- Added async_prefetch parameter to SACConfig for improved buffer management.
- Implemented get_iterator method in ReplayBuffer to support asynchronous prefetching of batches.
- Updated learner_server to utilize the new iterator for online and offline sampling, enhancing training efficiency.
- Cached encoder output in select_action method to reduce redundant computations.
- Updated action selection and grasp critic calls to utilize cached encoder features when available.
- Updated SACPolicy to conditionally compute grasp critic losses based on the presence of discrete actions.
- Refactored the forward method to handle grasp critic model selection and loss computation more clearly.
- Adjusted learner server to utilize optimized parameters for grasp critic during training.
- Improved action handling in the ManiskillMockGripperWrapper to accommodate both tuple and single action inputs.
- Cleaned up code formatting for better readability, including consistent spacing and removal of unnecessary blank lines.
- Consolidated continuous action dimension calculation to enhance clarity and maintainability.
- Simplified loss return statements in the forward method to improve code structure.
- Ensured grasp critic parameters are included conditionally based on configuration settings.
- Introduced mock_gripper parameter in ManiskillEnvConfig to enable gripper simulation.
- Added ManiskillMockGripperWrapper to adjust action space for environments with discrete actions.
- Updated SACPolicy to compute continuous action dimensions correctly, ensuring compatibility with the new gripper setup.
- Refactored action handling in the training loop to accommodate the changes in action dimensions.
- 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.
- Removed GraspCriticNetworkConfig class and integrated its parameters into SACConfig.
- Added num_discrete_actions parameter to SACConfig for better action handling.
- Updated SACPolicy to conditionally create grasp critic networks based on num_discrete_actions.
- Enhanced grasp critic forward pass to handle discrete actions and compute losses accordingly.
- Integrated the grasp critic gradient update to the training loop in learner_server
- Added Adam optimizer and configured grasp critic learning rate in configuration_sac
- Added target critics networks update after the critics gradient step
Minor modifications in gym_manipulator to quantize the gripper actions
clamped the observations after F.resize in ConvertToLeRobotObservation wrapper due to a bug in F.resize, images were returned exceeding the maximum value of 1.0
- Enhanced type annotations for variables in the `SACPolicy` class to improve code clarity.
- Updated method calls to use keyword arguments for better readability.
- Streamlined the extraction of batch components, ensuring consistent typing across the class methods.
- Updated the `forward` method in `SACPolicy` to handle loss computation for actor, critic, and temperature models.
- Replaced direct calls to `compute_loss_*` methods with a unified `forward` method in `learner_server`.
- Enhanced batch processing by consolidating input parameters into a single dictionary for better readability and maintainability.
- Removed redundant code and improved documentation for clarity.
- Consolidated logging initialization and enhanced logging for actor processes.
- Streamlined the handling of gRPC connections and process management.
- Improved readability by organizing core algorithm functions and communication functions.
- Added detailed comments and documentation for clarity.
- Ensured proper queue management and shutdown handling for actor processes.
- Removed unused imports and streamlined the code structure.
- Consolidated logging initialization and enhanced logging for training processes.
- Improved handling of training state loading and resume logic.
- Refactored transition and interaction message processing for better readability and maintainability.
- Added detailed comments and documentation for clarity.
- Simplified the `image_features` property to directly iterate over `input_features`.
- Removed unused imports and unnecessary code related to main execution, enhancing clarity and maintainability.
- Implemented process-specific logging for actor and learner servers to improve traceability.
- Created a dedicated logs directory and ensured it exists before logging.
- Initialized logging with explicit log files for each process, including actor transitions, interactions, and policy.
- Updated the actor CLI to validate configuration and set up logging accordingly.
Moved HilSerl env config to configs/env/configs.py
fixes in actor_server and modeling_sac and configuration_sac
added the possibility of ignoring missing keys in env_cfg in get_features_from_env_config function
- Introduced `WrapperConfig` dataclass for environment wrapper configurations.
- Updated `ManiskillEnvConfig` to include a `wrapper` field for enhanced environment management.
- Modified `SACConfig` to return `None` for `observation_delta_indices` and `action_delta_indices` properties.
- Refactored `make_robot_env` function to improve readability and maintainability.
Added support for hil_serl classifier to be trained with train.py
run classifier training by python lerobot/scripts/train.py --policy.type=hilserl_classifier
fixes in find_joint_limits, control_robot, end_effector_control_utils
- Reduced frame rate in `ManiskillEnvConfig` from 400 to 200.
- Enhanced `SACConfig` with new dataclasses for actor, learner, and network configurations.
- Improved input and output feature management in `SACConfig`.
- Refactored `actor_server` and `learner_server` to access configuration properties directly.
- Updated training pipeline to validate configurations and handle dataset repo IDs more robustly.
Add ManiSkill environment configuration and wrappers
- Introduced `VideoRecordConfig` for video recording settings.
- Added `ManiskillEnvConfig` to encapsulate environment-specific configurations.
- Implemented various wrappers for the ManiSkill environment, including observation and action scaling.
- Enhanced the `make_maniskill` function to create a wrapped ManiSkill environment with video recording and observation processing.
- Updated the `actor_server` and `learner_server` to utilize the new configuration structure.
- Refactored the training pipeline to accommodate the new environment and policy configurations.
AdilZouitine
pre-commit-ci[bot]
Michel Aractingi
Michel Aractingi
s1lent4gnt