def create_from_tensors( cls, trainer: DQNTrainer, mdp_ids: np.ndarray, sequence_numbers: torch.Tensor, states: Union[mt.State, torch.Tensor], actions: Union[mt.Action, torch.Tensor], propensities: torch.Tensor, rewards: torch.Tensor, possible_actions_mask: torch.Tensor, possible_actions: Optional[mt.FeatureVector] = None, max_num_actions: Optional[int] = None, metrics: Optional[torch.Tensor] = None, ): # Switch to evaluation mode for the network old_q_train_state = trainer.q_network.training old_reward_train_state = trainer.reward_network.training trainer.q_network.train(False) trainer.reward_network.train(False) if max_num_actions: # Parametric model CPE state_action_pairs = mt.StateAction( # type: ignore state=states, action=actions) tiled_state = mt.FeatureVector( states.float_features.repeat( # type: ignore 1, max_num_actions).reshape( # type: ignore -1, states.float_features.shape[1] # type: ignore )) # Get Q-value of action taken possible_actions_state_concat = mt.StateAction( # type: ignore state=tiled_state, action=possible_actions # type: ignore ) # Parametric actions # FIXME: model_values and model propensities should be calculated # as in discrete dqn model model_values = trainer.q_network( possible_actions_state_concat).q_value # type: ignore optimal_q_values = model_values eval_action_idxs = None assert (model_values.shape[0] * model_values.shape[1] == possible_actions_mask.shape[0] * possible_actions_mask.shape[1]), ( "Invalid shapes: " + str(model_values.shape) + " != " + str(possible_actions_mask.shape)) model_values = model_values.reshape(possible_actions_mask.shape) model_propensities = masked_softmax(model_values, possible_actions_mask, trainer.rl_temperature) model_rewards = trainer.reward_network( possible_actions_state_concat).q_value # type: ignore assert (model_rewards.shape[0] * model_rewards.shape[1] == possible_actions_mask.shape[0] * possible_actions_mask.shape[1]), ( "Invalid shapes: " + str(model_rewards.shape) + " != " + str(possible_actions_mask.shape)) model_rewards = model_rewards.reshape(possible_actions_mask.shape) model_values_for_logged_action = trainer.q_network( state_action_pairs).q_value model_rewards_for_logged_action = trainer.reward_network( state_action_pairs).q_value action_mask = ( torch.abs(model_values - model_values_for_logged_action) < 1e-3).float() model_metrics = None model_metrics_for_logged_action = None model_metrics_values = None model_metrics_values_for_logged_action = None else: if isinstance(states, mt.State): states = mt.StateInput(state=states) # type: ignore num_actions = trainer.num_actions action_mask = actions.float() # type: ignore # Switch to evaluation mode for the network old_q_cpe_train_state = trainer.q_network_cpe.training trainer.q_network_cpe.train(False) # Discrete actions rewards = trainer.boost_rewards(rewards, actions) # type: ignore model_values = trainer.q_network_cpe( states).q_values[:, 0:num_actions] optimal_q_values = trainer.get_detached_q_values( states.state # type: ignore )[ # type: ignore 0] # type: ignore eval_action_idxs = trainer.get_max_q_values( # type: ignore optimal_q_values, possible_actions_mask)[1] model_propensities = masked_softmax(optimal_q_values, possible_actions_mask, trainer.rl_temperature) assert model_values.shape == actions.shape, ( # type: ignore "Invalid shape: " + str(model_values.shape) # type: ignore + " != " + str(actions.shape) # type: ignore ) assert model_values.shape == possible_actions_mask.shape, ( # type: ignore "Invalid shape: " + str(model_values.shape) # type: ignore + " != " + str(possible_actions_mask.shape) # type: ignore ) model_values_for_logged_action = torch.sum(model_values * action_mask, dim=1, keepdim=True) rewards_and_metric_rewards = trainer.reward_network(states) # In case we reuse the modular for Q-network if hasattr(rewards_and_metric_rewards, "q_values"): rewards_and_metric_rewards = rewards_and_metric_rewards.q_values model_rewards = rewards_and_metric_rewards[:, 0:num_actions] assert model_rewards.shape == actions.shape, ( # type: ignore "Invalid shape: " + str(model_rewards.shape) # type: ignore + " != " + str(actions.shape) # type: ignore ) model_rewards_for_logged_action = torch.sum(model_rewards * action_mask, dim=1, keepdim=True) model_metrics = rewards_and_metric_rewards[:, num_actions:] assert model_metrics.shape[1] % num_actions == 0, ( "Invalid metrics shape: " + str(model_metrics.shape) + " " + str(num_actions)) num_metrics = model_metrics.shape[1] // num_actions if num_metrics == 0: model_metrics_values = None model_metrics_for_logged_action = None model_metrics_values_for_logged_action = None else: model_metrics_values = trainer.q_network_cpe(states) # Backward compatility if hasattr(model_metrics_values, "q_values"): model_metrics_values = model_metrics_values.q_values model_metrics_values = model_metrics_values[:, num_actions:] assert (model_metrics_values.shape[1] == num_actions * num_metrics), ( # type: ignore "Invalid shape: " + str(model_metrics_values.shape[1]) # type: ignore + " != " + str(actions.shape[1] * num_metrics) # type: ignore ) model_metrics_for_logged_action_list = [] model_metrics_values_for_logged_action_list = [] for metric_index in range(num_metrics): metric_start = metric_index * num_actions metric_end = (metric_index + 1) * num_actions model_metrics_for_logged_action_list.append( torch.sum( model_metrics[:, metric_start:metric_end] * action_mask, dim=1, keepdim=True, )) model_metrics_values_for_logged_action_list.append( torch.sum( model_metrics_values[:, metric_start:metric_end] * action_mask, dim=1, keepdim=True, )) model_metrics_for_logged_action = torch.cat( model_metrics_for_logged_action_list, dim=1) model_metrics_values_for_logged_action = torch.cat( model_metrics_values_for_logged_action_list, dim=1) # Switch back to the old mode trainer.q_network_cpe.train(old_q_cpe_train_state) # type: ignore # Switch back to the old mode trainer.q_network.train(old_q_train_state) # type: ignore trainer.reward_network.train(old_reward_train_state) # type: ignore return cls( mdp_id=mdp_ids, sequence_number=sequence_numbers, logged_propensities=propensities, logged_rewards=rewards, action_mask=action_mask, model_rewards=model_rewards, model_rewards_for_logged_action=model_rewards_for_logged_action, model_values=model_values, model_values_for_logged_action=model_values_for_logged_action, model_metrics_values=model_metrics_values, model_metrics_values_for_logged_action= model_metrics_values_for_logged_action, model_propensities=model_propensities, logged_metrics=metrics, model_metrics=model_metrics, model_metrics_for_logged_action=model_metrics_for_logged_action, # Will compute later logged_values=None, logged_metrics_values=None, possible_actions_mask=possible_actions_mask, optimal_q_values=optimal_q_values, eval_action_idxs=eval_action_idxs, )
def train(self, training_batch): if isinstance(training_batch, TrainingDataPage): if self.maxq_learning: training_batch = training_batch.as_discrete_maxq_training_batch( ) else: training_batch = training_batch.as_discrete_sarsa_training_batch( ) learning_input = training_batch.training_input boosted_rewards = self.boost_rewards(learning_input.reward, learning_input.action) self.minibatch += 1 rewards = boosted_rewards discount_tensor = torch.full_like(rewards, self.gamma) not_done_mask = learning_input.not_terminal.float() if self.use_seq_num_diff_as_time_diff: assert self.multi_steps is None discount_tensor = torch.pow(self.gamma, learning_input.time_diff.float()) if self.multi_steps is not None: discount_tensor = torch.pow(self.gamma, learning_input.step.float()) all_next_q_values, all_next_q_values_target = self.get_detached_q_values( learning_input.next_state) if self.maxq_learning: # Compute max a' Q(s', a') over all possible actions using target network possible_next_actions_mask = ( learning_input.possible_next_actions_mask.float()) if self.bcq: action_on_policy = get_valid_actions_from_imitator( self.bcq_imitator, learning_input.next_state.float_features, self.bcq_drop_threshold, ) possible_next_actions_mask *= action_on_policy next_q_values, max_q_action_idxs = self.get_max_q_values_with_target( all_next_q_values, all_next_q_values_target, possible_next_actions_mask) else: # SARSA next_q_values, max_q_action_idxs = self.get_max_q_values_with_target( all_next_q_values, all_next_q_values_target, learning_input.next_action) filtered_next_q_vals = next_q_values * not_done_mask target_q_values = rewards + (discount_tensor * filtered_next_q_vals) # Get Q-value of action taken current_state = rlt.StateInput(state=learning_input.state) all_q_values = self.q_network(current_state).q_values self.all_action_scores = all_q_values.detach() q_values = torch.sum(all_q_values * learning_input.action, 1, keepdim=True) loss = self.q_network_loss(q_values, target_q_values) self.loss = loss.detach() self.q_network_optimizer.zero_grad() loss.backward() self.q_network_optimizer.step() # Use the soft update rule to update target network self._soft_update(self.q_network, self.q_network_target, self.tau) # Get Q-values of next states, used in computing cpe with torch.no_grad(): next_state = rlt.StateInput(state=learning_input.next_state) all_next_action_scores = self.q_network( next_state).q_values.detach() logged_action_idxs = learning_input.action.argmax(dim=1, keepdim=True) reward_loss, model_rewards, model_propensities = self.calculate_cpes( training_batch, current_state, next_state, all_next_action_scores, logged_action_idxs, discount_tensor, not_done_mask, ) if self.maxq_learning: possible_actions_mask = learning_input.possible_actions_mask if self.bcq: action_on_policy = get_valid_actions_from_imitator( self.bcq_imitator, learning_input.state.float_features, self.bcq_drop_threshold, ) possible_actions_mask *= action_on_policy self.loss_reporter.report( td_loss=self.loss, reward_loss=reward_loss, logged_actions=logged_action_idxs, logged_propensities=training_batch.extras.action_probability, logged_rewards=rewards, logged_values=None, # Compute at end of each epoch for CPE model_propensities=model_propensities, model_rewards=model_rewards, model_values=self.all_action_scores, model_values_on_logged_actions= None, # Compute at end of each epoch for CPE model_action_idxs=self.get_max_q_values( self.all_action_scores, possible_actions_mask if self.maxq_learning else learning_input.action, )[1], )
def input_prototype(self): return rlt.StateInput(state=rlt.FeatureVector( float_features=torch.randn(1, self.state_dim)))
def train(self, training_batch) -> None: """ IMPORTANT: the input action here is assumed to be preprocessed to match the range of the output of the actor. """ if hasattr(training_batch, "as_parametric_sarsa_training_batch"): training_batch = training_batch.as_parametric_sarsa_training_batch( ) learning_input = training_batch.training_input self.minibatch += 1 state = learning_input.state action = learning_input.action next_state = learning_input.next_state reward = learning_input.reward not_done_mask = learning_input.not_terminal action = self._maybe_scale_action_in_train(action) max_action = (self.max_action_range_tensor_training if self.max_action_range_tensor_training else torch.ones( action.float_features.shape).type(self.dtype)) min_action = ( self.min_action_range_tensor_serving if self.min_action_range_tensor_serving else -torch.ones(action.float_features.shape).type(self.dtype)) # Compute current value estimates current_state_action = rlt.StateAction(state=state, action=action) q1_value = self.q1_network(current_state_action).q_value if self.q2_network: q2_value = self.q2_network(current_state_action).q_value actor_action = self.actor_network(rlt.StateInput(state=state)).action # Generate target = r + y * min (Q1(s',pi(s')), Q2(s',pi(s'))) with torch.no_grad(): next_actor = self.actor_network_target( rlt.StateInput(state=next_state)).action next_actor += (torch.randn_like(next_actor) * self.target_policy_smoothing).clamp( -self.noise_clip, self.noise_clip) next_actor = torch.max(torch.min(next_actor, max_action), min_action) next_state_actor = rlt.StateAction( state=next_state, action=rlt.FeatureVector(float_features=next_actor)) next_state_value = self.q1_network_target(next_state_actor).q_value if self.q2_network is not None: next_state_value = torch.min( next_state_value, self.q2_network_target(next_state_actor).q_value) target_q_value = ( reward + self.gamma * next_state_value * not_done_mask.float()) # Optimize Q1 and Q2 q1_loss = F.mse_loss(q1_value, target_q_value) q1_loss.backward() self._maybe_run_optimizer(self.q1_network_optimizer, self.minibatches_per_step) if self.q2_network: q2_loss = F.mse_loss(q2_value, target_q_value) q2_loss.backward() self._maybe_run_optimizer(self.q2_network_optimizer, self.minibatches_per_step) # Only update actor and target networks after a fixed number of Q updates if self.minibatch % self.delayed_policy_update == 0: actor_loss = -self.q1_network( rlt.StateAction( state=state, action=rlt.FeatureVector( float_features=actor_action))).q_value.mean() actor_loss.backward() self._maybe_run_optimizer(self.actor_network_optimizer, self.minibatches_per_step) # Use the soft update rule to update the target networks self._maybe_soft_update( self.q1_network, self.q1_network_target, self.tau, self.minibatches_per_step, ) self._maybe_soft_update( self.actor_network, self.actor_network_target, self.tau, self.minibatches_per_step, ) if self.q2_network is not None: self._maybe_soft_update( self.q2_network, self.q2_network_target, self.tau, self.minibatches_per_step, ) # Logging at the end to schedule all the cuda operations first if (self.tensorboard_logging_freq is not None and self.minibatch % self.tensorboard_logging_freq == 0): SummaryWriterContext.add_histogram("q1/logged_state_value", q1_value) if self.q2_network: SummaryWriterContext.add_histogram("q2/logged_state_value", q2_value) SummaryWriterContext.add_histogram("q_network/next_state_value", next_state_value) SummaryWriterContext.add_histogram("q_network/target_q_value", target_q_value) SummaryWriterContext.add_histogram("actor/loss", actor_loss) self.loss_reporter.report( td_loss=float(q1_loss), reward_loss=None, logged_rewards=reward, model_values_on_logged_actions=q1_value, )
def input_prototype(self): return rlt.StateInput(state=self.state_preprocessor.input_prototype())
def forward(self, input): preprocessed_state = self.state_preprocessor(input.state) return self.actor_network(rlt.StateInput(state=preprocessed_state))
def train(self, training_batch) -> None: """ IMPORTANT: the input action here is assumed to be preprocessed to match the range of the output of the actor. """ if hasattr(training_batch, "as_parametric_sarsa_training_batch"): training_batch = training_batch.as_parametric_sarsa_training_batch( ) learning_input = training_batch.training_input self.minibatch += 1 state = learning_input.state action = learning_input.action reward = learning_input.reward discount = torch.full_like(reward, self.gamma) not_done_mask = learning_input.not_terminal if self._should_scale_action_in_train(): action = rlt.FeatureVector( rescale_torch_tensor( action.float_features, new_min=self.min_action_range_tensor_training, new_max=self.max_action_range_tensor_training, prev_min=self.min_action_range_tensor_serving, prev_max=self.max_action_range_tensor_serving, )) # # First, optimize Q networks; minimizing MSE between # Q(s, a) & r + discount * V'(next_s) # current_state_action = rlt.StateAction(state=state, action=action) q1_value = self.q1_network(current_state_action).q_value if self.q2_network: q2_value = self.q2_network(current_state_action).q_value with torch.no_grad(): if self.value_network is not None: next_state_value = self.value_network_target( learning_input.next_state.float_features) else: actor_output = self.actor_network( rlt.StateInput(state=learning_input.next_state)) next_state_actor_action = rlt.StateAction( state=learning_input.next_state, action=rlt.FeatureVector( float_features=actor_output.action), ) next_state_value = self.q1_network_target( next_state_actor_action).q_value if self.q2_network is not None: target_q2_value = self.q2_network_target( next_state_actor_action).q_value next_state_value = torch.min(next_state_value, target_q2_value) log_prob_a = self.actor_network.get_log_prob( learning_input.next_state, actor_output.action) log_prob_a = log_prob_a.clamp(-20.0, 20.0) next_state_value -= self.entropy_temperature * log_prob_a target_q_value = ( reward + discount * next_state_value * not_done_mask.float()) q1_loss = F.mse_loss(q1_value, target_q_value) q1_loss.backward() self._maybe_run_optimizer(self.q1_network_optimizer, self.minibatches_per_step) if self.q2_network: q2_loss = F.mse_loss(q2_value, target_q_value) q2_loss.backward() self._maybe_run_optimizer(self.q2_network_optimizer, self.minibatches_per_step) # # Second, optimize the actor; minimizing KL-divergence between action propensity # & softmax of value. Due to reparameterization trick, it ends up being # log_prob(actor_action) - Q(s, actor_action) # actor_output = self.actor_network(rlt.StateInput(state=state)) state_actor_action = rlt.StateAction( state=state, action=rlt.FeatureVector(float_features=actor_output.action)) q1_actor_value = self.q1_network(state_actor_action).q_value min_q_actor_value = q1_actor_value if self.q2_network: q2_actor_value = self.q2_network(state_actor_action).q_value min_q_actor_value = torch.min(q1_actor_value, q2_actor_value) actor_loss = (self.entropy_temperature * actor_output.log_prob - min_q_actor_value) # Do this in 2 steps so we can log histogram of actor loss actor_loss_mean = actor_loss.mean() actor_loss_mean.backward() self._maybe_run_optimizer(self.actor_network_optimizer, self.minibatches_per_step) # # Lastly, if applicable, optimize value network; minimizing MSE between # V(s) & E_a~pi(s) [ Q(s,a) - log(pi(a|s)) ] # if self.value_network is not None: state_value = self.value_network(state.float_features) if self.logged_action_uniform_prior: log_prob_a = torch.zeros_like(min_q_actor_value) target_value = min_q_actor_value else: with torch.no_grad(): log_prob_a = actor_output.log_prob log_prob_a = log_prob_a.clamp(-20.0, 20.0) target_value = (min_q_actor_value - self.entropy_temperature * log_prob_a) value_loss = F.mse_loss(state_value, target_value.detach()) value_loss.backward() self._maybe_run_optimizer(self.value_network_optimizer, self.minibatches_per_step) # Use the soft update rule to update the target networks if self.value_network is not None: self._maybe_soft_update( self.value_network, self.value_network_target, self.tau, self.minibatches_per_step, ) else: self._maybe_soft_update( self.q1_network, self.q1_network_target, self.tau, self.minibatches_per_step, ) if self.q2_network is not None: self._maybe_soft_update( self.q2_network, self.q2_network_target, self.tau, self.minibatches_per_step, ) # Logging at the end to schedule all the cuda operations first if (self.tensorboard_logging_freq is not None and self.minibatch % self.tensorboard_logging_freq == 0): SummaryWriterContext.add_histogram("q1/logged_state_value", q1_value) if self.q2_network: SummaryWriterContext.add_histogram("q2/logged_state_value", q2_value) SummaryWriterContext.add_histogram("log_prob_a", log_prob_a) if self.value_network: SummaryWriterContext.add_histogram("value_network/target", target_value) SummaryWriterContext.add_histogram("q_network/next_state_value", next_state_value) SummaryWriterContext.add_histogram("q_network/target_q_value", target_q_value) SummaryWriterContext.add_histogram("actor/min_q_actor_value", min_q_actor_value) SummaryWriterContext.add_histogram("actor/action_log_prob", actor_output.log_prob) SummaryWriterContext.add_histogram("actor/loss", actor_loss) self.loss_reporter.report( td_loss=float(q1_loss), reward_loss=None, logged_rewards=reward, model_values_on_logged_actions=q1_value, model_propensities=actor_output.log_prob.exp(), model_values=min_q_actor_value, )
def train(self, training_batch, evaluator=None) -> None: """ IMPORTANT: the input action here is assumed to be preprocessed to match the range of the output of the actor. """ if hasattr(training_batch, "as_parametric_sarsa_training_batch"): training_batch = training_batch.as_parametric_sarsa_training_batch() learning_input = training_batch.training_input self.minibatch += 1 state = learning_input.state action = learning_input.action reward = learning_input.reward discount = torch.full_like(reward, self.gamma) not_done_mask = learning_input.not_terminal current_state_action = rlt.StateAction(state=state, action=action) q1_value = self.q1_network(current_state_action).q_value min_q_value = q1_value if self.q2_network: q2_value = self.q2_network(current_state_action).q_value min_q_value = torch.min(q1_value, q2_value) # Use the minimum as target, ensure no gradient going through min_q_value = min_q_value.detach() # # First, optimize value network; minimizing MSE between # V(s) & Q(s, a) - log(pi(a|s)) # state_value = self.value_network(state.float_features) # .q_value with torch.no_grad(): log_prob_a = self.actor_network.get_log_prob(state, action.float_features) target_value = min_q_value - self.entropy_temperature * log_prob_a value_loss = F.mse_loss(state_value, target_value) self.value_network_optimizer.zero_grad() value_loss.backward() self.value_network_optimizer.step() # # Second, optimize Q networks; minimizing MSE between # Q(s, a) & r + discount * V'(next_s) # with torch.no_grad(): next_state_value = ( self.value_network_target(learning_input.next_state.float_features) * not_done_mask ) if self.minibatch < self.reward_burnin: target_q_value = reward else: target_q_value = reward + discount * next_state_value q1_loss = F.mse_loss(q1_value, target_q_value) self.q1_network_optimizer.zero_grad() q1_loss.backward() self.q1_network_optimizer.step() if self.q2_network: q2_loss = F.mse_loss(q2_value, target_q_value) self.q2_network_optimizer.zero_grad() q2_loss.backward() self.q2_network_optimizer.step() # # Lastly, optimize the actor; minimizing KL-divergence between action propensity # & softmax of value. Due to reparameterization trick, it ends up being # log_prob(actor_action) - Q(s, actor_action) # actor_output = self.actor_network(rlt.StateInput(state=state)) state_actor_action = rlt.StateAction( state=state, action=rlt.FeatureVector(float_features=actor_output.action) ) q1_actor_value = self.q1_network(state_actor_action).q_value min_q_actor_value = q1_actor_value if self.q2_network: q2_actor_value = self.q2_network(state_actor_action).q_value min_q_actor_value = torch.min(q1_actor_value, q2_actor_value) actor_loss = ( self.entropy_temperature * actor_output.log_prob - min_q_actor_value ) # Do this in 2 steps so we can log histogram of actor loss actor_loss_mean = actor_loss.mean() self.actor_network_optimizer.zero_grad() actor_loss_mean.backward() self.actor_network_optimizer.step() if self.minibatch < self.reward_burnin: # Reward burnin: force target network self._soft_update(self.value_network, self.value_network_target, 1.0) else: # Use the soft update rule to update both target networks self._soft_update(self.value_network, self.value_network_target, self.tau) # Logging at the end to schedule all the cuda operations first if ( self.tensorboard_logging_freq is not None and self.minibatch % self.tensorboard_logging_freq == 0 ): SummaryWriterContext.add_histogram("q1/logged_state_value", q1_value) if self.q2_network: SummaryWriterContext.add_histogram("q2/logged_state_value", q2_value) SummaryWriterContext.add_histogram("log_prob_a", log_prob_a) SummaryWriterContext.add_histogram("min_q/logged_state_value", min_q_value) SummaryWriterContext.add_histogram("value_network/target", target_value) SummaryWriterContext.add_histogram( "q_network/next_state_value", next_state_value ) SummaryWriterContext.add_histogram( "q_network/target_q_value", target_q_value ) SummaryWriterContext.add_histogram( "actor/min_q_actor_value", min_q_actor_value ) SummaryWriterContext.add_histogram( "actor/action_log_prob", actor_output.log_prob ) SummaryWriterContext.add_histogram("actor/loss", actor_loss) if evaluator is not None: cpe_stats = BatchStatsForCPE( td_loss=q1_loss.detach().cpu().numpy(), model_values_on_logged_actions=q1_value.detach().cpu().numpy(), ) evaluator.report(cpe_stats)
def train(self, training_batch): if isinstance(training_batch, TrainingDataPage): training_batch = training_batch.as_discrete_sarsa_training_batch() learning_input = training_batch.training_input # Apply reward boost if specified reward_boosts = torch.sum(learning_input.action * self.reward_boosts, dim=1, keepdim=True) boosted_rewards = learning_input.reward + reward_boosts self.minibatch += 1 rewards = boosted_rewards discount_tensor = torch.full_like(rewards, self.gamma) not_done_mask = learning_input.not_terminal if self.use_seq_num_diff_as_time_diff: # TODO: Implement this in another diff logger.warning( "_dqn_trainer has not implemented use_seq_num_diff_as_time_diff feature" ) pass all_next_q_values, all_next_q_values_target = self.get_detached_q_values( learning_input.next_state) if self.maxq_learning: # Compute max a' Q(s', a') over all possible actions using target network next_q_values, max_q_action_idxs = self.get_max_q_values_with_target( all_next_q_values.q_values, all_next_q_values_target.q_values if self.double_q_learning else None, learning_input.possible_next_actions_mask.float(), ) else: # SARSA next_q_values, max_q_action_idxs = self.get_max_q_values_with_target( all_next_q_values.q_values, all_next_q_values_target.q_values if self.double_q_learning else None, learning_input.next_action, ) filtered_next_q_vals = next_q_values * not_done_mask.float() if self.minibatch < self.reward_burnin: target_q_values = rewards else: target_q_values = rewards + (discount_tensor * filtered_next_q_vals) # Get Q-value of action taken current_state = rlt.StateInput(state=learning_input.state) all_q_values = self.q_network(current_state).q_values self.all_action_scores = all_q_values.detach() q_values = torch.sum(all_q_values * learning_input.action, 1, keepdim=True) loss = self.q_network_loss(q_values, target_q_values) self.loss = loss.detach() self.q_network_optimizer.zero_grad() loss.backward() if self.gradient_handler: self.gradient_handler(self.q_network.parameters()) self.q_network_optimizer.step() if self.minibatch < self.reward_burnin: # Reward burnin: force target network self._soft_update(self.q_network, self.q_network_target, 1.0) else: # Use the soft update rule to update target network self._soft_update(self.q_network, self.q_network_target, self.tau) # get reward estimates reward_estimates = self.reward_network(current_state).q_values reward_loss = F.mse_loss(reward_estimates, rewards) self.reward_network_optimizer.zero_grad() reward_loss.backward() self.reward_network_optimizer.step() self.loss_reporter.report( td_loss=self.loss, reward_loss=reward_loss, model_values_on_logged_actions=q_values, )