* feat(rewards): add RewardModelConfig and PreTrainedRewardModel base classes
* refactor(rewards): migrate Classifier from policies/sac/reward_model/ to rewards/classifier/
* refactor(rewards): migrate SARM from policies/sarm/ to rewards/sarm/
* refactor(rewards): add rewards/factory.py and remove reward model code from policies/factory.py
* refactor(rewards): update imports and delete old reward model locations
* test(rewards): add reward model tests and update existing test imports
* fix(rewards): restore full Classifier and SARM implementations
* test(rewards): restore missing CUDA and mixed precision classifier processor tests
* refactor(lerobot_train.py): remove rabc specific configuration and replace it with a generic samplerweight class in lerobot_train
* refactor(lerobot_train.py): add missing sampling weight script
* linter + missing files
* add testing for sampl weighter
* revert some useless changes, improve typing
* update docs
* add automatic detection of the progress path
* remove type exp
* improve comment
* fix: move rabc.py to rewards/sarm/ and update import paths
* refactor(imports): update reward model imports to new module structure
* refactor(imports): update reward model imports to reflect new module structure
* refactor(imports): conditionally import pandas based on availability
* feat(configs): add reward_model field to TrainPipelineConfig and Hub fields to RewardModelConfig
* refactor(policies): remove reward model branches from policy factory and __init__
* refactor(rewards): expand __init__ facade and fix SARMConfig __post_init__ crash
* feat(train): route reward model training through rewards/factory instead of policies/factory
* refactor(train): streamline reward model training logic
* fix(rewards): ensure FileNotFoundError is raised for missing config_file
* refactor(train): update __get_path_fields__ to include reward_model for config loading
* refactor(classifier): remove redundant input normalization in predict_reward method
* fix(train): raise ValueError for non-trainable reward models in train function
* refactor(pretrained_rm): add model card template
* refactor(tests): reward models
* refactor(sarm): update reset method and remove unused action prediction methods
* refactor(wandb): differentiate tags for reward model and policy training in cfg_to_group function
* fix(train): raise ValueError for PEFT usage in reward model training
* refactor(rewards): enhance RewardModelConfig with device handling and delta indices properties
---------
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
* add initial modeling
* make rewind pretrained policy
* add annotation
* small fix
* add sarm
* subtasks
* fix spawn
* fix rewind discrepancies
* Add script to generate embedding for dataset (#2138)
* Add generate and validate script
* fix precommit
* Improve generate embeddings function by using dataset tools (#2206)
---------
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
* cleanup
* change order train log
* print batch size
* update sarm processor
* add reward output
* change expected features
* add image validation
* change validation
* get state input from dataset stats
* raise if no state key is found
* pass stats
* cleanup and refactor
* add episode inddex to complementary data
* add subtask init and detection
* revert lerobot_train changes
* pass dataset metadata to policy
* change loadig subtasks
* add small logging
* fix progress conversion and adding initial frame
* use large offset for initial frame (ugly)
* Remove rewind, use clip tokenizer
* add tests, implement formula 1,2 correctly and cleanup
* use task from dataset, cleanup visualizer
* simplify
* simplify and cleanup code and move compute_temporal_proportions to utils
* fix normalization in visualization
* Fix visualization and change prompt
* fix formatting
* add visualize subtask annotations
* use qwen thinking
* try different prompt
* format
* update prompt
* higher temp, long output
* different settings
* use instruct
* show full resp
* split message
* Temp: increase tolerance dataset
* Fix RA-BC (#2572)
* Add next observation loading for RA-BC progress deltas
* Compute weights based on temporal progress deltas instead of static rewards
* Add hard-masking for negative progress deltas in weight computation
* Feat/add dual head (#2582)
* Add dual dense sparse head and annotation
* Add docs
* add dual to procesor
* cleanup
* change sampling in visualize and cleanup
* remove validation
* remove compile
* Feat/test uniform (#2587)
* test uniform
* add different string for misaligned
* Fix rewind and add tests
* uncomment text implementation
* run precommit
* Add head mode for ra-bc
* fix visalization of single task
* add
* return per sample loss
* Fix RA_BC (#2602)
* update rabc implementation
* compute rabc beforehand
* fix import
* add only progress calulation
* use precomputed progress
* multi gpu processing
* import
* fix dataset meta data extraction
* add logging
* logging
* log
* progress per episode
* split differently
* move clip to gpu
* pre decode frames for an episode
* fix cuda initalization
* fix import
* multi processing
* rename
* fix import
* fix
* fix rabc
* use last known progress if oob
* use last known progress if oob
* add misalignment loss with random embeddings
* discard previous changes
* add selection of models to docs for ra_bc
* add transformers dep
* extend tolerance
* initial commit with new codebase
* add tests
* fix
* remove temporal sampler
* drop last frame for sampler
* use original ref
* some fixes
* fix visualization
* remove smoothing and fix order subtasks
* add stride rabc computation
* add push to hub
* add explanation
* add kappa expllaination
* better rabc logging
* feedback pr
* remove dataset tolerance
* revert dataset tool
* revert dataset changes
* add credit
* run precommit
* change path for generate ra_bc
* fix type
* include sarm in all in pyproject
* fix precommit
* lazy import matplotlib
* lazy import qwen
* remove rich console
* skip if transformers is not installed?
* run only when we have faker
* place transformer lazy loading
* Dont test if low transformer version
* fix
* increase transformer
* increase as 4.57.0 is yanked
* remove pi from all
* go back
---------
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
Co-authored-by: s1lent4gnt <kmeftah.khalil@gmail.com>
Khalil Meftah