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Changed the init_final value to center the starting mean and std of the policy 

Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>
This commit is contained in:
Michel Aractingi 2025-02-13 16:42:43 +01:00 committed by AdilZouitine
parent 24fb8a7f47
commit 0847b2119b
4 changed files with 5 additions and 4 deletions
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2
lerobot/common/policies/hilserl/classifier/modeling_classifier.py
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@ -148,7 +148,7 @@ class Classifier(
def predict_reward(self, x, threshold=0.6): def predict_reward(self, x, threshold=0.6):
if self.config.num_classes == 2: if self.config.num_classes == 2:
probs = self.forward(x).probabilities probs = self.forward(x).probabilities
logging.info(f"Predicted reward images: {probs}") logging.debug(f"Predicted reward images: {probs}")
return (probs > threshold).float() return (probs > threshold).float()
else: else:
return torch.argmax(self.forward(x).probabilities, dim=1) return torch.argmax(self.forward(x).probabilities, dim=1)

1
lerobot/common/policies/sac/configuration_sac.py
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@ -95,5 +95,6 @@ class SACConfig:
"use_tanh_squash": True, "use_tanh_squash": True,
"log_std_min": -5, "log_std_min": -5,
"log_std_max": 2, "log_std_max": 2,
"init_final": 0.01,
} }
) )

2
lerobot/scripts/server/actor_server.py
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@ -327,7 +327,7 @@ def send_transitions_in_chunks(transitions: list, message_queue, chunk_size: int
def get_frequency_stats(list_policy_time: list[float]) -> dict[str, float]: def get_frequency_stats(list_policy_time: list[float]) -> dict[str, float]:
stats = {} stats = {}
list_policy_fps = [1.0 / t for t in list_policy_time] list_policy_fps = [1.0 / t for t in list_policy_time]
if len(list_policy_fps) > 0: if len(list_policy_fps) > 1:
policy_fps = mean(list_policy_fps) policy_fps = mean(list_policy_fps)
quantiles_90 = quantiles(list_policy_fps, n=10)[-1] quantiles_90 = quantiles(list_policy_fps, n=10)[-1]
logging.debug(f"[ACTOR] Average policy frame rate: {policy_fps}") logging.debug(f"[ACTOR] Average policy frame rate: {policy_fps}")

4
lerobot/scripts/server/gym_manipulator.py
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@ -217,7 +217,7 @@ class HILSerlRobotEnv(gym.Env):
if torch.any(teleop_action < -self.delta_relative_bounds_size * self.delta) and torch.any( if torch.any(teleop_action < -self.delta_relative_bounds_size * self.delta) and torch.any(
teleop_action > self.delta_relative_bounds_size teleop_action > self.delta_relative_bounds_size
): ):
print( logging.debug(
f"Relative teleop delta exceeded bounds {self.delta_relative_bounds_size}, teleop_action {teleop_action}\n" f"Relative teleop delta exceeded bounds {self.delta_relative_bounds_size}, teleop_action {teleop_action}\n"
f"lower bounds condition {teleop_action < -self.delta_relative_bounds_size}\n" f"lower bounds condition {teleop_action < -self.delta_relative_bounds_size}\n"
f"upper bounds condition {teleop_action > self.delta_relative_bounds_size}" f"upper bounds condition {teleop_action > self.delta_relative_bounds_size}"
@ -318,7 +318,7 @@ class RewardWrapper(gym.Wrapper):
) )
info["Reward classifer frequency"] = 1 / (time.perf_counter() - start_time) info["Reward classifer frequency"] = 1 / (time.perf_counter() - start_time)
logging.info(f"Reward: {reward}") # logging.info(f"Reward: {reward}")
if reward == 1.0: if reward == 1.0:
terminated = True terminated = True