Clean the code and remove todo

lerobot/common/policies/hilserl/configuration_hilserl.py

@@ -1,23 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from dataclasses import dataclass
-
-
-@dataclass
-class HILSerlConfig:
-    pass

lerobot/common/policies/hilserl/modeling_hilserl.py

@@ -1,29 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import torch.nn as nn
-from huggingface_hub import PyTorchModelHubMixin
-
-
-class HILSerlPolicy(
-    nn.Module,
-    PyTorchModelHubMixin,
-    library_name="lerobot",
-    repo_url="https://github.com/huggingface/lerobot",
-    tags=["robotics", "hilserl"],
-):
-    pass

lerobot/common/policies/sac/modeling_sac.py

@@ -15,8 +15,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# TODO: (1) better device management
-
 import math
 from dataclasses import asdict
 from typing import Callable, List, Literal, Optional, Tuple
@@ -254,7 +252,6 @@ class SACPolicy(
         with torch.no_grad():
             next_action_preds, next_log_probs, _ = self.actor(next_observations, next_observation_features)
 
-            # TODO: (maractingi, azouitine) This is to slow, we should find a way to do this in a more efficient way
             next_action_preds = self.unnormalize_outputs({"action": next_action_preds})["action"]
 
             # 2- compute q targets
@@ -378,7 +375,6 @@ class SACPolicy(
     ) -> Tensor:
         actions_pi, log_probs, _ = self.actor(observations, observation_features)
 
-        # TODO: (maractingi, azouitine) This is to slow, we should find a way to do this in a more efficient way
         actions_pi: Tensor = self.unnormalize_outputs({"action": actions_pi})["action"]
 
         q_preds = self.critic_forward(

lerobot/scripts/server/actor_server.py

@@ -226,7 +226,6 @@ def act_with_policy(
     ### Instantiate the policy in both the actor and learner processes
     ### To avoid sending a SACPolicy object through the port, we create a policy instance
     ### on both sides, the learner sends the updated parameters every n steps to update the actor's parameters
-    # TODO: At some point we should just need make sac policy
     policy: SACPolicy = make_policy(
         cfg=cfg.policy,
         env_cfg=cfg.env,
@@ -280,7 +279,6 @@ def act_with_policy(
 
         # NOTE: We override the action if the intervention is True, because the action applied is the intervention action
         if "is_intervention" in info and info["is_intervention"]:
-            # TODO: Check the shape
             # NOTE: The action space for demonstration before hand is with the full action space
             # but sometimes for example we want to deactivate the gripper
             action = info["action_intervention"]
@@ -301,16 +299,13 @@ def act_with_policy(
                 next_state=next_obs,
                 done=done,
                 truncated=truncated,  # TODO: (azouitine) Handle truncation properly
-                complementary_info=info,  # TODO Handle information for the transition, is_demonstraction: bool
+                complementary_info=info,
             )
         )
         # assign obs to the next obs and continue the rollout
         obs = next_obs
 
-        # HACK: We have only one env but we want to batch it, it will be resolved with the torch box
-        # Because we are using a single environment we can index at zero
         if done or truncated:
-            # TODO: Handle logging for episode information
             logging.info(f"[ACTOR] Global step {interaction_step}: Episode reward: {sum_reward_episode}")
 
             update_policy_parameters(policy=policy.actor, parameters_queue=parameters_queue, device=device)
@@ -342,9 +337,10 @@ def act_with_policy(
                     }
                 )
             )
+
+            # Reset intervention counters
             sum_reward_episode = 0.0
             episode_intervention = False
-            # Reset intervention counters
             episode_intervention_steps = 0
             episode_total_steps = 0
             obs, info = online_env.reset()