fix(pytorch): correct step-based learning rate decay

stable_learning_control/algos/pytorch/common/get_lr_scheduler.py

@@ -22,22 +22,24 @@
     return gamma
 
 
-def calc_linear_decay_rate(lr_init, lr_final, steps):
-    r"""Returns the linear decay factor (G) needed to achieve a given final learning
-    rate at a certain step. This decay factor can for example be used with a
-    :class:`torch.optim.lr_scheduler.LambdaLR` scheduler. Keep in mind that this
-    function assumes the following formula for the learning rate decay.
+def get_linear_decay_rate(lr_init, lr_final, steps):
+    r"""Returns a linear decay factor (G) that enables a learning rate to transition
+    from an initial value (`lr_init`) at step 0 to a final value (`lr_final`) at a
+    specified step (N). This decay factor is compatible with the
+    :class:`torch.optim.lr_scheduler.LambdaLR` scheduler. The decay factor is calculated
+    using the following formula:
 
     .. math::
         lr_{terminal} = lr_{init} * (1.0 - G \cdot step)
 
     Args:
         lr_init (float): The initial learning rate.
         lr_final (float): The final learning rate you want to achieve.
-        steps (int): The step/epoch at which you want to achieve this learning rate.
+        steps (int): The number of steps/epochs over which the learning rate should
+            decay. This is equal to epochs - 1.
 
     Returns:
-        decimal.Decimal: Linear learning rate decay factor (G)
+        decimal.Decimal: Linear learning rate decay factor (G).
     """  # noqa: W605
     return -(
         ((Decimal(lr_final) / Decimal(lr_init)) - Decimal(1.0)) / Decimal(max(steps, 1))
@@ -53,7 +55,7 @@
             (options are: ``linear`` and ``exponential`` and ``constant``).
         lr_start (float): Initial learning rate.
         lr_final (float): Final learning rate.
-        steps (int, optional): Number of steps/epochs used in the training.  This
+        steps (int, optional): Number of steps/epochs used in the training. This
             includes the starting step.
 
     Returns:
@@ -83,7 +85,7 @@
                 return np.longdouble(
                     Decimal(1.0)
                     - (
-                        calc_linear_decay_rate(lr_start, lr_final, (steps - 1.0))
+                        get_linear_decay_rate(lr_start, lr_final, (steps - 1.0))
                         * Decimal(step)
                     )
                 )
@@ -96,3 +98,51 @@
         return torch.optim.lr_scheduler.LambdaLR(
             optimizer, lr_lambda=lambda step: np.longdouble(1.0)
         )  # Return a constant function.
+
+
+def estimate_step_learning_rate(
+    lr_scheduler, lr_start, lr_final, update_after, total_steps, step
+):
+    """Estimates the learning rate at a given step.
+
+    This function estimates the learning rate for a specific training step. It differs
+    from the `get_last_lr` method of the learning rate scheduler, which returns the
+    learning rate at the last scheduler step, not necessarily the current training step.
+
+    Args:
+        lr_scheduler (torch.optim.lr_scheduler): The learning rate scheduler.
+        lr_start (float): The initial learning rate.
+        update_after (int): The step number after which the learning rate should start
+            decreasing.
+        lr_final (float): The final learning rate.
+        total_steps (int): The total number of steps/epochs in the training process. 
+            Excludes the initial step.
+        step (int): The current step number. Excludes the initial step.
+
+    Returns:
+        float: The learning rate at the given step.
+    """
+    if step < update_after:
+        return lr_start
+    else:
+        adjusted_step = step - update_after
+        adjusted_total_steps = total_steps - update_after
+        if isinstance(lr_scheduler, torch.optim.lr_scheduler.LambdaLR):
+            decay_rate = get_linear_decay_rate(lr_start, lr_final, adjusted_total_steps)
+            lr = float(
+                Decimal(lr_start) * (Decimal(1.0) - decay_rate * Decimal(adjusted_step))
+            )
+        elif isinstance(lr_scheduler, torch.optim.lr_scheduler.ExponentialLR):
+            decay_rate = get_exponential_decay_rate(
+                lr_start, lr_final, adjusted_total_steps
+            )
+            lr = float(
+                Decimal(lr_start) * (Decimal(decay_rate) ** Decimal(adjusted_step))
+            )
+        else:
+            supported_schedulers = ["LambdaLR", "ExponentialLR"]
+            raise ValueError(
+                f"The learning rate scheduler is not supported for this function. "
+                f"Supported schedulers are: {', '.join(supported_schedulers)}"
+            )
+        return max(lr, lr_final)

stable_learning_control/algos/pytorch/lac/lac.py

@@ -36,6 +36,7 @@
 )
 from stable_learning_control.algos.pytorch.common.get_lr_scheduler import (
     get_lr_scheduler,
+    estimate_step_learning_rate,
 )
 from stable_learning_control.algos.pytorch.common.helpers import (
     count_vars,
@@ -1111,7 +1112,7 @@ def lac(
     actor_critic = LyapunovActorCritic if actor_critic is None else actor_critic
 
     # Ensure the environment is correctly seeded.
-    # NOTE: Done here since we donote:n't want to seed on every env.reset() call.
+    # NOTE: Done here since we don't want to seed on every env.reset() call.
     if seed is not None:
         env.np_random, _ = seeding.np_random(seed)
         env.action_space.seed(seed)
@@ -1197,29 +1198,51 @@ def lac(
     logger.log("Network structure:\n", type="info")
     logger.log(policy.ac, end="\n\n")
 
-    # Create learning rate schedulers.
-    opt_schedulers = []
-    lr_decay_ref_var = total_steps if lr_decay_ref.lower() == "steps" else epochs
-    pi_opt_scheduler = get_lr_scheduler(
-        policy._pi_optimizer, lr_decay_type, lr_a, lr_a_final, lr_decay_ref_var
-    )
-    opt_schedulers.append(pi_opt_scheduler)
-    alpha_opt_scheduler = get_lr_scheduler(
-        policy._log_alpha_optimizer, lr_decay_type, lr_a, lr_a_final, lr_decay_ref_var
-    )
-    opt_schedulers.append(alpha_opt_scheduler)
-    c_opt_scheduler = get_lr_scheduler(
-        policy._c_optimizer, lr_decay_type, lr_c, lr_c_final, lr_decay_ref_var
-    )
-    opt_schedulers.append(c_opt_scheduler)
-    labda_opt_scheduler = get_lr_scheduler(
-        policy._log_labda_optimizer,
-        lr_decay_type,
-        lr_a,
-        lr_a_final,
-        lr_decay_ref_var,
-    )
-    opt_schedulers.append(labda_opt_scheduler)
+    # Parse learning rate decay type.
+    valid_lr_decay_options = ["step", "epoch"]
+    lr_decay_ref = lr_decay_ref.lower()
+    if lr_decay_ref not in valid_lr_decay_options:
+        options = [f"'{option}'" for option in valid_lr_decay_options]
+        logger.log(
+            f"The learning rate decay reference variable was set to '{lr_decay_ref}', "
+            "which is not a valid option. Valid options are "
+            f"{', '.join(options)}. The learning rate decay reference "
+            "variable has been set to 'epoch'.",
+            type="warning",
+        )
+        lr_decay_ref = "epoch"
+
+    # Calculate the number of learning rate scheduler steps.
+    if lr_decay_ref == "step":
+        # NOTE: Decay applied at policy update to improve performance.
+        lr_decay_steps = (total_steps - update_after) / update_every
+    else:
+        lr_decay_steps = epochs
+
+    # Setup learning rate schedulers.
+    # NOTE: +1 since we start at the initial learning rate.
+    opt_schedulers = {
+        "pi": get_lr_scheduler(
+            policy._pi_optimizer, lr_decay_type, lr_a, lr_a_final, lr_decay_steps + 1
+        ),
+        "c": get_lr_scheduler(
+            policy._c_optimizer, lr_decay_type, lr_c, lr_c_final, lr_decay_steps + 1
+        ),
+        "alpha": get_lr_scheduler(
+            policy._log_alpha_optimizer,
+            lr_decay_type,
+            lr_a,
+            lr_a_final,
+            lr_decay_steps + 1,
+        ),
+        "lambda": get_lr_scheduler(
+            policy._log_labda_optimizer,
+            lr_decay_type,
+            lr_a,
+            lr_a_final,
+            lr_decay_steps + 1,
+        ),
+    }
 
     logger.setup_pytorch_saver(policy)
 
@@ -1253,6 +1276,7 @@ def lac(
         "Entropy",
     ]
     if use_tensorboard:
+        # NOTE: TensorBoard counts from 0.
         logger.log_to_tb(
             "Lr_a",
             policy._pi_optimizer.param_groups[0]["lr"],
@@ -1321,16 +1345,18 @@ def lac(
                 logger.store(**update_diagnostics)  # Log diagnostics.
 
             # Step based learning rate decay.
-            if lr_decay_ref.lower() == "step":
-                for scheduler in opt_schedulers:
+            if lr_decay_ref == "step":
+                for scheduler in opt_schedulers.values():
                     scheduler.step()
                 policy.bound_lr(
                     lr_a_final, lr_c_final, lr_a_final, lr_a_final
                 )  # Make sure lr is bounded above the final lr.
 
             # SGD batch tb logging.
             if use_tensorboard and not tb_low_log_freq:
-                logger.log_to_tb(keys=diag_tb_log_list, global_step=t)
+                logger.log_to_tb(
+                    keys=diag_tb_log_list, global_step=t
+                )  # NOTE: TensorBoard counts from 0.
 
         # End of epoch handling (Save model, test performance and log data)
         if (t + 1) % steps_per_epoch == 0:
@@ -1349,17 +1375,50 @@ def lac(
                     extend=True,
                 )
 
-            # Epoch based learning rate decay.
-            if lr_decay_ref.lower() != "step":
-                for scheduler in opt_schedulers:
-                    scheduler.step()
-                policy.bound_lr(
-                    lr_a_final, lr_c_final, lr_a_final, lr_a_final
-                )  # Make sure lr is bounded above the final lr.
+            # Retrieve current learning rates.
+            if lr_decay_ref == "step":
+                # NOTE: Estimate since 'step' decay is applied at policy update.
+                lr_actor = estimate_step_learning_rate(
+                    opt_schedulers["pi"],
+                    lr_a,
+                    lr_a_final,
+                    update_after,
+                    total_steps,
+                    t + 1,
+                )
+                lr_critic = estimate_step_learning_rate(
+                    opt_schedulers["c"],
+                    lr_c,
+                    lr_c_final,
+                    update_after,
+                    total_steps,
+                    t + 1,
+                )
+                lr_alpha = estimate_step_learning_rate(
+                    opt_schedulers["alpha"],
+                    lr_a,
+                    lr_a_final,
+                    update_after,
+                    total_steps,
+                    t + 1,
+                )
+                lr_labda = estimate_step_learning_rate(
+                    opt_schedulers["lambda"],
+                    lr_a,
+                    lr_a_final,
+                    update_after,
+                    total_steps,
+                    t + 1,
+                )
+            else:
+                lr_actor = policy._pi_optimizer.param_groups[0]["lr"]
+                lr_critic = policy._c_optimizer.param_groups[0]["lr"]
+                lr_alpha = policy._log_alpha_optimizer.param_groups[0]["lr"]
+                lr_labda = policy._log_labda_optimizer.param_groups[0]["lr"]
 
             # Log info about epoch.
             logger.log_tabular("Epoch", epoch)
-            logger.log_tabular("TotalEnvInteracts", t)
+            logger.log_tabular("TotalEnvInteracts", t + 1)
             logger.log_tabular(
                 "EpRet",
                 with_min_and_max=True,
@@ -1379,25 +1438,25 @@ def lac(
                 )
             logger.log_tabular(
                 "Lr_a",
-                policy._pi_optimizer.param_groups[0]["lr"],
+                lr_actor,
                 tb_write=use_tensorboard,
                 tb_prefix="LearningRates",
             )
             logger.log_tabular(
                 "Lr_c",
-                policy._c_optimizer.param_groups[0]["lr"],
+                lr_critic,
                 tb_write=use_tensorboard,
                 tb_prefix="LearningRates",
             )
             logger.log_tabular(
                 "Lr_alpha",
-                policy._log_alpha_optimizer.param_groups[0]["lr"],
+                lr_alpha,
                 tb_write=use_tensorboard,
                 tb_prefix="LearningRates",
             )
             logger.log_tabular(
                 "Lr_labda",
-                policy._log_labda_optimizer.param_groups[0]["lr"],
+                lr_labda,
                 tb_write=use_tensorboard,
                 tb_prefix="LearningRates",
             )
@@ -1440,7 +1499,15 @@ def lac(
                 tb_write=(use_tensorboard and tb_low_log_freq),
             )
             logger.log_tabular("Time", time.time() - start_time)
-            logger.dump_tabular(global_step=t)
+            logger.dump_tabular(global_step=t)  # NOTE: TensorBoard counts from 0.
+
+            # Epoch based learning rate decay.
+            if lr_decay_ref != "step":
+                for scheduler in opt_schedulers.values():
+                    scheduler.step()
+                policy.bound_lr(
+                    lr_a_final, lr_c_final, lr_a_final, lr_a_final
+                )  # Make sure lr is bounded above the final lr.
 
     # Export model to 'TorchScript'
     if export: