Commit bbe386a5 authored by Chakraborty's avatar Chakraborty
Browse files
parents 8f996f60 c63cee77
......@@ -228,6 +228,48 @@ class CustomTorchPolicy(TorchPolicy):
ent_coef, vf_coef,
obs, returns, actions, values, logp_actions_old, advs):
if not self.config['pi_phase_mixed_precision']:
loss, vf_loss = self._calc_pi_vf_loss(apply_grad, num_accumulate,
cliprange, vfcliprange, max_grad_norm,
ent_coef, vf_coef,
obs, returns, actions, values, logp_actions_old, advs)
loss.backward()
vf_loss.backward()
if apply_grad:
self.optimizer.step()
self.optimizer.zero_grad()
if not self.config['single_optimizer']:
self.value_optimizer.step()
self.value_optimizer.zero_grad()
else:
with autocast():
loss, vf_loss = self._calc_pi_vf_loss(apply_grad, num_accumulate,
cliprange, vfcliprange, max_grad_norm,
ent_coef, vf_coef,
obs, returns, actions, values, logp_actions_old, advs)
self.amp_scaler.scale(loss).backward(retain_graph=True)
self.amp_scaler.scale(vf_loss).backward()
if apply_grad:
self.amp_scaler.step(self.optimizer)
if not self.config['single_optimizer']:
self.amp_scaler.step(self.value_optimizer)
self.amp_scaler.update()
self.optimizer.zero_grad()
if not self.config['single_optimizer']:
self.value_optimizer.zero_grad()
def _calc_pi_vf_loss(self, apply_grad, num_accumulate,
cliprange, vfcliprange, max_grad_norm,
ent_coef, vf_coef,
obs, returns, actions, values, logp_actions_old, advs):
vpred, pi_logits = self.model.vf_pi(obs, ret_numpy=False, no_grad=False, to_torch=False)
pd = self.make_distr(pi_logits)
logp_actions = pd.log_prob(actions[...,None]).squeeze(1)
......@@ -244,16 +286,7 @@ class CustomTorchPolicy(TorchPolicy):
loss = loss / num_accumulate
vf_loss = vf_loss / num_accumulate
loss.backward()
vf_loss.backward()
if apply_grad:
self.optimizer.step()
self.optimizer.zero_grad()
if not self.config['single_optimizer']:
self.value_optimizer.step()
self.value_optimizer.zero_grad()
return loss, vf_loss
def aux_train(self):
nbatch_train = self.mem_limited_batch_size
......@@ -295,6 +328,7 @@ class CustomTorchPolicy(TorchPolicy):
else:
self.optimizer.step()
else:
with autocast():
loss, vf_loss = self._aux_calc_loss(obs_in, target_vf, target_pi)
......
......@@ -97,6 +97,7 @@ DEFAULT_CONFIG = with_common_config({
"aux_phase_mixed_precision": False,
"single_optimizer": False,
"max_time": 7200,
"pi_phase_mixed_precision": False,
})
# __sphinx_doc_end__
# yapf: enable
......
......@@ -45,9 +45,9 @@ procgen-ppo:
no_done_at_end: False
# Custom switches
skips: 2
n_pi: 16
num_retunes: 14
skips: 6
n_pi: 10
num_retunes: 16
retune_epochs: 6
standardize_rewards: True
aux_mbsize: 4
......@@ -62,15 +62,16 @@ procgen-ppo:
aux_phase_mixed_precision: True
single_optimizer: True
max_time: 7200
pi_phase_mixed_precision: True
adaptive_gamma: False
final_lr: 5.0e-5
final_lr: 1.0e-4
lr_schedule: 'linear'
final_entropy_coeff: 0.002
entropy_schedule: False
# Memory management, if batch size overflow, batch splitting is done to handle it
max_minibatch_size: 1000
max_minibatch_size: 500
updates_per_batch: 8
normalize_actions: False
......@@ -87,8 +88,10 @@ procgen-ppo:
model:
custom_model: impala_torch_ppg
custom_model_config:
depths: [32, 64, 64]
nlatents: 512
# depths: [32, 64, 64]
# nlatents: 512
depths: [64, 128, 128]
nlatents: 1024
init_normed: True
use_layernorm: False
diff_framestack: True
......
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