DecisionFocusedLearningAlgorithms

Warning

This package is currently under active development. The API may change in future releases. Please refer to the documentation for the latest updates.

Overview

This package provides a unified interface for training decision-focused learning algorithms that combine machine learning with combinatorial optimization. It implements several state-of-the-art algorithms for learning to predict parameters of optimization problems.

Key Features

• Unified Interface: Consistent API across all algorithms via train_policy!
• Policy-Centric Design: DFLPolicy encapsulates statistical models and optimizers
• Flexible Metrics: Track custom metrics during training
• Benchmark Integration: Seamless integration with DecisionFocusedLearningBenchmarks.jl

Quick Start

using DecisionFocusedLearningAlgorithms
using DecisionFocusedLearningBenchmarks

# Create a policy
benchmark = ArgmaxBenchmark()
model = generate_statistical_model(benchmark)
maximizer = generate_maximizer(benchmark)
policy = DFLPolicy(model, maximizer)

# Train with FYL algorithm
algorithm = PerturbedFenchelYoungLossImitation()
result = train_policy(algorithm, benchmark; epochs=50)

See the documentation for more details.

Available Algorithms

• Perturbed Fenchel-Young Loss Imitation: Differentiable imitation learning with perturbed optimization
• AnticipativeImitation: Imitation of anticipative solutions for dynamic problems
• DAgger: DAgger algorithm for dynamic problems