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@mayalenE mayalenE released this 07 Dec 18:18
· 77 commits to main since this release
v0.2
8ff29c5

  • New modules:

    • BaseGoalAchievementLoss: base module for goal-achievement losses
    • BaseOptimizer: base module for optimizers
    • OpenESOptimizer: sgd optimizer where grad is not the exact value but estimated with ES (Salimans et al., 2017)
    • EAOptimizer: simple ea optimizer
    • BaseIM: base module for computing IM values and grad
    • LearningProgressIM: learning-progress IM variant
    • IMFlowGoalGenerator: imgep module for sampling new goals by flowing previous goals along IM gradients
    • BasePerturbationGenerator: base module for generating perturbations during evaluation (dependent of experiment's system_output_library)
    • NoisePerturbationGenerator: submodule for generating noise perturbation params, given experiment's grn trajectories
    • WallPerturbationGenerator: submodule for generating wall perturbation params, given experiment's grn trajectories
    • PiecewiseWallCollisionIntervention: grn intervention module to apply walls

  • New util functions in misc.py :

    • flat_top_gaussian
    • sigmoid
    • calc_segment_intersection
    • wall_sticky_collision
    • wall_elastic_collision
    • calc_perpendicular_wall_distance
    • calc_radial_wall_distance
    • wall_force_field_collision

  • Bug Fixes:

    • Clamp intervention parameters in optimizers
    • NearestNeighborInterventionSelector allow k>1

  • API changes:

    • BaseGCInterventionOptimizer: imgep module now allows several optimizer variants (where optimizer is a BaseOptimizer object)
    • grn interventions __call__() function now takes y, y_, w, w_, c, c_, t_ as arguments
    • GRNRollout output pytree keys are now ys, ws, cs, ts instead of y, w, c, times
    • imgep_experiment_pipeline:
      • goal_generator now takes the target_goal_embedding_library as input
      • saving modules is optional
    • imgep_evaluation_pipeline:
      • removed the goal_embedding_encoder module from the pipeline
      • perturbation_generator now takes the experiment_system_output_library as input
      • saving modules is optional

  • Tests:

    • test_grn_modules
    • test_imgep_modules

  • Examples:

    • new example python scripts to run imgep experiment and evaluation pipelines with different possible configurations
    • new jupyter notebooks to analyze the results
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