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fix(tf2): correct off-by-one error in learning rate decay calculation #415

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Feb 19, 2024
Merged

fix(tf2): correct off-by-one error in learning rate decay calculation #415

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22 changes: 7 additions & 15 deletions stable_learning_control/algos/tf2/lac/lac.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1101,7 +1101,9 @@ def lac(
# 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
lr_decay_steps = (
total_steps - update_after
) / update_every + 1 # NOTE: +1 since we start at the initial learning rate.
else:
lr_decay_steps = epochs

Expand All @@ -1110,16 +1112,6 @@ def lac(
lr_a_scheduler = get_lr_scheduler(lr_decay_type, lr_a, lr_a_final, lr_decay_steps)
lr_c_scheduler = get_lr_scheduler(lr_decay_type, lr_c, lr_c_final, lr_decay_steps)

# Create step based learning rate schedulers.
# NOTE: Used to estimate the learning rate at each step.
if lr_decay_ref == "step":
lr_a_step_scheduler = get_lr_scheduler(
lr_decay_type, lr_a, lr_a_final, lr_decay_steps + 1
)
lr_c_step_scheduler = get_lr_scheduler(
lr_decay_type, lr_c, lr_c_final, lr_decay_steps + 1
)

# Restore policy if supplied.
if start_policy is not None:
logger.log(f"Restoring model from '{start_policy}'.", type="info")
Expand Down Expand Up @@ -1303,10 +1295,10 @@ def lac(
# Retrieve current learning rates.
if lr_decay_ref == "step":
progress = max((t + 1) - update_after, 0) / update_every
lr_actor = lr_a_step_scheduler(progress)
lr_critic = lr_c_step_scheduler(progress)
lr_alpha = lr_a_step_scheduler(progress)
lr_labda = lr_a_step_scheduler(progress)
lr_actor = lr_a_scheduler(progress)
lr_critic = lr_c_scheduler(progress)
lr_alpha = lr_a_scheduler(progress)
lr_labda = lr_a_scheduler(progress)
else:
lr_actor = policy._pi_optimizer.lr.numpy()
lr_critic = policy._c_optimizer.lr.numpy()
Expand Down
20 changes: 6 additions & 14 deletions stable_learning_control/algos/tf2/sac/sac.py
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Original file line number Diff line number Diff line change
Expand Up @@ -968,7 +968,9 @@ def sac(
# 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
lr_decay_steps = (
total_steps - update_after
) / update_every + 1 # NOTE: +1 since we start at the initial learning rate.
else:
lr_decay_steps = epochs

Expand All @@ -977,16 +979,6 @@ def sac(
lr_a_scheduler = get_lr_scheduler(lr_decay_type, lr_a, lr_a_final, lr_decay_steps)
lr_c_scheduler = get_lr_scheduler(lr_decay_type, lr_c, lr_c_final, lr_decay_steps)

# Create step based learning rate schedulers.
# NOTE: Used to estimate the learning rate at each step.
if lr_decay_ref == "step":
lr_a_step_scheduler = get_lr_scheduler(
lr_decay_type, lr_a, lr_a_final, lr_decay_steps + 1
)
lr_c_step_scheduler = get_lr_scheduler(
lr_decay_type, lr_c, lr_c_final, lr_decay_steps + 1
)

# Restore policy if supplied.
if start_policy is not None:
logger.log(f"Restoring model from '{start_policy}'.", type="info")
Expand Down Expand Up @@ -1138,9 +1130,9 @@ def sac(
# Retrieve current learning rates.
if lr_decay_ref == "step":
progress = max((t + 1) - update_after, 0) / update_every
lr_actor = lr_a_step_scheduler(progress)
lr_critic = lr_c_step_scheduler(progress)
lr_alpha = lr_a_step_scheduler(progress)
lr_actor = lr_a_scheduler(progress)
lr_critic = lr_c_scheduler(progress)
lr_alpha = lr_a_scheduler(progress)
else:
lr_actor = policy._pi_optimizer.lr.numpy()
lr_critic = policy._c_optimizer.lr.numpy()
Expand Down