class Trainer:
def __init__(
self,
capacity_per_level=500000,
warmup_steps=100000,
n_frames=4,
n_atoms=51,
v_min=-1,
v_max=0,
gamma=.99,
device='cuda',
batch_size=48,
lr=0.0000625 * 2,
lr_decay=0.99,
update_target_net_every=25000,
train_every=6,
frame_skip=4,
disable_noisy_after=2000000,
super_hexagon_path='C:\\Program Files (x86)\\Steam\\steamapps\\common\\Super Hexagon\\superhexagon.exe',
run_afap=True):
# training objects
self.memory_buffer = MemoryBuffer(
capacity_per_level,
SuperHexagonInterface.n_levels,
n_frames,
SuperHexagonInterface.frame_size,
SuperHexagonInterface.frame_size_cropped,
gamma,
device=device)
self.net = Network(n_frames, SuperHexagonInterface.n_actions,
n_atoms).to(device)
self.target_net = Network(n_frames, SuperHexagonInterface.n_actions,
n_atoms).to(device)
self.target_net.load_state_dict(self.net.state_dict())
self.optimizer = torch.optim.Adam(self.net.parameters(),
lr=lr,
eps=1.5e-4)
self.lr_scheduler = torch.optim.lr_scheduler.LambdaLR(
self.optimizer, ExpLrDecay(lr_decay, min_factor=.1))
# parameters
self.batch_size = batch_size
self.update_target_net_every = update_target_net_every
self.train_every = train_every
self.frame_skip = frame_skip
self.disable_noisy_after = disable_noisy_after
self.warmup_steps = warmup_steps
self.gamma = gamma
self.device = device
# parameters for distributional
self.n_atoms = n_atoms
self.v_min = v_min
self.v_max = v_max
self.delta_z = (v_max - v_min) / (n_atoms - 1)
self.support = torch.linspace(v_min,
v_max,
n_atoms,
dtype=torch.float,
device=device)
self.offset = torch.arange(0,
batch_size * n_atoms,
n_atoms,
device=device).view(-1, 1)
self.m = torch.empty((batch_size, n_atoms), device=device)
# debug and logging stuff
self.list_steps_alive = [[]
for _ in range(SuperHexagonInterface.n_levels)
]
self.longest_run = [(0, 0)] * SuperHexagonInterface.n_levels
self.total_simulated_steps = [0] * SuperHexagonInterface.n_levels
self.losses = []
self.kls = []
self.times = []
self.iteration = 0
self.super_hexagon_path = super_hexagon_path
self.run_afap = run_afap
def warmup(self, game, log_every):
t = True
for i in range(1, self.warmup_steps + 1):
if i % log_every == 0:
print('Warmup', i)
if t:
self.total_simulated_steps[game.level] += game.simulated_steps
if self.total_simulated_steps[
game.level] > self.total_simulated_steps[game.level -
1]:
game.select_level((game.level + 1) % 6)
f, fc = game.reset()
self.memory_buffer.insert_first(game.level, f, fc)
a = np.random.randint(0, 3)
(f, fc), r, t = game.step(a)
self.memory_buffer.insert(game.level, a, r, t, f, fc)
return t
def train(
self,
save_every=50000,
save_name='trainer',
log_every=1000,
):
game = SuperHexagonInterface(self.frame_skip,
self.super_hexagon_path,
run_afap=self.run_afap,
allow_game_restart=True)
# if trainer was loaded, select the level that was played the least
if any(x != 0 for x in self.total_simulated_steps):
game.select_level(np.argmin(self.total_simulated_steps).item())
# init state
f, fc = np.zeros(game.frame_size,
dtype=np.bool), np.zeros(game.frame_size_cropped,
dtype=np.bool)
sf, sfc = torch.zeros((1, 4, *game.frame_size),
device=self.device), torch.zeros(
(1, 4, *game.frame_size_cropped),
device=self.device)
t = True
# run warmup is necessary
if self.iteration == 0:
if os.path.exists('warmup_buffer.npz'):
self.memory_buffer.load_warmup('warmup_buffer.npz')
else:
t = self.warmup(game, log_every)
self.memory_buffer.save_warmup('warmup_buffer.npz')
# trainings loop
last_time = time()
save_when_terminal = False
while True:
self.iteration += 1
# disable noisy
if self.iteration == self.disable_noisy_after:
self.net.eval()
self.target_net.eval()
# log
if self.iteration % log_every == 0 and all(
len(l) > 0 for l in self.list_steps_alive):
print(
f'{self.iteration} | '
f'{[round(np.mean(np.array(l[-100:])[:, 1]) / 60, 2) for l in self.list_steps_alive]}s | '
f'{[round(r[1] / 60, 2) for r in self.longest_run]}s | '
f'{self.total_simulated_steps} | '
f'{time() - last_time:.2f}s | '
f'{np.mean(self.losses[-log_every:])} | '
f'{np.mean(self.kls[-log_every:])} | '
f'{self.lr_scheduler.get_last_lr()[0]} | '
f'{game.level}')
# indicate that the trainer should be saved the next time the agent dies
if self.iteration % save_every == 0:
save_when_terminal = True
# update target net
if self.iteration % self.update_target_net_every == 0:
self.lr_scheduler.step()
self.target_net.load_state_dict(self.net.state_dict())
# if terminal
if t:
# select next level if this level was played at least as long as the previous level
if self.total_simulated_steps[
game.level] > self.total_simulated_steps[game.level -
1]:
game.select_level((game.level + 1) % 6)
f, fc = game.reset()
self.memory_buffer.insert_first(game.level, f, fc)
sf.zero_()
sfc.zero_()
# update state
sf[0, 1:] = sf[0, :-1].clone()
sfc[0, 1:] = sfc[0, :-1].clone()
sf[0, 0] = torch.from_numpy(f).to(self.device)
sfc[0, 0] = torch.from_numpy(fc).to(self.device)
# train
if self.iteration % self.train_every == 0:
loss, kl = self.train_batch()
self.losses.append(loss)
self.kls.append(kl)
# act
with torch.no_grad():
self.net.reset_noise()
a = (self.net(sf, sfc) *
self.support).sum(dim=2).argmax(dim=1).item()
(f, fc), r, t = game.step(a)
self.memory_buffer.insert(game.level, a, r, t, f, fc)
# if terminal
if t:
if game.steps_alive > self.longest_run[game.level][1]:
self.longest_run[game.level] = (self.iteration,
game.steps_alive)
self.list_steps_alive[game.level].append(
(self.iteration, game.steps_alive))
self.total_simulated_steps[game.level] += game.simulated_steps
self.times.append(time() - last_time)
if save_when_terminal:
print('saving...')
for _ in range(60):
game.game.step(False)
self.save(save_name)
for _ in range(60):
game.game.step(False)
save_when_terminal = False
def train_batch(self):
# sample minibatch
f, fc, a, r, t, f1, fc1 = self.memory_buffer.make_batch(
self.batch_size)
# compute target q distribution
with torch.no_grad():
self.target_net.reset_noise()
qdn = self.target_net(f1, fc1)
an = (qdn * self.support).sum(dim=2).argmax(dim=1)
Tz = (r.unsqueeze(1) +
t.logical_not().unsqueeze(1) * self.gamma * self.support).clamp_(
self.v_min, self.v_max)
b = (Tz - self.v_min) / self.delta_z
l = b.floor().long()
u = b.ceil().long()
l[(u > 0) & (l == u)] -= 1
u[(l == u)] += 1
vdn = qdn.gather(
1,
an.view(-1, 1,
1).expand(self.batch_size, -1,
self.n_atoms)).view(self.batch_size,
self.n_atoms)
self.m.zero_()
self.m.view(-1).index_add_(0, (l + self.offset).view(-1),
(vdn * (u - b)).view(-1))
self.m.view(-1).index_add_(0, (u + self.offset).view(-1),
(vdn * (b - l)).view(-1))
# forward and backward pass
qld = self.net(f, fc, log=True)
vld = qld.gather(
1,
a.view(-1, 1,
1).expand(self.batch_size, -1,
self.n_atoms)).view(self.batch_size, self.n_atoms)
loss = -torch.sum(self.m * vld, dim=1).mean()
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
kl = F.kl_div(vld.detach(), self.m, reduction='batchmean')
return loss.detach().item(), kl.item()
def save(self, file_name='trainer'):
# first backup the last save file
# in case anything goes wrong
file_name_backup = file_name + '_backup'
if os.path.exists(file_name):
os.rename(file_name, file_name_backup)
# save this object
with open(file_name, 'wb') as f:
pickle.dump(self, f)
# remove backup if nothing went wrong
if os.path.exists(file_name_backup):
os.remove(file_name_backup)
@staticmethod
def load(file_name='trainer'):
with open(file_name, 'rb') as f:
ret = pickle.load(f)
assert ret.memory_buffer.last_was_terminal
return ret
frame_skip = 4
log_every = 1000
n_atoms = 51
# setup
fp, fcp = np.zeros(
(1, n_frames, *SuperHexagonInterface.frame_size),
dtype=np.bool), np.zeros(
(1, n_frames, *SuperHexagonInterface.frame_size_cropped),
dtype=np.bool)
support = np.linspace(-1, 0, n_atoms)
net = Network(n_frames, SuperHexagonInterface.n_actions,
n_atoms).to(device)
net.load_state_dict(torch.load(net_path, map_location=device))
net.eval()
game = SuperHexagonInterface(frame_skip=frame_skip, run_afap=False)
game.select_level(level)
list_times_alive = []
f, fc = game.reset()
# helper function
def to_torch_tensor(x):
return torch.from_numpy(x).to(device).float()
# global no_grad
torch.set_grad_enabled(False)
# run actor
