def sac_opp(
global_ac,
global_ac_targ,
global_cpc,
rank,
T,
E,
args,
scores,
wins,
buffer,
device=None,
tensorboard_dir=None,
):
torch.manual_seed(args.seed + rank)
np.random.seed(args.seed + rank)
# writer = GlobalSummaryWriter.getSummaryWriter()
tensorboard_dir = os.path.join(tensorboard_dir, str(rank))
if not os.path.exists(tensorboard_dir):
os.makedirs(tensorboard_dir)
writer = SummaryWriter(log_dir=tensorboard_dir)
if args.exp_name == "test":
env = gym.make("CartPole-v0")
elif args.non_station:
env = make_ftg_ram_nonstation(args.env,
p2_list=args.list,
total_episode=args.station_rounds,
stable=args.stable)
else:
env = make_ftg_ram(args.env, p2=args.p2)
obs_dim = env.observation_space.shape[0]
act_dim = env.action_space.n
print("set up child process env")
local_ac = MLPActorCritic(obs_dim + args.c_dim, act_dim,
**dict(hidden_sizes=[args.hid] *
args.l)).to(device)
local_ac.load_state_dict(global_ac.state_dict())
print("local ac load global ac")
# Freeze target networks with respect to optimizers (only update via polyak averaging)
# Async Version
for p in global_ac_targ.parameters():
p.requires_grad = False
# Experience buffer
if args.cpc:
replay_buffer = ReplayBufferOppo(obs_dim=obs_dim,
max_size=args.replay_size,
encoder=global_cpc)
else:
replay_buffer = ReplayBuffer(obs_dim=obs_dim, size=args.replay_size)
# Entropy Tuning
target_entropy = -torch.prod(
torch.Tensor(env.action_space.shape).to(
device)).item() # heuristic value from the paper
alpha = max(local_ac.log_alpha.exp().item(),
args.min_alpha) if not args.fix_alpha else args.min_alpha
# Set up optimizers for policy and q-function
# Async Version
pi_optimizer = Adam(global_ac.pi.parameters(), lr=args.lr, eps=1e-4)
q1_optimizer = Adam(global_ac.q1.parameters(), lr=args.lr, eps=1e-4)
q2_optimizer = Adam(global_ac.q2.parameters(), lr=args.lr, eps=1e-4)
cpc_optimizer = Adam(global_cpc.parameters(), lr=args.lr, eps=1e-4)
alpha_optim = Adam([global_ac.log_alpha], lr=args.lr, eps=1e-4)
# Prepare for interaction with environment
o, ep_ret, ep_len = env.reset(), 0, 0
if args.cpc:
c_hidden = global_cpc.init_hidden(1, args.c_dim, use_gpu=args.cuda)
c1, c_hidden = global_cpc.predict(o, c_hidden)
assert len(c1.shape) == 3
c1 = c1.flatten().cpu().numpy()
all_embeddings = []
meta = []
trajectory = list()
p2 = env.p2
p2_list = [str(p2)]
discard = False
uncertainties = []
local_t, local_e = 0, 0
t = T.value()
e = E.value()
glod_input = list()
glod_target = list()
# Main loop: collect experience in env and update/log each epoch
while e <= args.episode:
with torch.no_grad():
# Until start_steps have elapsed, randomly sample actions
# from a uniform distribution for better exploration. Afterwards,
# use the learned policy.
if t > args.start_steps:
if args.cpc:
a = local_ac.get_action(np.concatenate((o, c1), axis=0),
device=device)
a_prob = local_ac.act(
torch.as_tensor(np.expand_dims(np.concatenate((o, c1),
axis=0),
axis=0),
dtype=torch.float32,
device=device))
else:
a = local_ac.get_action(o, greedy=True, device=device)
a_prob = local_ac.act(
torch.as_tensor(np.expand_dims(o, axis=0),
dtype=torch.float32,
device=device))
else:
a = env.action_space.sample()
a_prob = local_ac.act(
torch.as_tensor(np.expand_dims(o, axis=0),
dtype=torch.float32,
device=device))
uncertainty = ood_scores(a_prob).item()
# Step the env
o2, r, d, info = env.step(a)
if info.get('no_data_receive', False):
discard = True
ep_ret += r
ep_len += 1
# Ignore the "done" signal if it comes from hitting the time
# horizon (that is, when it's an artificial terminal signal
# that isn't based on the agent's state)
d = False if (ep_len == args.max_ep_len) or discard else d
glod_input.append(o), glod_target.append(a)
if args.cpc:
# changed the trace structure for further analysis
c2, c_hidden = global_cpc.predict(o2, c_hidden)
assert len(c2.shape) == 3
c2 = c2.flatten().cpu().numpy()
replay_buffer.store(np.concatenate((o, c1), axis=0), a, r,
np.concatenate((o2, c2), axis=0), d)
trajectory.append([o, a, r, o2, d, c1, c2, ep_len])
all_embeddings.append(c1)
meta.append([env.p2, local_e, ep_len, r, a, uncertainty])
c1 = c2
trajectory.append([o, a, r, o2, d, c1, c2])
else:
replay_buffer.store(o, a, r, o2, d)
# Super critical, easy to overlook step: make sure to update
# most recent observation!
o = o2
T.increment()
t = T.value()
local_t += 1
# End of trajectory handling
if d or (ep_len == args.max_ep_len) or discard:
replay_buffer.store(trajectory)
E.increment()
e = E.value()
local_e += 1
# logger.store(EpRet=ep_ret, EpLen=ep_len)
if info.get('win', False):
wins.append(1)
else:
wins.append(0)
scores.append(ep_ret)
m_score = np.mean(scores[-100:])
win_rate = np.mean(wins[-100:])
print(
"Process {}, opponent:{}, # of global_episode :{}, # of global_steps :{}, round score: {}, mean score : {:.1f}, win_rate:{}, steps: {}, alpha: {}"
.format(rank, args.p2, e, t, ep_ret, m_score, win_rate, ep_len,
alpha))
writer.add_scalar("metrics/round_score", ep_ret, e)
writer.add_scalar("metrics/mean_score", m_score.item(), e)
writer.add_scalar("metrics/win_rate", win_rate.item(), e)
writer.add_scalar("metrics/round_step", ep_len, e)
writer.add_scalar("metrics/alpha", alpha, e)
# CPC update handing
if local_e > args.batch_size and local_e % args.update_every == 0 and args.cpc:
data, indexes, min_len = replay_buffer.sample_traj(
args.batch_size)
global_cpc.train()
cpc_optimizer.zero_grad()
c_hidden = global_cpc.init_hidden(len(data),
args.c_dim,
use_gpu=args.cuda)
acc, loss, latents = global_cpc(data, c_hidden)
replay_buffer.update_latent(indexes, min_len, latents.detach())
loss.backward()
# add gradient clipping
nn.utils.clip_grad_norm_(global_cpc.parameters(), 20)
cpc_optimizer.step()
writer.add_scalar("training/acc", acc, e)
writer.add_scalar("training/cpc_loss", loss.detach().item(), e)
all_embeddings = np.array(all_embeddings)
writer.add_embedding(mat=all_embeddings,
metadata=meta,
metadata_header=[
"opponent", "round", "step", "reward",
"action", "uncertainty"
])
c_hidden = global_cpc.init_hidden(1,
args.c_dim,
use_gpu=args.cuda)
o, ep_ret, ep_len = env.reset(), 0, 0
trajectory = list()
discard = False
# OOD update stage
if (t >= args.ood_update_step and local_t % args.ood_update_step == 0
or replay_buffer.is_full()) and args.ood:
# used all the data collected from the last args.ood_update_steps as the train data
print("Conduct OOD updating")
ood_train = (glod_input, glod_target)
glod_model = convert_to_glod(global_ac.pi,
train_loader=ood_train,
hidden_dim=args.hid,
act_dim=act_dim,
device=device)
glod_scores = retrieve_scores(
glod_model,
replay_buffer.obs_buf[:replay_buffer.size],
device=device,
k=args.ood_K)
glod_scores = glod_scores.detach().cpu().numpy()
print(len(glod_scores))
writer.add_histogram(values=glod_scores,
max_bins=300,
global_step=local_t,
tag="OOD")
drop_points = np.percentile(
a=glod_scores, q=[args.ood_drop_lower, args.ood_drop_upper])
lower, upper = drop_points[0], drop_points[1]
print(lower, upper)
mask = np.logical_and((glod_scores >= lower),
(glod_scores <= upper))
reserved_indexes = np.argwhere(mask).flatten()
print(len(reserved_indexes))
if len(reserved_indexes) > 0:
replay_buffer.ood_drop(reserved_indexes)
glod_input = list()
glod_target = list()
# SAC Update handling
if local_t >= args.update_after and local_t % args.update_every == 0:
for j in range(args.update_every):
batch = replay_buffer.sample_trans(batch_size=args.batch_size,
device=device)
# First run one gradient descent step for Q1 and Q2
q1_optimizer.zero_grad()
q2_optimizer.zero_grad()
loss_q = local_ac.compute_loss_q(batch, global_ac_targ,
args.gamma, alpha)
loss_q.backward()
# Next run one gradient descent step for pi.
pi_optimizer.zero_grad()
loss_pi, entropy = local_ac.compute_loss_pi(batch, alpha)
loss_pi.backward()
alpha_optim.zero_grad()
alpha_loss = -(local_ac.log_alpha *
(entropy + target_entropy).detach()).mean()
alpha_loss.backward(retain_graph=False)
alpha = max(
local_ac.log_alpha.exp().item(),
args.min_alpha) if not args.fix_alpha else args.min_alpha
nn.utils.clip_grad_norm_(local_ac.parameters(), 20)
for global_param, local_param in zip(global_ac.parameters(),
local_ac.parameters()):
global_param._grad = local_param.grad
pi_optimizer.step()
q1_optimizer.step()
q2_optimizer.step()
alpha_optim.step()
state_dict = global_ac.state_dict()
local_ac.load_state_dict(state_dict)
# Finally, update target networks by polyak averaging.
with torch.no_grad():
for p, p_targ in zip(global_ac.parameters(),
global_ac_targ.parameters()):
p_targ.data.copy_((1 - args.polyak) * p.data +
args.polyak * p_targ.data)
writer.add_scalar("training/pi_loss",
loss_pi.detach().item(), t)
writer.add_scalar("training/q_loss", loss_q.detach().item(), t)
writer.add_scalar("training/alpha_loss",
alpha_loss.detach().item(), t)
writer.add_scalar("training/entropy",
entropy.detach().mean().item(), t)
if t % args.save_freq == 0 and t > 0:
torch.save(
global_ac.state_dict(),
os.path.join(args.save_dir, args.exp_name, args.model_para))
torch.save(
global_cpc.state_dict(),
os.path.join(args.save_dir, args.exp_name, args.cpc_para))
state_dict_trans(
global_ac.state_dict(),
os.path.join(args.save_dir, args.exp_name, args.numpy_para))
torch.save((e, t, list(scores), list(wins)),
os.path.join(args.save_dir, args.exp_name,
args.train_indicator))
print("Saving model at episode:{}".format(t))
# First run one gradient descent step for Q1 and Q2
q1_optimizer.zero_grad()
q2_optimizer.zero_grad()
loss_q = global_ac.compute_loss_q(batch, global_ac_targ,
args.gamma, alpha)
loss_q.backward()
nn.utils.clip_grad_norm_(global_ac.parameters(),
max_norm=20,
norm_type=2)
q1_optimizer.step()
q2_optimizer.step()
# Next run one gradient descent step for pi.
pi_optimizer.zero_grad()
loss_pi, entropy = global_ac.compute_loss_pi(batch, alpha)
loss_pi.backward()
nn.utils.clip_grad_norm_(global_ac.parameters(),
max_norm=20,
norm_type=2)
pi_optimizer.step()
alpha_optim.zero_grad()
alpha_loss = -(global_ac.log_alpha *
(entropy + target_entropy).detach()).mean()
alpha_loss.backward(retain_graph=False)
nn.utils.clip_grad_norm_(global_ac.parameters(),
max_norm=20,
norm_type=2)
alpha = max(
global_ac.log_alpha.exp().item(),
def sac(
global_ac,
global_ac_targ,
rank,
T,
E,
args,
scores,
wins,
buffer_q,
device=None,
tensorboard_dir=None,
):
torch.manual_seed(args.seed + rank)
np.random.seed(args.seed + rank)
# writer = GlobalSummaryWriter.getSummaryWriter()
tensorboard_dir = os.path.join(tensorboard_dir, str(rank))
if not os.path.exists(tensorboard_dir):
os.makedirs(tensorboard_dir)
writer = SummaryWriter(log_dir=tensorboard_dir)
# if args.exp_name == "test":
# env = gym.make("CartPole-v0")
# elif args.non_station:
# env = make_ftg_ram_nonstation(args.env, p2_list=args.list, total_episode=args.station_rounds,stable=args.stable)
# else:
# env = make_ftg_ram(args.env, p2=args.p2)
# obs_dim = env.observation_space.shape[0]
# act_dim = env.action_space.n
env = Soccer()
# env = gym.make("CartPole-v0")
obs_dim = env.n_features
act_dim = env.n_actions
print("set up child process env")
local_ac = MLPActorCritic(obs_dim, act_dim,
**dict(hidden_sizes=[args.hid] *
args.l)).to(device)
state_dict = global_ac.state_dict()
local_ac.load_state_dict(state_dict)
print("local ac load global ac")
# Freeze target networks with respect to optimizers (only update via polyak averaging)
# Async Version
for p in global_ac_targ.parameters():
p.requires_grad = False
# Experience buffer
replay_buffer = ReplayBuffer(obs_dim=obs_dim, size=args.replay_size)
training_buffer = ReplayBuffer(obs_dim=obs_dim, size=args.replay_size)
# Entropy Tuning
target_entropy = -np.log((1.0 / act_dim)) * 0.5
alpha = max(local_ac.log_alpha.exp().item(),
args.min_alpha) if not args.fix_alpha else args.min_alpha
# Set up optimizers for policy and q-function
# Async Version
pi_optimizer = Adam(global_ac.pi.parameters(), lr=args.lr, eps=1e-4)
q1_optimizer = Adam(global_ac.q1.parameters(), lr=args.lr, eps=1e-4)
q2_optimizer = Adam(global_ac.q2.parameters(), lr=args.lr, eps=1e-4)
alpha_optim = Adam([global_ac.log_alpha], lr=args.lr, eps=1e-4)
# Prepare for interaction with environment
o, ep_ret, ep_len = env.reset(), 0, 0
discard = False
glod_model = None
glod_lower = None
glod_upper = None
last_updated = 0
saved_e = 0
t = T.value()
e = E.value()
local_t, local_e = 0, 0
# Main loop: collect experience in env and update/log each epoch
while e <= args.episode:
# Until start_steps have elapsed, randomly sample actions
# from a uniform distribution for better exploration. Afterwards,
# use the learned policy.
with torch.no_grad():
a = local_ac.get_action(o, device=device)
if hasattr(env, 'p2'):
p2 = env.p2
else:
p2 = None
# Step the env
o2, r, d, info = env.step(a, np.random.randint(act_dim))
env.render()
if info.get('no_data_receive', False):
discard = True
ep_ret += r
ep_len += 1
# Ignore the "done" signal if it comes from hitting the time
# horizon (that is, when it's an artificial terminal signal
# that isn't based on the agent's state)
# d = False if (ep_len == args.max_ep_len) or discard else d
# Store experience to replay buffer
if glod_model is None or not args.ood:
replay_buffer.store(o, a, r, o2, d, str(p2))
training_buffer.store(o, a, r, o2, d, str(p2))
else:
obs_glod_score = retrieve_scores(glod_model,
np.expand_dims(o, axis=0),
device=torch.device("cpu"),
k=args.ood_K)
if glod_lower <= obs_glod_score <= glod_upper:
training_buffer.store(o, a, r, o2, d, str(p2))
replay_buffer.store(o, a, r, o2, d, str(p2))
# Super critical, easy to overlook step: make sure to update
# most recent observation!
o = o2
T.increment()
t = T.value()
local_t += 1
# End of trajectory handling
if d or (ep_len == args.max_ep_len) or discard:
E.increment()
e = E.value()
local_e += 1
# logger.store(EpRet=ep_ret, EpLen=ep_len)
if info.get('win', False):
wins.append(1)
else:
wins.append(0)
scores.append(ep_ret)
m_score = np.mean(scores[-100:])
win_rate = np.mean(wins[-100:])
print(
"Process {}, opponent:{}, # of global_episode :{}, # of global_steps :{}, round score: {}, mean score : {:.1f}, win_rate:{}, steps: {}, alpha: {}"
.format(rank, args.p2, e, t, ep_ret, m_score, win_rate, ep_len,
alpha))
writer.add_scalar("metrics/round_score", ep_ret, e)
writer.add_scalar("metrics/mean_score", m_score.item(), e)
writer.add_scalar("metrics/win_rate", win_rate.item(), e)
writer.add_scalar("metrics/round_step", ep_len, e)
writer.add_scalar("metrics/alpha", alpha, e)
o, ep_ret, ep_len = env.reset(), 0, 0
discard = False
# OOD update stage, can only use CPU as the GPU memory can not hold so much data
if local_e >= args.ood_starts and local_e % args.ood_update_rounds == 0 and args.ood and local_e != last_updated:
print("OOD updating at rounds {}".format(e))
print("Replay Buffer Size: {}, Training Buffer Size: {}".format(
replay_buffer.size, training_buffer.size))
glod_idxs = np.random.randint(0,
training_buffer.size,
size=int(training_buffer.size *
args.ood_train_per))
glod_input = training_buffer.obs_buf[glod_idxs]
glod_target = training_buffer.act_buf[glod_idxs]
ood_train = (glod_input, glod_target)
glod_model = deepcopy(global_ac.pi).cpu()
glod_model = convert_to_glod(glod_model,
train_loader=ood_train,
hidden_dim=args.hid,
act_dim=act_dim,
device=torch.device("cpu"))
training_buffer = deepcopy(replay_buffer)
glod_scores = retrieve_scores(
glod_model,
replay_buffer.obs_buf[:training_buffer.size],
device=torch.device("cpu"),
k=args.ood_K)
glod_scores = glod_scores.detach().cpu().numpy()
glod_p2 = training_buffer.p2_buf[:training_buffer.size]
drop_points = np.percentile(
a=glod_scores, q=[args.ood_drop_lower, args.ood_drop_upper])
glod_lower, glod_upper = drop_points[0], drop_points[1]
mask = np.logical_and((glod_scores >= glod_lower),
(glod_scores <= glod_upper))
reserved_indexes = np.argwhere(mask).flatten()
if len(reserved_indexes) > 0:
training_buffer.ood_drop(reserved_indexes)
writer.add_histogram(values=glod_scores,
max_bins=300,
global_step=local_e,
tag="OOD")
print("Replay Buffer Size: {}, Training Buffer Size: {}".format(
replay_buffer.size, training_buffer.size))
torch.save(
(glod_scores, replay_buffer.p2_buf[:replay_buffer.size]),
os.path.join(args.save_dir, args.exp_name,
"glod_info_{}_{}".format(rank, local_e)))
last_updated = local_e
# SAC Update handling
if local_e >= args.update_after and local_t % args.update_every == 0:
for j in range(args.update_every):
batch = training_buffer.sample_trans(args.batch_size,
device=device)
# First run one gradient descent step for Q1 and Q2
q1_optimizer.zero_grad()
q2_optimizer.zero_grad()
loss_q = local_ac.compute_loss_q(batch, global_ac_targ,
args.gamma, alpha)
loss_q.backward()
nn.utils.clip_grad_norm_(global_ac.parameters(),
max_norm=20,
norm_type=2)
q1_optimizer.step()
q2_optimizer.step()
# Next run one gradient descent step for pi.
pi_optimizer.zero_grad()
loss_pi, entropy = local_ac.compute_loss_pi(batch, alpha)
loss_pi.backward()
nn.utils.clip_grad_norm_(global_ac.parameters(),
max_norm=20,
norm_type=2)
pi_optimizer.step()
alpha_optim.zero_grad()
alpha_loss = -(local_ac.log_alpha *
(entropy + target_entropy).detach()).mean()
alpha_loss.backward(retain_graph=False)
alpha = max(
local_ac.log_alpha.exp().item(),
args.min_alpha) if not args.fix_alpha else args.min_alpha
nn.utils.clip_grad_norm_(global_ac.parameters(),
max_norm=20,
norm_type=2)
alpha_optim.step()
for global_param, local_param in zip(global_ac.parameters(),
local_ac.parameters()):
global_param._grad = local_param.grad
state_dict = global_ac.state_dict()
local_ac.load_state_dict(state_dict)
# Finally, update target networks by polyak averaging.
with torch.no_grad():
for p, p_targ in zip(global_ac.parameters(),
global_ac_targ.parameters()):
p_targ.data.copy_((1 - args.polyak) * p.data +
args.polyak * p_targ.data)
writer.add_scalar("training/pi_loss",
loss_pi.detach().item(), t)
writer.add_scalar("training/q_loss", loss_q.detach().item(), t)
writer.add_scalar("training/alpha_loss",
alpha_loss.detach().item(), t)
writer.add_scalar("training/entropy",
entropy.detach().mean().item(), t)
if e % args.save_freq == 0 and e > 0 and e != saved_e:
torch.save(
global_ac.state_dict(),
os.path.join(args.save_dir, args.exp_name,
"model_torch_{}".format(e)))
state_dict_trans(
global_ac.state_dict(),
os.path.join(args.save_dir, args.exp_name,
"model_numpy_{}".format(e)))
torch.save((e, t, list(scores), list(wins)),
os.path.join(args.save_dir, args.exp_name,
"model_data_{}".format(e)))
print("Saving model at episode:{}".format(t))
saved_e = e