- 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.
- 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.
- 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.
- 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
- 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.
- 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.
- 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.
- 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.
- Increased frame rate in `maniskill_example.yaml` from 20 to 400 for enhanced simulation speed.
- Updated `sac_maniskill.yaml` to set `dataset_repo_id` to null and adjusted `grad_clip_norm` from 10.0 to 40.0.
- Changed `storage_device` from "cpu" to "cuda" for better resource utilization.
- Modified `save_freq` from 2000000 to 1000000 to optimize saving intervals.
- Enhanced input normalization parameters for `observation.state` and `observation.image` in SAC policy.
- Adjusted `num_critics` from 10 to 2 and `policy_parameters_push_frequency` from 1 to 4 for improved training dynamics.
- Updated `learner_server.py` to utilize `offline_buffer_capacity` for replay buffer initialization.
- Changed action multiplier in `maniskill_manipulator.py` from 1 to 0.03 for finer control over actions.
- Updated SACPolicy to create critic heads using a list comprehension for better readability.
- Simplified the saving and loading of models using `save_model` and `load_model` functions from the safetensors library.
- Introduced `initialize_offline_replay_buffer` function in the learner server to streamline offline dataset handling and replay buffer initialization.
- Enhanced logging for dataset loading processes to improve traceability during training.
- Introduced `grad_clip_norm` parameter in SAC configuration for gradient clipping
- Updated SACPolicy to store temperature as an instance variable for consistent usage
- Modified loss calculations in SACPolicy to utilize the instance temperature
- Enhanced MLP and CriticHead to support a customizable final activation function
- Implemented gradient clipping in the learner server during training steps for both actor and critic
- Added tracking for gradient norms in training information
- Introduce `storage_device` parameter in SAC configuration and training settings
- Update learner server to use configurable storage device for replay buffer
- Reduce online buffer capacity in ManiSkill configuration
- Modify replay buffer initialization to support custom storage device
- Introduce `optimize_memory` parameter to reduce memory usage in replay buffer
- Implement simplified memory optimization by not storing duplicate next_states
- Update learner server and buffer initialization to use memory optimization by default
- Reduced image size in ManiSkill environment configuration from 128 to 64
- Added support for truncation in replay buffer and actor server
- Updated SAC policy configuration to use a specific dataset and modify vision encoder settings
- Improved dataset conversion process with progress tracking and task naming
- Added flexibility for joint action space masking in learner server
- 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>
- Modify logger to support multiple custom step keys
- Update logging method to handle custom step keys more flexibly
- Enhance logging of optimization step and frequency
Co-authored-by: michel-aractingi <michel.aractingi@gmail.com>
- Added Nan detection mechanisms in the actor, learner and gym_manipulator for the case where we encounter nans in the loop.
- changed the non-blocking in the `.to(device)` functions to only work for the case of cuda because they were causing nans when running the policy on mps
- Added some joint clipping and limits in the env, robot and policy configs. TODO clean this part and make the limits in one config file only.
Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>
- added `torch.compile` to the actor and learner servers.
- organized imports in `train_sac.py`
- optimized the parameters push by not sending the frozen pre-trained encoder.
Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>
Michel Aractingi