parameters: (self)
return_statement: return
docstring: Perform one update step of the policy weights.
docstring_summary: Perform one update step of the policy weights.
docstring_tokens: Perform one update step of the policy weights .
function: def train_step(self):
          """ 
          Perform one update step of the policy weights.
          """
          
          g_hat = self.aggregate_rollouts()                    
          print("Euclidean norm of update step:", np.linalg.norm(g_hat))
          self.w_policy -= self.optimizer._compute_step(g_hat).reshape(self.w_policy.shape)
          return
function_tokens: def train_step ( self ) : g_hat = self . aggregate_rollouts ( ) print ( "Euclidean norm of update step:" , np . linalg . norm ( g_hat ) ) self . w_policy -= self . optimizer . _compute_step ( g_hat ) . reshape ( self . w_policy . shape ) return