def calc_gae_advs_v_targets(self, batch, v_preds):
'''
Calculate GAE, and advs = GAE, v_targets = advs + v_preds
See GAE from Schulman et al. https://arxiv.org/pdf/1506.02438.pdf
'''
next_states = batch['next_states'][-1]
if not self.body.env.is_venv:
next_states = next_states.unsqueeze(dim=0)
with torch.no_grad():
next_v_pred = self.calc_v(next_states, use_cache=False)
v_preds = v_preds.detach() # adv does not accumulate grad
if self.body.env.is_venv:
v_preds = math_util.venv_pack(v_preds, self.body.env.num_envs)
next_v_pred = next_v_pred.unsqueeze(dim=0)
v_preds_all = torch.cat((v_preds, next_v_pred), dim=0)
advs = math_util.calc_gaes(batch['rewards'], batch['dones'],
v_preds_all, self.gamma, self.lam)
v_targets = advs + v_preds
advs = math_util.standardize(
advs) # standardize only for advs, not v_targets
if self.body.env.is_venv:
advs = math_util.venv_unpack(advs)
v_targets = math_util.venv_unpack(v_targets)
logger.debug(f'advs: {advs}\nv_targets: {v_targets}')
return advs, v_targets
def calc_ret_advs_v_targets(self, batch, v_preds):
'''Calculate plain returns, and advs = rets - v_preds, v_targets = rets'''
v_preds = v_preds.detach() # adv does not accumulate grad
if self.body.env.is_venv:
v_preds = math_util.venv_pack(v_preds, self.body.env.num_envs)
rets = math_util.calc_returns(batch['rewards'], batch['dones'],
self.gamma)
advs = rets - v_preds
v_targets = rets
if self.body.env.is_venv:
advs = math_util.venv_unpack(advs)
v_targets = math_util.venv_unpack(v_targets)
logger.debug(f'advs: {advs}\nv_targets: {v_targets}')
return advs, v_targets
def calc_q_loss(self, batch):
'''Compute the Q value loss using predicted and target Q values from the appropriate networks'''
states = batch['states']
next_states = batch['next_states']
if self.body.env.is_venv:
states = math_util.venv_unpack(states)
next_states = math_util.venv_unpack(next_states)
q_preds = self.net(states)
with torch.no_grad():
next_q_preds = self.net(next_states)
if self.body.env.is_venv:
q_preds = math_util.venv_pack(q_preds, self.body.env.num_envs)
next_q_preds = math_util.venv_pack(next_q_preds,
self.body.env.num_envs)
act_q_preds = q_preds.gather(
-1, batch['actions'].long().unsqueeze(-1)).squeeze(-1)
act_next_q_preds = next_q_preds.gather(
-1, batch['next_actions'].long().unsqueeze(-1)).squeeze(-1)
act_q_targets = batch['rewards'] + self.gamma * (
1 - batch['dones']) * act_next_q_preds
logger.debug(
f'act_q_preds: {act_q_preds}\nact_q_targets: {act_q_targets}')
q_loss = self.net.loss_fn(act_q_preds, act_q_targets)
return q_loss
def calc_ret_advs_v_targets(self, batch, v_preds):
"""
Args:
batch:
v_preds:
Returns: advs(difference between prediction and original reward), v_targets, original reward.
"""
'''Calculate plain returns, and advs = rets - v_preds, v_targets = rets'''
v_preds = v_preds.detach() # adv does not accumulate grad
if self.body.env.is_venv:
v_preds = math_util.venv_pack(v_preds, self.body.env.num_envs)
rets = math_util.calc_returns(batch['rewards'], batch['dones'],
self.gamma)
advs = rets - v_preds
v_targets = rets
if self.body.env.is_venv:
advs = math_util.venv_unpack(advs)
v_targets = math_util.venv_unpack(v_targets)
logger.debug(f'advs: {advs}\nv_targets: {v_targets}')
return advs, v_targets
def calc_nstep_advs_v_targets(self, batch, v_preds):
'''
Calculate N-step returns, and advs = nstep_rets - v_preds, v_targets = nstep_rets
See n-step advantage under http://rail.eecs.berkeley.edu/deeprlcourse-fa17/f17docs/lecture_5_actor_critic_pdf.pdf
'''
next_states = batch['next_states'][-1]
if not self.body.env.is_venv:
next_states = next_states.unsqueeze(dim=0)
with torch.no_grad():
next_v_pred = self.calc_v(next_states, use_cache=False)
v_preds = v_preds.detach() # adv does not accumulate grad
if self.body.env.is_venv:
v_preds = math_util.venv_pack(v_preds, self.body.env.num_envs)
nstep_rets = math_util.calc_nstep_returns(batch['rewards'],
batch['dones'], next_v_pred,
self.gamma,
self.num_step_returns)
advs = nstep_rets - v_preds
v_targets = nstep_rets
if self.body.env.is_venv:
advs = math_util.venv_unpack(advs)
v_targets = math_util.venv_unpack(v_targets)
logger.debug(f'advs: {advs}\nv_targets: {v_targets}')
return advs, v_targets
def train(self):
if util.in_eval_lab_modes():
return np.nan
clock = self.body.env.clock
if self.to_train == 1:
net_util.copy(self.net, self.old_net) # update old net
batch = self.sample()
clock.set_batch_size(len(batch))
_pdparams, v_preds = self.calc_pdparam_v(batch)
advs, v_targets = self.calc_advs_v_targets(batch, v_preds)
# piggy back on batch, but remember to not pack or unpack
batch['advs'], batch['v_targets'] = advs, v_targets
if self.body.env.is_venv: # unpack if venv for minibatch sampling
for k, v in batch.items():
if k not in ('advs', 'v_targets'):
batch[k] = math_util.venv_unpack(v)
total_loss = torch.tensor(0.0)
for _ in range(self.training_epoch):
minibatches = util.split_minibatch(batch, self.minibatch_size)
for minibatch in minibatches:
if self.body.env.is_venv: # re-pack to restore proper shape
for k, v in minibatch.items():
if k not in ('advs', 'v_targets'):
minibatch[k] = math_util.venv_pack(
v, self.body.env.num_envs)
advs, v_targets = minibatch['advs'], minibatch['v_targets']
pdparams, v_preds = self.calc_pdparam_v(minibatch)
policy_loss = self.calc_policy_loss(
minibatch, pdparams, advs) # from actor
val_loss = self.calc_val_loss(v_preds,
v_targets) # from critic
if self.shared: # shared network
loss = policy_loss + val_loss
self.net.train_step(loss,
self.optim,
self.lr_scheduler,
clock=clock,
global_net=self.global_net)
else:
self.net.train_step(policy_loss,
self.optim,
self.lr_scheduler,
clock=clock,
global_net=self.global_net)
self.critic_net.train_step(
val_loss,
self.critic_optim,
self.critic_lr_scheduler,
clock=clock,
global_net=self.global_critic_net)
loss = policy_loss + val_loss
total_loss += loss
loss = total_loss / self.training_epoch / len(minibatches)
# reset
self.to_train = 0
logger.debug(
f'Trained {self.name} at epi: {clock.epi}, frame: {clock.frame}, t: {clock.t}, total_reward so far: {self.body.total_reward}, loss: {loss:g}'
)
return loss.item()
else:
return np.nan
def train(self):
# torch.save(self.net.state_dict(), './reward_model/policy_pretrain.mdl')
# raise ValueError("policy pretrain stops")
if util.in_eval_lab_modes():
return np.nan
clock = self.body.env.clock
if self.body.env.clock.epi > 700:
self.pretrain_finished = True
# torch.save(self.discriminator.state_dict(), './reward_model/airl_pretrain.mdl')
# raise ValueError("pretrain stops here")
if self.to_train == 1:
net_util.copy(self.net, self.old_net) # update old net
batch = self.sample()
if self.reward_type == 'OFFGAN':
batch = self.replace_reward_batch(batch)
# if self.reward_type =='DISC':
# batch = self.fetch_disc_reward(batch)
# if self.reward_type =='AIRL':
# batch = self.fetch_airl_reward(batch)
# if self.reward_type == 'OFFGAN_update':
# batch = self.fetch_offgan_reward(batch)
clock.set_batch_size(len(batch))
_pdparams, v_preds = self.calc_pdparam_v(batch)
advs, v_targets = self.calc_advs_v_targets(batch, v_preds)
# piggy back on batch, but remember to not pack or unpack
batch['advs'], batch['v_targets'] = advs, v_targets
if self.body.env.is_venv: # unpack if venv for minibatch sampling
for k, v in batch.items():
if k not in ('advs', 'v_targets'):
batch[k] = math_util.venv_unpack(v)
total_loss = torch.tensor(0.0)
for _ in range(self.training_epoch):
minibatches = util.split_minibatch(batch, self.minibatch_size)
# if not self.pretrain_finished or not self.policy_training_flag:
# break
for minibatch in minibatches:
if self.body.env.is_venv: # re-pack to restore proper shape
for k, v in minibatch.items():
if k not in ('advs', 'v_targets'):
minibatch[k] = math_util.venv_pack(
v, self.body.env.num_envs)
advs, v_targets = minibatch['advs'], minibatch['v_targets']
pdparams, v_preds = self.calc_pdparam_v(minibatch)
policy_loss = self.calc_policy_loss(
minibatch, pdparams, advs) # from actor
val_loss = self.calc_val_loss(v_preds,
v_targets) # from critic
if self.shared: # shared network
loss = policy_loss + val_loss
self.net.train_step(loss,
self.optim,
self.lr_scheduler,
clock=clock,
global_net=self.global_net)
else:
# pretrain_finished = false -> policy keep fixed, updating value net and disc
if not self.pretrain_finished:
self.critic_net.train_step(
val_loss,
self.critic_optim,
self.critic_lr_scheduler,
clock=clock,
global_net=self.global_critic_net)
loss = val_loss
if self.pretrain_finished and self.policy_training_flag:
self.net.train_step(policy_loss,
self.optim,
self.lr_scheduler,
clock=clock,
global_net=self.global_net)
self.critic_net.train_step(
val_loss,
self.critic_optim,
self.critic_lr_scheduler,
clock=clock,
global_net=self.global_critic_net)
loss = policy_loss + val_loss
total_loss += loss
loss = total_loss / self.training_epoch / len(minibatches)
if not self.pretrain_finished:
logger.info(
"warmup Value net, epi: {}, frame: {}, loss: {}".format(
clock.epi, clock.frame, loss))
# reset
self.to_train = 0
self.policy_training_flag = False
logger.debug(
f'Trained {self.name} at epi: {clock.epi}, frame: {clock.frame}, t: {clock.t}, total_reward so far: {self.body.total_reward}, loss: {loss:g}'
)
return loss.item()
else:
return np.nan