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_policy_loss(self, batch):
'''Calculate the policy loss for a batch of data.'''
# use simple returns as advs
advs = math_util.calc_returns(batch, self.gamma)
advs = math_util.standardize(advs)
logger.debug(f'advs: {advs}')
assert len(self.body.log_probs) == len(
advs
), f'batch_size of log_probs {len(self.body.log_probs)} vs advs: {len(advs)}'
log_probs = torch.stack(self.body.log_probs)
policy_loss = -log_probs * advs
if self.entropy_coef_spec is not None:
entropies = torch.stack(self.body.entropies)
policy_loss += (-self.body.entropy_coef * entropies)
policy_loss = torch.sum(policy_loss)
logger.debug(f'Actor policy loss: {policy_loss:g}')
return policy_loss
def calc_gae_advs_v_targets(self, batch):
'''
Calculate the GAE advantages and value targets for training actor and critic respectively
adv_targets = GAE (see math_util method)
v_targets = adv_targets + v_preds
before output, adv_targets is standardized (so v_targets used the unstandardized version)
Used for training with GAE
'''
states = torch.cat((batch['states'], batch['next_states'][-1:]),
dim=0) # prevent double-pass
v_preds = self.calc_v(states)
next_v_preds = v_preds[1:] # shift for only the next states
# v_target = r_t + gamma * V(s_(t+1)), i.e. 1-step return
v_targets = math_util.calc_nstep_returns(batch['rewards'],
batch['dones'], self.gamma, 1,
next_v_preds)
adv_targets = math_util.calc_gaes(batch['rewards'], batch['dones'],
v_preds, self.gamma, self.lam)
adv_targets = math_util.standardize(adv_targets)
logger.debug(f'adv_targets: {adv_targets}\nv_targets: {v_targets}')
return adv_targets, v_targets
def calc_gae_advs_v_targets(self, batch):
'''
Calculate the GAE advantages and value targets for training actor and critic respectively
adv_targets = GAE (see math_util method)
v_targets = adv_targets + v_preds
before output, adv_targets is standardized (so v_targets used the unstandardized version)
Used for training with GAE
'''
v_preds = self.calc_v(batch['states'])
# calc next_state boundary value and concat with above for efficiency
next_v_pred_tail = self.calc_v(batch['next_states'][-1:])
next_v_preds = torch.cat([v_preds[1:], next_v_pred_tail], dim=0)
# v targets = r_t + gamma * V(s_(t+1))
v_targets = math_util.calc_nstep_returns(batch, self.gamma, 1,
next_v_preds)
# ensure val for next_state is 0 at done
next_v_preds = next_v_preds * (1 - batch['dones'])
adv_targets = math_util.calc_gaes(batch['rewards'], v_preds,
next_v_preds, self.gamma, self.lam)
adv_targets = math_util.standardize(adv_targets)
logger.debug(f'adv_targets: {adv_targets}\nv_targets: {v_targets}')
return adv_targets, v_targets
