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Refactor SAC policy and training loop to enhance discrete action support

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- Updated SACPolicy to conditionally compute losses for grasp critic based on num_discrete_actions.
- Simplified forward method to return loss outputs as a dictionary for better clarity.
- Adjusted learner_server to handle both main and grasp critic losses during training.
- Ensured optimizers are created conditionally for grasp critic based on configuration settings.
This commit is contained in:
AdilZouitine
2025-04-01 11:42:28 +00:00
committed by Michel Aractingi
parent 7361a11a4d
commit f83d215e7a
3 changed files with 86 additions and 90 deletions
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120
lerobot/scripts/server/learner_server.py
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@@ -392,32 +392,30 @@ def add_actor_information_and_train(
"next_observation_feature": next_observation_features,
}
# Use the forward method for critic loss
loss_critic = policy.forward(forward_batch, model="critic")
# Use the forward method for critic loss (includes both main critic and grasp critic)
critic_output = policy.forward(forward_batch, model="critic")
# Main critic optimization
loss_critic = critic_output["loss_critic"]
optimizers["critic"].zero_grad()
loss_critic.backward()
# clip gradients
critic_grad_norm = torch.nn.utils.clip_grad_norm_(
parameters=policy.critic_ensemble.parameters(), max_norm=clip_grad_norm_value
)
optimizers["critic"].step()
# Add gripper critic optimization
loss_grasp_critic = policy.forward(forward_batch, model="grasp_critic")
optimizers["grasp_critic"].zero_grad()
loss_grasp_critic.backward()
# clip gradients
grasp_critic_grad_norm = torch.nn.utils.clip_grad_norm_(
parameters=policy.grasp_critic.parameters(), max_norm=clip_grad_norm_value
)
optimizers["grasp_critic"].step()
# Grasp critic optimization (if available)
if "loss_grasp_critic" in critic_output and hasattr(policy, "grasp_critic"):
loss_grasp_critic = critic_output["loss_grasp_critic"]
optimizers["grasp_critic"].zero_grad()
loss_grasp_critic.backward()
grasp_critic_grad_norm = torch.nn.utils.clip_grad_norm_(
parameters=policy.grasp_critic.parameters(), max_norm=clip_grad_norm_value
)
optimizers["grasp_critic"].step()
# Update target networks
policy.update_target_networks()
policy.update_grasp_target_networks()
batch = replay_buffer.sample(batch_size=batch_size)
@@ -450,81 +448,80 @@ def add_actor_information_and_train(
"next_observation_feature": next_observation_features,
}
# Use the forward method for critic loss
loss_critic = policy.forward(forward_batch, model="critic")
# Use the forward method for critic loss (includes both main critic and grasp critic)
critic_output = policy.forward(forward_batch, model="critic")
# Main critic optimization
loss_critic = critic_output["loss_critic"]
optimizers["critic"].zero_grad()
loss_critic.backward()
# clip gradients
critic_grad_norm = torch.nn.utils.clip_grad_norm_(
parameters=policy.critic_ensemble.parameters(), max_norm=clip_grad_norm_value
).item()
optimizers["critic"].step()
training_infos = {}
training_infos["loss_critic"] = loss_critic.item()
training_infos["critic_grad_norm"] = critic_grad_norm
# Initialize training info dictionary
training_infos = {
"loss_critic": loss_critic.item(),
"critic_grad_norm": critic_grad_norm,
}
# Add gripper critic optimization
loss_grasp_critic = policy.forward(forward_batch, model="grasp_critic")
optimizers["grasp_critic"].zero_grad()
loss_grasp_critic.backward()
# clip gradients
grasp_critic_grad_norm = torch.nn.utils.clip_grad_norm_(
parameters=policy.grasp_critic.parameters(), max_norm=clip_grad_norm_value
)
optimizers["grasp_critic"].step()
# Add training info for the grasp critic
training_infos["loss_grasp_critic"] = loss_grasp_critic.item()
training_infos["grasp_critic_grad_norm"] = grasp_critic_grad_norm
# Grasp critic optimization (if available)
if "loss_grasp_critic" in critic_output:
loss_grasp_critic = critic_output["loss_grasp_critic"]
optimizers["grasp_critic"].zero_grad()
loss_grasp_critic.backward()
grasp_critic_grad_norm = torch.nn.utils.clip_grad_norm_(
parameters=policy.grasp_critic.parameters(), max_norm=clip_grad_norm_value
).item()
optimizers["grasp_critic"].step()
# Add grasp critic info to training info
training_infos["loss_grasp_critic"] = loss_grasp_critic.item()
training_infos["grasp_critic_grad_norm"] = grasp_critic_grad_norm
# Actor and temperature optimization (at specified frequency)
if optimization_step % policy_update_freq == 0:
for _ in range(policy_update_freq):
# Use the forward method for actor loss
loss_actor = policy.forward(forward_batch, model="actor")
# Actor optimization
actor_output = policy.forward(forward_batch, model="actor")
loss_actor = actor_output["loss_actor"]
optimizers["actor"].zero_grad()
loss_actor.backward()
# clip gradients
actor_grad_norm = torch.nn.utils.clip_grad_norm_(
parameters=policy.actor.parameters_to_optimize, max_norm=clip_grad_norm_value
).item()
optimizers["actor"].step()
# Add actor info to training info
training_infos["loss_actor"] = loss_actor.item()
training_infos["actor_grad_norm"] = actor_grad_norm
# Temperature optimization using forward method
loss_temperature = policy.forward(forward_batch, model="temperature")
# Temperature optimization
temperature_output = policy.forward(forward_batch, model="temperature")
loss_temperature = temperature_output["loss_temperature"]
optimizers["temperature"].zero_grad()
loss_temperature.backward()
# clip gradients
temp_grad_norm = torch.nn.utils.clip_grad_norm_(
parameters=[policy.log_alpha], max_norm=clip_grad_norm_value
).item()
optimizers["temperature"].step()
# Add temperature info to training info
training_infos["loss_temperature"] = loss_temperature.item()
training_infos["temperature_grad_norm"] = temp_grad_norm
training_infos["temperature"] = policy.temperature
# Update temperature
policy.update_temperature()
# Check if it's time to push updated policy to actors
# Push policy to actors if needed
if time.time() - last_time_policy_pushed > policy_parameters_push_frequency:
push_actor_policy_to_queue(parameters_queue=parameters_queue, policy=policy)
last_time_policy_pushed = time.time()
# Update target networks
policy.update_target_networks()
policy.update_grasp_target_networks()
# Log training metrics at specified intervals
if optimization_step % log_freq == 0:
@@ -727,7 +724,7 @@ def save_training_checkpoint(
logging.info("Resume training")
def make_optimizers_and_scheduler(cfg, policy: nn.Module):
def make_optimizers_and_scheduler(cfg: TrainPipelineConfig, policy: nn.Module):
"""
Creates and returns optimizers for the actor, critic, and temperature components of a reinforcement learning policy.
@@ -759,17 +756,20 @@ def make_optimizers_and_scheduler(cfg, policy: nn.Module):
lr=cfg.policy.actor_lr,
)
optimizer_critic = torch.optim.Adam(params=policy.critic_ensemble.parameters(), lr=cfg.policy.critic_lr)
optimizer_grasp_critic = torch.optim.Adam(
params=policy.grasp_critic.parameters(), lr=policy.config.grasp_critic_lr
)
if cfg.policy.num_discrete_actions is not None:
optimizer_grasp_critic = torch.optim.Adam(
params=policy.grasp_critic.parameters(), lr=policy.critic_lr
)
optimizer_temperature = torch.optim.Adam(params=[policy.log_alpha], lr=cfg.policy.critic_lr)
lr_scheduler = None
optimizers = {
"actor": optimizer_actor,
"critic": optimizer_critic,
"grasp_critic": optimizer_grasp_critic,
"temperature": optimizer_temperature,
}
if cfg.policy.num_discrete_actions is not None:
optimizers["grasp_critic"] = optimizer_grasp_critic
return optimizers, lr_scheduler