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- Fixed big issue in the loading of the policy parameters sent by the learner to the actor -- pass only the actor to the update_policy_parameters and remove strict=False

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- Fixed big issue in the normalization of the actions in the `forward` function of the critic -- remove the `torch.no_grad` decorator in `normalize.py` in the normalization function
- Fixed performance issue to boost the optimization frequency by setting the storage device to be the same as the device of learning.

Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>
This commit is contained in:
Michel Aractingi
2025-02-19 16:22:51 +00:00
parent befa1fe9af
commit ff47c0b0d3
7 changed files with 68 additions and 57 deletions
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6
lerobot/scripts/server/actor_server.py
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@@ -166,7 +166,7 @@ def update_policy_parameters(policy: SACPolicy, parameters_queue: queue.Queue, d
logging.info("[ACTOR] Load new parameters from Learner.")
state_dict = parameters_queue.get()
state_dict = move_state_dict_to_device(state_dict, device=device)
policy.load_state_dict(state_dict, strict=False)
policy.load_state_dict(state_dict)
def act_with_policy(cfg: DictConfig, robot: Robot, reward_classifier: nn.Module):
@@ -182,7 +182,7 @@ def act_with_policy(cfg: DictConfig, robot: Robot, reward_classifier: nn.Module)
logging.info("make_env online")
online_env = make_robot_env(robot=robot, reward_classifier=reward_classifier, cfg=cfg.env)
online_env = make_robot_env(robot=robot, reward_classifier=reward_classifier, cfg=cfg)
set_global_seed(cfg.seed)
device = get_safe_torch_device(cfg.device, log=True)
@@ -283,7 +283,7 @@ def act_with_policy(cfg: DictConfig, robot: Robot, reward_classifier: nn.Module)
# TODO: Handle logging for episode information
logging.info(f"[ACTOR] Global step {interaction_step}: Episode reward: {sum_reward_episode}")
update_policy_parameters(policy=policy, parameters_queue=parameters_queue, device=device)
update_policy_parameters(policy=policy.actor, parameters_queue=parameters_queue, device=device)
if len(list_transition_to_send_to_learner) > 0:
send_transitions_in_chunks(