fixed softmax temp

lerobot/common/envs/configs.py

@@ -171,7 +171,6 @@ class VideoRecordConfig:
 class WrapperConfig:
     """Configuration for environment wrappers."""
 
-    delta_action: float | None = None
     joint_masking_action_space: list[bool] | None = None
 
 
@@ -191,7 +190,6 @@ class EnvWrapperConfig:
     """Configuration for environment wrappers."""
 
     display_cameras: bool = False
-    delta_action: float = 0.1
     use_relative_joint_positions: bool = True
     add_joint_velocity_to_observation: bool = False
     add_ee_pose_to_observation: bool = False
@@ -203,8 +201,9 @@ class EnvWrapperConfig:
     joint_masking_action_space: Optional[Any] = None
     ee_action_space_params: Optional[EEActionSpaceConfig] = None
     use_gripper: bool = False
-    gripper_quantization_threshold: float = 0.8
-    gripper_penalty: float = 0.0
+    gripper_quantization_threshold: float | None = None
+    gripper_penalty: float  = 0.0
+    gripper_penalty_in_reward: bool = False
     open_gripper_on_reset: bool = False
 
 

lerobot/common/policies/sac/modeling_sac.py

@@ -129,7 +129,7 @@ class SACPolicy(
                 encoder=encoder_critic,
                 input_dim=encoder_critic.output_dim,
                 output_dim=config.num_discrete_actions,
-                softmax_temperature=1.0,
+                softmax_temperature=.15,
                 **asdict(config.grasp_critic_network_kwargs),
             )
 
@@ -138,14 +138,14 @@ class SACPolicy(
                 encoder=encoder_critic,
                 input_dim=encoder_critic.output_dim,
                 output_dim=config.num_discrete_actions,
-                softmax_temperature=1.0,
+                softmax_temperature=0.15,
                 **asdict(config.grasp_critic_network_kwargs),
             )
 
             self.grasp_critic_target.load_state_dict(self.grasp_critic.state_dict())
 
-            self.grasp_critic = torch.compile(self.grasp_critic)
-            self.grasp_critic_target = torch.compile(self.grasp_critic_target)
+            # self.grasp_critic = torch.compile(self.grasp_critic)
+            # self.grasp_critic_target = torch.compile(self.grasp_critic_target)
 
         self.actor = Policy(
             encoder=encoder_actor,
@@ -428,6 +428,7 @@ class SACPolicy(
         actions_discrete = torch.round(actions_discrete)
         actions_discrete = actions_discrete.long()
 
+        gripper_penalties: Tensor | None = None
         if complementary_info is not None:
             gripper_penalties: Tensor | None = complementary_info.get("gripper_penalty")
 
@@ -569,7 +570,6 @@ class SACObservationEncoder(nn.Module):
         obs_dict = self.input_normalization(obs_dict)
         if len(self.all_image_keys) > 0 and vision_encoder_cache is None:
             vision_encoder_cache = self.get_image_features(obs_dict)
-            feat.append(vision_encoder_cache)
 
         if vision_encoder_cache is not None:
             feat.append(vision_encoder_cache)
@@ -786,6 +786,7 @@ class GraspCritic(nn.Module):
         super().__init__()
         self.encoder = encoder
         self.output_dim = output_dim
+        self.softmax_temperature = softmax_temperature
 
         self.net = MLP(
             input_dim=input_dim,
@@ -797,6 +798,7 @@ class GraspCritic(nn.Module):
         )
 
         self.output_layer = nn.Linear(in_features=hidden_dims[-1], out_features=self.output_dim)
+        init_final = 0.05
         if init_final is not None:
             nn.init.uniform_(self.output_layer.weight, -init_final, init_final)
             nn.init.uniform_(self.output_layer.bias, -init_final, init_final)

lerobot/common/robot_devices/control_utils.py

@@ -221,7 +221,6 @@ def record_episode(
         events=events,
         policy=policy,
         fps=fps,
-        # record_delta_actions=record_delta_actions,
         teleoperate=policy is None,
         single_task=single_task,
     )
@@ -267,8 +266,6 @@ def control_loop(
 
         if teleoperate:
             observation, action = robot.teleop_step(record_data=True)
-            # if record_delta_actions:
-            #     action["action"] = action["action"] - current_joint_positions
         else:
             observation = robot.capture_observation()
 

lerobot/scripts/server/actor_server.py

@@ -250,28 +250,18 @@ def act_with_policy(
             logging.info("[ACTOR] Shutting down act_with_policy")
             return
 
-        if interaction_step >= cfg.policy.online_step_before_learning:
-            # Time policy inference and check if it meets FPS requirement
-            with TimerManager(
-                elapsed_time_list=list_policy_time,
-                label="Policy inference time",
-                log=False,
-            ) as timer:  # noqa: F841
-                action = policy.select_action(batch=obs)
-            policy_fps = 1.0 / (list_policy_time[-1] + 1e-9)
+        # Time policy inference and check if it meets FPS requirement
+        with TimerManager(
+            elapsed_time_list=list_policy_time,
+            label="Policy inference time",
+            log=False,
+        ) as timer:  # noqa: F841
+            action = policy.select_action(batch=obs)
+        policy_fps = 1.0 / (list_policy_time[-1] + 1e-9)
 
-            log_policy_frequency_issue(policy_fps=policy_fps, cfg=cfg, interaction_step=interaction_step)
+        log_policy_frequency_issue(policy_fps=policy_fps, cfg=cfg, interaction_step=interaction_step)
 
-            next_obs, reward, done, truncated, info = online_env.step(action.squeeze(dim=0).cpu().numpy())
-        else:
-            # TODO (azouitine): Make a custom space for torch tensor
-            action = online_env.action_space.sample()
-            next_obs, reward, done, truncated, info = online_env.step(action)
-
-            # HACK: We have only one env but we want to batch it, it will be resolved with the torch box
-            action = (
-                torch.from_numpy(action[0]).to(device, non_blocking=device.type == "cuda").unsqueeze(dim=0)
-            )
+        next_obs, reward, done, truncated, info = online_env.step(action.squeeze(dim=0).cpu().numpy())
 
         sum_reward_episode += float(reward)
         # Increment total steps counter for intervention rate
@@ -291,7 +281,7 @@ def act_with_policy(
         for key, tensor in obs.items():
             if torch.isnan(tensor).any():
                 logging.error(f"[ACTOR] NaN values found in obs[{key}] at step {interaction_step}")
-
+            
         list_transition_to_send_to_learner.append(
             Transition(
                 state=obs,

lerobot/scripts/server/buffer.py

@@ -79,7 +79,7 @@ def move_transition_to_device(transition: Transition, device: str = "cpu") -> Tr
                 transition["complementary_info"][key] = val.to(device, non_blocking=non_blocking)
             elif isinstance(val, (int, float, bool)):
                 transition["complementary_info"][key] = torch.tensor(
-                    val, device=device, non_blocking=non_blocking
+                    val, device=device
                 )
             else:
                 raise ValueError(f"Unsupported type {type(val)} for complementary_info[{key}]")
@@ -269,7 +269,7 @@ class ReplayBuffer:
                     self.complementary_info[key] = torch.empty(
                         (self.capacity, *value_shape), device=self.storage_device
                     )
-                elif isinstance(value, (int, float)):
+                elif isinstance(value, (int, float, bool)):
                     # Handle scalar values similar to reward
                     self.complementary_info[key] = torch.empty((self.capacity,), device=self.storage_device)
                 else:
@@ -505,7 +505,6 @@ class ReplayBuffer:
         state_keys: Optional[Sequence[str]] = None,
         capacity: Optional[int] = None,
         action_mask: Optional[Sequence[int]] = None,
-        action_delta: Optional[float] = None,
         image_augmentation_function: Optional[Callable] = None,
         use_drq: bool = True,
         storage_device: str = "cpu",
@@ -520,7 +519,6 @@ class ReplayBuffer:
             state_keys (Optional[Sequence[str]]): The list of keys that appear in `state` and `next_state`.
             capacity (Optional[int]): Buffer capacity. If None, uses dataset length.
             action_mask (Optional[Sequence[int]]): Indices of action dimensions to keep.
-            action_delta (Optional[float]): Factor to divide actions by.
             image_augmentation_function (Optional[Callable]): Function for image augmentation.
                 If None, uses default random shift with pad=4.
             use_drq (bool): Whether to use DrQ image augmentation when sampling.
@@ -565,8 +563,6 @@ class ReplayBuffer:
                 else:
                     first_action = first_action[:, action_mask]
 
-            if action_delta is not None:
-                first_action = first_action / action_delta
 
             # Get complementary info if available
             first_complementary_info = None
@@ -598,8 +594,6 @@ class ReplayBuffer:
                 else:
                     action = action[:, action_mask]
 
-            if action_delta is not None:
-                action = action / action_delta
 
             replay_buffer.add(
                 state=data["state"],

lerobot/scripts/server/end_effector_control_utils.py

@@ -258,24 +258,24 @@ class GamepadController(InputController):
                 elif event.button == 0:
                     self.episode_end_status = "rerecord_episode"
 
-                # RB button (6) for opening gripper
+                # LT button for closing gripper
                 elif event.button == 6:
-                    self.open_gripper_command = True
-
-                # LT button (7) for closing gripper
-                elif event.button == 7:
                     self.close_gripper_command = True
 
+                # RB button for opening gripper
+                elif event.button == 7:
+                    self.open_gripper_command = True
+
             # Reset episode status on button release
             elif event.type == pygame.JOYBUTTONUP:
                 if event.button in [0, 2, 3]:
                     self.episode_end_status = None
-
-                elif event.button == 6:
-                    self.open_gripper_command = False
-
-                elif event.button == 7:
+                
+                if event.button == 6:
                     self.close_gripper_command = False
+                
+                if event.button == 7:
+                    self.open_gripper_command = False
 
             # Check for RB button (typically button 5) for intervention flag
             if self.joystick.get_button(5):

lerobot/scripts/server/gym_manipulator.py

@@ -42,7 +42,6 @@ class HILSerlRobotEnv(gym.Env):
         self,
         robot,
         use_delta_action_space: bool = True,
-        delta: float | None = None,
         display_cameras: bool = False,
     ):
         """
@@ -55,8 +54,6 @@ class HILSerlRobotEnv(gym.Env):
             robot: The robot interface object used to connect and interact with the physical robot.
             use_delta_action_space (bool): If True, uses a delta (relative) action space for joint control. Otherwise, absolute
                 joint positions are used.
-            delta (float or None): A scaling factor for the relative adjustments applied to joint positions. Should be a value between
-                0 and 1 when using a delta action space.
             display_cameras (bool): If True, the robot's camera feeds will be displayed during execution.
         """
         super().__init__()
@@ -74,7 +71,6 @@ class HILSerlRobotEnv(gym.Env):
         self.current_step = 0
         self.episode_data = None
 
-        self.delta = delta
         self.use_delta_action_space = use_delta_action_space
         self.current_joint_positions = self.robot.follower_arms["main"].read("Present_Position")
 
@@ -374,7 +370,7 @@ class RewardWrapper(gym.Wrapper):
         self.device = device
 
     def step(self, action):
-        observation, _, terminated, truncated, info = self.env.step(action)
+        observation, reward, terminated, truncated, info = self.env.step(action)
         images = [
             observation[key].to(self.device, non_blocking=self.device.type == "cuda")
             for key in observation
@@ -382,15 +378,17 @@ class RewardWrapper(gym.Wrapper):
         ]
         start_time = time.perf_counter()
         with torch.inference_mode():
-            reward = (
+            success = (
                 self.reward_classifier.predict_reward(images, threshold=0.8)
                 if self.reward_classifier is not None
                 else 0.0
             )
         info["Reward classifer frequency"] = 1 / (time.perf_counter() - start_time)
 
-        if reward == 1.0:
+        if success == 1.0:
             terminated = True
+            reward = 1.0
+
         return observation, reward, terminated, truncated, info
 
     def reset(self, seed=None, options=None):
@@ -555,6 +553,9 @@ class ImageCropResizeWrapper(gym.Wrapper):
             # TODO(michel-aractingi): Bug in resize, it returns values outside [0, 1]
             obs[k] = obs[k].clamp(0.0, 1.0)
 
+            # import cv2
+            # cv2.imwrite(f"tmp_img/{k}.jpg", cv2.cvtColor(obs[k].squeeze(0).permute(1,2,0).cpu().numpy()*255, cv2.COLOR_RGB2BGR))
+
             # Check for NaNs after processing
             if torch.isnan(obs[k]).any():
                 logging.error(f"NaN values detected in observation {k} after crop and resize")
@@ -720,19 +721,31 @@ class ResetWrapper(gym.Wrapper):
         env: HILSerlRobotEnv,
         reset_pose: np.ndarray | None = None,
         reset_time_s: float = 5,
+        open_gripper_on_reset: bool = False
     ):
         super().__init__(env)
         self.reset_time_s = reset_time_s
         self.reset_pose = reset_pose
         self.robot = self.unwrapped.robot
+        self.open_gripper_on_reset = open_gripper_on_reset
 
     def reset(self, *, seed=None, options=None):
+
+
         if self.reset_pose is not None:
             start_time = time.perf_counter()
             log_say("Reset the environment.", play_sounds=True)
             reset_follower_position(self.robot, self.reset_pose)
             busy_wait(self.reset_time_s - (time.perf_counter() - start_time))
             log_say("Reset the environment done.", play_sounds=True)
+            if self.open_gripper_on_reset:
+                current_joint_pos = self.robot.follower_arms["main"].read("Present_Position")
+                current_joint_pos[-1] = MAX_GRIPPER_COMMAND
+                self.robot.send_action(torch.from_numpy(current_joint_pos))
+                busy_wait(0.1)
+                current_joint_pos[-1] = 0.0
+                self.robot.send_action(torch.from_numpy(current_joint_pos))
+                busy_wait(0.2)
         else:
             log_say(
                 f"Manually reset the environment for {self.reset_time_s} seconds.",
@@ -762,37 +775,48 @@ class BatchCompitableWrapper(gym.ObservationWrapper):
 
 
 class GripperPenaltyWrapper(gym.RewardWrapper):
-    def __init__(self, env, penalty: float = -0.1):
+    def __init__(self, env, penalty: float = -0.1, gripper_penalty_in_reward: bool = True):
         super().__init__(env)
         self.penalty = penalty
+        self.gripper_penalty_in_reward = gripper_penalty_in_reward
         self.last_gripper_state = None
+        
 
     def reward(self, reward, action):
         gripper_state_normalized = self.last_gripper_state / MAX_GRIPPER_COMMAND
 
-        if isinstance(action, tuple):
-            action = action[0]
-        action_normalized = action[-1] / MAX_GRIPPER_COMMAND
+        action_normalized = action - 1.0 #action / MAX_GRIPPER_COMMAND
 
-        gripper_penalty_bool = (gripper_state_normalized < 0.1 and action_normalized > 0.9) or (
-            gripper_state_normalized > 0.9 and action_normalized < 0.1
+        gripper_penalty_bool = (gripper_state_normalized < 0.75 and action_normalized > 0.5) or (
+            gripper_state_normalized > 0.75 and action_normalized < -0.5
         )
-        breakpoint()
 
-        return reward + self.penalty * gripper_penalty_bool
+        return reward + self.penalty * int(gripper_penalty_bool)
 
     def step(self, action):
         self.last_gripper_state = self.unwrapped.robot.follower_arms["main"].read("Present_Position")[-1]
+        if isinstance(action, tuple):
+            gripper_action = action[0][-1]
+        else:
+            gripper_action = action[-1]
         obs, reward, terminated, truncated, info = self.env.step(action)
-        reward = self.reward(reward, action)
+        gripper_penalty = self.reward(reward, gripper_action)
+
+        if self.gripper_penalty_in_reward:
+            reward += gripper_penalty
+        else:
+            info["gripper_penalty"] = gripper_penalty
+            
         return obs, reward, terminated, truncated, info
 
     def reset(self, **kwargs):
         self.last_gripper_state = None
-        return super().reset(**kwargs)
+        obs, info = super().reset(**kwargs)
+        if self.gripper_penalty_in_reward:
+            info["gripper_penalty"] = 0.0
+        return obs, info
 
-
-class GripperQuantizationWrapper(gym.ActionWrapper):
+class GripperActionWrapper(gym.ActionWrapper):
     def __init__(self, env, quantization_threshold: float = 0.2):
         super().__init__(env)
         self.quantization_threshold = quantization_threshold
@@ -801,16 +825,18 @@ class GripperQuantizationWrapper(gym.ActionWrapper):
         is_intervention = False
         if isinstance(action, tuple):
             action, is_intervention = action
-
         gripper_command = action[-1]
-        # Quantize gripper command to -1, 0 or 1
-        if gripper_command < -self.quantization_threshold:
-            gripper_command = -MAX_GRIPPER_COMMAND
-        elif gripper_command > self.quantization_threshold:
-            gripper_command = MAX_GRIPPER_COMMAND
-        else:
-            gripper_command = 0.0
 
+        # Gripper actions are between 0, 2
+        # we want to quantize them to -1, 0 or 1
+        gripper_command = gripper_command - 1.0
+
+        if self.quantization_threshold is not None:
+            # Quantize gripper command to -1, 0 or 1
+            gripper_command = (
+                np.sign(gripper_command) if abs(gripper_command) > self.quantization_threshold else 0.0
+            )
+        gripper_command = gripper_command * MAX_GRIPPER_COMMAND
         gripper_state = self.unwrapped.robot.follower_arms["main"].read("Present_Position")[-1]
         gripper_action = np.clip(gripper_state + gripper_command, 0, MAX_GRIPPER_COMMAND)
         action[-1] = gripper_action.item()
@@ -836,10 +862,12 @@ class EEActionWrapper(gym.ActionWrapper):
             ]
         )
         if self.use_gripper:
-            action_space_bounds = np.concatenate([action_space_bounds, [1.0]])
+            # gripper actions open at 2.0, and closed at 0.0
+            min_action_space_bounds = np.concatenate([-action_space_bounds, [0.0]])
+            max_action_space_bounds = np.concatenate([action_space_bounds, [2.0]])
         ee_action_space = gym.spaces.Box(
-            low=-action_space_bounds,
-            high=action_space_bounds,
+            low=min_action_space_bounds,
+            high=max_action_space_bounds,
             shape=(3 + int(self.use_gripper),),
             dtype=np.float32,
         )
@@ -997,11 +1025,11 @@ class GamepadControlWrapper(gym.Wrapper):
         if self.use_gripper:
             gripper_command = self.controller.gripper_command()
             if gripper_command == "open":
-                gamepad_action = np.concatenate([gamepad_action, [1.0]])
+                gamepad_action = np.concatenate([gamepad_action, [2.0]])
             elif gripper_command == "close":
-                gamepad_action = np.concatenate([gamepad_action, [-1.0]])
-            else:
                 gamepad_action = np.concatenate([gamepad_action, [0.0]])
+            else:
+                gamepad_action = np.concatenate([gamepad_action, [1.0]])
 
         # Check episode ending buttons
         # We'll rely on controller.get_episode_end_status() which returns "success", "failure", or None
@@ -1141,7 +1169,6 @@ def make_robot_env(cfg) -> gym.vector.VectorEnv:
     env = HILSerlRobotEnv(
         robot=robot,
         display_cameras=cfg.wrapper.display_cameras,
-        delta=cfg.wrapper.delta_action,
         use_delta_action_space=cfg.wrapper.use_relative_joint_positions
         and cfg.wrapper.ee_action_space_params is None,
     )
@@ -1165,10 +1192,11 @@ def make_robot_env(cfg) -> gym.vector.VectorEnv:
     # env = RewardWrapper(env=env, reward_classifier=reward_classifier, device=cfg.device)
     env = TimeLimitWrapper(env=env, control_time_s=cfg.wrapper.control_time_s, fps=cfg.fps)
     if cfg.wrapper.use_gripper:
-        env = GripperQuantizationWrapper(
+        env = GripperActionWrapper(
             env=env, quantization_threshold=cfg.wrapper.gripper_quantization_threshold
         )
-        # env = GripperPenaltyWrapper(env=env, penalty=cfg.wrapper.gripper_penalty)
+        if cfg.wrapper.gripper_penalty is not None:
+            env = GripperPenaltyWrapper(env=env, penalty=cfg.wrapper.gripper_penalty, gripper_penalty_in_reward=cfg.wrapper.gripper_penalty_in_reward)
 
     if cfg.wrapper.ee_action_space_params is not None:
         env = EEActionWrapper(
@@ -1176,6 +1204,7 @@ def make_robot_env(cfg) -> gym.vector.VectorEnv:
             ee_action_space_params=cfg.wrapper.ee_action_space_params,
             use_gripper=cfg.wrapper.use_gripper,
         )
+
     if cfg.wrapper.ee_action_space_params is not None and cfg.wrapper.ee_action_space_params.use_gamepad:
         # env = ActionScaleWrapper(env=env, ee_action_space_params=cfg.wrapper.ee_action_space_params)
         env = GamepadControlWrapper(
@@ -1192,6 +1221,7 @@ def make_robot_env(cfg) -> gym.vector.VectorEnv:
         env=env,
         reset_pose=cfg.wrapper.fixed_reset_joint_positions,
         reset_time_s=cfg.wrapper.reset_time_s,
+        open_gripper_on_reset=cfg.wrapper.open_gripper_on_reset
     )
     if cfg.wrapper.ee_action_space_params is None and cfg.wrapper.joint_masking_action_space is not None:
         env = JointMaskingActionSpace(env=env, mask=cfg.wrapper.joint_masking_action_space)
@@ -1341,11 +1371,10 @@ def record_dataset(env, policy, cfg):
         dataset.push_to_hub()
 
 
-def replay_episode(env, repo_id, root=None, episode=0):
+def replay_episode(env, cfg):
     from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
 
-    local_files_only = root is not None
-    dataset = LeRobotDataset(repo_id, root=root, episodes=[episode], local_files_only=local_files_only)
+    dataset = LeRobotDataset(cfg.repo_id, root=cfg.dataset_root, episodes=[cfg.episode])
     env.reset()
 
     actions = dataset.hf_dataset.select_columns("action")
@@ -1353,7 +1382,7 @@ def replay_episode(env, repo_id, root=None, episode=0):
     for idx in range(dataset.num_frames):
         start_episode_t = time.perf_counter()
 
-        action = actions[idx]["action"][:4]
+        action = actions[idx]["action"]
         env.step((action, False))
         # env.step((action / env.unwrapped.delta, False))
 
@@ -1384,9 +1413,7 @@ def main(cfg: EnvConfig):
     if cfg.mode == "replay":
         replay_episode(
             env,
-            cfg.replay_repo_id,
-            root=cfg.dataset_root,
-            episode=cfg.replay_episode,
+            cfg=cfg,
         )
         exit()
 

lerobot/scripts/server/learner_server.py

@@ -380,6 +380,7 @@ def add_actor_information_and_train(
         for _ in range(utd_ratio - 1):
             # Sample from the iterators
             batch = next(online_iterator)
+            # batch = replay_buffer.sample(batch_size)
 
             if dataset_repo_id is not None:
                 batch_offline = next(offline_iterator)
@@ -407,6 +408,7 @@ def add_actor_information_and_train(
                 "done": done,
                 "observation_feature": observation_features,
                 "next_observation_feature": next_observation_features,
+                "complementary_info": batch.get("complementary_info", None),
             }
 
             # Use the forward method for critic loss (includes both main critic and grasp critic)
@@ -437,9 +439,11 @@ def add_actor_information_and_train(
 
         # Sample for the last update in the UTD ratio
         batch = next(online_iterator)
+        # batch = replay_buffer.sample(batch_size)
 
         if dataset_repo_id is not None:
             batch_offline = next(offline_iterator)
+            # batch_offline = offline_replay_buffer.sample(batch_size)
             batch = concatenate_batch_transitions(
                 left_batch_transitions=batch, right_batch_transition=batch_offline
             )
@@ -775,9 +779,7 @@ def make_optimizers_and_scheduler(cfg: TrainPipelineConfig, policy: nn.Module):
         params=policy.actor.parameters_to_optimize,
         lr=cfg.policy.actor_lr,
     )
-    optimizer_critic = torch.optim.Adam(
-        params=policy.critic_ensemble.parameters_to_optimize, lr=cfg.policy.critic_lr
-    )
+    optimizer_critic = torch.optim.Adam(params=policy.critic_ensemble.parameters_to_optimize, lr=cfg.policy.critic_lr)
 
     if cfg.policy.num_discrete_actions is not None:
         optimizer_grasp_critic = torch.optim.Adam(
@@ -992,7 +994,6 @@ def initialize_offline_replay_buffer(
         device=device,
         state_keys=cfg.policy.input_features.keys(),
         action_mask=active_action_dims,
-        action_delta=cfg.env.wrapper.delta_action,
         storage_device=storage_device,
         optimize_memory=True,
         capacity=cfg.policy.offline_buffer_capacity,