def calculate_gradient(self, batch_info, device, model, rollout):
assert isinstance(rollout, Trajectories), "<STR_LIT>"<EOL>local_epsilon = <NUM_LIT><EOL>evaluator = model.evaluate(rollout)<EOL>actions = evaluator.get('<STR_LIT>')<EOL>rollout_probabilities = torch.exp(evaluator.get('<STR_LIT>'))<EOL>if self.trust_region:<EOL><INDENT>self.update_average_model(model)<EOL><DEDENT>logprobs = evaluator.get('<STR_LIT>')<EOL>q = evaluator.get('<STR_LIT>')<EOL>action_logprobs = select_indices(logprobs, actions)<EOL>action_q = select_indices(q, actions)<EOL>with torch.no_grad():<EOL><INDENT>model_probabilities = torch.exp(logprobs)<EOL>rho = model_probabilities / (rollout_probabilities + local_epsilon)<EOL>actions_rho = select_indices(rho, actions)<EOL>model_state_values = (model_probabilities * q).sum(dim=<NUM_LIT:1>)<EOL>trajectory_rewards = rollout.transition_tensors['<STR_LIT>']<EOL>trajectory_dones = rollout.transition_tensors['<STR_LIT>']<EOL>q_retraced = self.retrace(<EOL>trajectory_rewards,<EOL>trajectory_dones,<EOL>action_q.reshape(trajectory_rewards.size()),<EOL>model_state_values.reshape(trajectory_rewards.size()),<EOL>actions_rho.reshape(trajectory_rewards.size()),<EOL>rollout.rollout_tensors['<STR_LIT>']<EOL>).flatten()<EOL>advantages = q_retraced - model_state_values<EOL>importance_sampling_coefficient = torch.min(actions_rho, self.rho_cap * torch.ones_like(actions_rho))<EOL>explained_variance = <NUM_LIT:1> - torch.var(q_retraced - action_q) / torch.var(q_retraced)<EOL><DEDENT>policy_entropy = torch.mean(model.entropy(logprobs))<EOL>policy_gradient_loss = -torch.mean(advantages * importance_sampling_coefficient * action_logprobs)<EOL>with torch.no_grad():<EOL><INDENT>advantages_bias_correction = q - model_state_values.view(model_probabilities.size(<NUM_LIT:0>), <NUM_LIT:1>)<EOL>bias_correction_coefficient = F.relu(<NUM_LIT:1.0> - self.rho_cap / (rho + local_epsilon))<EOL><DEDENT>policy_gradient_bias_correction_gain = torch.sum(<EOL>logprobs * bias_correction_coefficient * advantages_bias_correction * model_probabilities,<EOL>dim=<NUM_LIT:1><EOL>)<EOL>policy_gradient_bias_correction_loss = - torch.mean(policy_gradient_bias_correction_gain)<EOL>policy_loss = policy_gradient_loss + policy_gradient_bias_correction_loss<EOL>q_function_loss = <NUM_LIT:0.5> * F.mse_loss(action_q, q_retraced)<EOL>if self.trust_region:<EOL><INDENT>with torch.no_grad():<EOL><INDENT>average_evaluator = self.average_model.evaluate(rollout)<EOL>average_action_logits = average_evaluator.get('<STR_LIT>')<EOL><DEDENT>actor_loss = policy_loss - self.entropy_coefficient * policy_entropy<EOL>q_loss = self.q_coefficient * q_function_loss<EOL>actor_gradient = torch.autograd.grad(-actor_loss, logprobs, retain_graph=True)[<NUM_LIT:0>]<EOL>kl_divergence_grad_symbolic = - torch.exp(average_action_logits) / logprobs.size(<NUM_LIT:0>)<EOL>k_dot_g = (actor_gradient * kl_divergence_grad_symbolic).sum(dim=-<NUM_LIT:1>)<EOL>k_dot_k = (kl_divergence_grad_symbolic ** <NUM_LIT:2>).sum(dim=-<NUM_LIT:1>)<EOL>adjustment = (k_dot_g - self.trust_region_delta) / k_dot_k<EOL>adjustment_clipped = adjustment.clamp(min=<NUM_LIT:0.0>)<EOL>actor_gradient_updated = actor_gradient - adjustment_clipped.view(adjustment_clipped.size(<NUM_LIT:0>), <NUM_LIT:1>)<EOL>logprobs.backward(gradient=-actor_gradient_updated, retain_graph=True)<EOL>q_loss.backward(retain_graph=True)<EOL><DEDENT>else:<EOL><INDENT>loss = policy_loss + self.q_coefficient * q_function_loss - self.entropy_coefficient * policy_entropy<EOL>loss.backward()<EOL><DEDENT>return {<EOL>'<STR_LIT>': policy_loss.item(),<EOL>'<STR_LIT>': policy_gradient_loss.item(),<EOL>'<STR_LIT>': policy_gradient_bias_correction_loss.item(),<EOL>'<STR_LIT>': action_q.mean().item(),<EOL>'<STR_LIT>': q_retraced.mean().item(),<EOL>'<STR_LIT>': q_function_loss.item(),<EOL>'<STR_LIT>': policy_entropy.item(),<EOL>'<STR_LIT>': torch.norm(advantages).item(),<EOL>'<STR_LIT>': explained_variance.item(),<EOL>'<STR_LIT>': model_probabilities.std().item(),<EOL>'<STR_LIT>': rollout_probabilities.std().item()<EOL>}<EOL>
Calculate loss of the supplied rollout