set_optimizer(
cnn_params + emb_params + beta_v_params, 0.0008)
self.scheduler_beta_v = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_beta_v, self.decay)
self.optimizer_beta_h = BehavioralEmbeddedAgent.set_optimizer(
cnn_params + emb_params + beta_h_params, 0.0008)
self.scheduler_beta_h = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_beta_h, self.decay)
self.optimizer_pi_f = BehavioralEmbeddedAgent.set_optimizer(
pi_f_params, 0.0008)
self.scheduler_pi_f = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_pi_f, self.decay)
self.optimizer_pi_v = BehavioralEmbeddedAgent.set_optimizer(
pi_v_params, 0.0008)
self.scheduler_pi_v = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_pi_v, self.decay)
self.optimizer_pi_h = BehavioralEmbeddedAgent.set_optimizer(
pi_h_params, 0.0008)
self.scheduler_pi_h = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer_pi_h, self.decay)
actions = torch.LongTensor(consts.hotvec_matrix).cuda()
self.actions_matrix = actions.unsqueeze(0)
self.q_bins = consts.q_bins[args.game][:-1] / self.meta['avg_score']
# the long bins are already normalized
self.v_bins = consts.v_bins[args.game][:-1] / self.meta['avg_score']
self.q_bins_torch = Variable(torch.from_numpy(
consts.q_bins[args.game] / self.meta['avg_score']),
requires_grad=False).cuda()
self.v_bins_torch = Variable(torch.from_numpy(
consts.v_bins[args.game] / self.meta['avg_score']),
requires_grad=False).cuda()
self.batch_range = np.arange(self.batch)
self.zero = Variable(torch.zeros(1))