Created
August 10, 2019 06:50
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SAC training loop
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| def train_alpha(self, alpha_loss): | |
| '''Custom method to train the alpha variable''' | |
| self.alpha_lr_scheduler.step(epoch=self.body.env.clock.frame) | |
| self.alpha_optim.zero_grad() | |
| alpha_loss.backward() | |
| self.alpha_optim.step() | |
| self.alpha = self.log_alpha.detach().exp() | |
| def train(self): | |
| '''Train actor critic by computing the loss in batch efficiently''' | |
| if util.in_eval_lab_modes(): | |
| return np.nan | |
| clock = self.body.env.clock | |
| if self.to_train == 1: | |
| for _ in range(self.training_iter): | |
| batch = self.sample() | |
| clock.set_batch_size(len(batch)) | |
| states = batch['states'] | |
| actions = self.guard_q_actions(batch['actions']) | |
| q_targets = self.calc_q_targets(batch) | |
| # Q-value loss for both Q nets | |
| q1_preds = self.calc_q(states, actions, self.q1_net) | |
| q1_loss = self.calc_reg_loss(q1_preds, q_targets) | |
| self.q1_net.train_step(q1_loss, self.q1_optim, self.q1_lr_scheduler, clock=clock, global_net=self.global_q1_net) | |
| q2_preds = self.calc_q(states, actions, self.q2_net) | |
| q2_loss = self.calc_reg_loss(q2_preds, q_targets) | |
| self.q2_net.train_step(q2_loss, self.q2_optim, self.q2_lr_scheduler, clock=clock, global_net=self.global_q2_net) | |
| # policy loss | |
| action_pd = policy_util.init_action_pd(self.body.ActionPD, self.calc_pdparam(states)) | |
| log_probs, reparam_actions = self.calc_log_prob_action(action_pd, reparam=True) | |
| policy_loss = self.calc_policy_loss(batch, log_probs, reparam_actions) | |
| self.net.train_step(policy_loss, self.optim, self.lr_scheduler, clock=clock, global_net=self.global_net) | |
| # alpha loss | |
| alpha_loss = self.calc_alpha_loss(log_probs) | |
| self.train_alpha(alpha_loss) | |
| loss = q1_loss + q2_loss + policy_loss + alpha_loss | |
| # update target networks | |
| self.update_nets() | |
| # update PER priorities if availalbe | |
| self.try_update_per(torch.min(q1_preds, q2_preds), q_targets) | |
| # 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.env.total_reward}, loss: {loss:g}') | |
| return loss.item() | |
| else: | |
| return np.nan |
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