- Changed the device assignment for tensors in the ReplayBuffer class from `device` to `storage_device` for consistency and improved resource management.
- Introduced counters for tracking intervention steps and total steps during training.
- Calculated and logged the intervention rate at the end of each episode.
- Reset intervention counters after each episode to ensure accurate tracking.
- 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
- Replaced list-based memory storage with pre-allocated tensor storage
- Optimized sampling process with direct tensor indexing
- Added support for DrQ image augmentation during sampling for offline dataset
- Improved dataset conversion with more robust episode handling
- Enhanced buffer initialization and state tracking
- Added comprehensive testing for buffer conversion and sampling
- 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
- Introduced Ensemble and CriticHead classes for more efficient critic network handling
- Added support for multiple camera inputs in observation encoder
- Optimized image encoding by batching image processing
- Updated configuration for ManiSkill environment with reduced image size and action scaling
- Compiled critic networks for improved performance
- Simplified normalization and ensemble handling in critic networks
Co-authored-by: michel-aractingi <michel.aractingi@gmail.com>
- 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 `lerobot/scripts/server/find_joint_limits.py` to test the min and max angles of the motion you wish the robot to explore during RL training.
- Added logic in `manipulator.py` to limit the maximum possible joint angles to allow motion within a predefined joint position range. The limits are specified in the yaml config for each robot. Checkout the so100.yaml.
Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>
- Added several wrappers around the base gym env robot class.
- Including: time limit, reward classifier, crop images, preprocess observations.
- Added an interactive script crop_roi.py where the user can interactively select the roi in the observation images and return the correct crop values that will improve the policy and reward classifier performance.
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>
Added functions for converting the replay buffer from and to LeRobotDataset. When we want to save the replay buffer, we convert it first to LeRobotDataset format and save it locally and vice-versa.
Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>
pre-commit-ci[bot]
AdilZouitine
Eugene Mironov
Michel Aractingi
s1lent4gnt
Yoel