Update readme

Author: BatyLeo

README.md

@@ -6,87 +6,22 @@
 [![Coverage](https://codecov.io/gh/JuliaDecisionFocusedLearning/DecisionFocusedLearningBenchmarks.jl/branch/main/graph/badge.svg)](https://app.codecov.io/gh/JuliaDecisionFocusedLearning/DecisionFocusedLearningBenchmarks.jl)
 [![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/JuliaDiff/BlueStyle)
 
-> [!WARNING]  
+> [!WARNING]
 >  This package is currently under active development. The API may change in future releases.
->  Please refer to the [documentation](https://JuliaDecisionFocusedLearning.github.io/DecisionFocusedLearningBenchmarks.jl/stable/) for the latest updates.
 
-## What is Decision-Focused Learning?
-
-Decision-Focused Learning (DFL) is a paradigm that integrates machine learning prediction with combinatorial optimization to make better decisions under uncertainty.
-Unlike traditional "predict-then-optimize" approaches that optimize prediction accuracy independently of downstream decision quality, DFL directly optimizes end-to-end decision performance.
-
-A typical DFL algorithm involves training a parametrized policy that combines a statistical predictor with an optimization component:
-
-```math
-x \;\longrightarrow\; \boxed{\,\text{Statistical model } \varphi_w\,} 
-\;\xrightarrow{\theta}\; \boxed{\,\text{CO algorithm } f\,} 
-\;\longrightarrow\; y
-```
-
-Where:
-- **Statistical model** $\varphi_w$: machine learning predictor (e.g., neural network)
-- **CO algorithm** $f$: combinatorial optimization solver
-- **Instance** $x$: input data (e.g., features, context)
-- **Parameters** $\theta$: predicted parameters for the optimization problem solved by `f`
-- **Solution** $y$: output decision/solution
-
-## Package Overview
-
-**DecisionFocusedLearningBenchmarks.jl** provides a comprehensive collection of benchmark problems for evaluating decision-focused learning algorithms. The package offers:
-
-- **Standardized benchmark problems** spanning diverse application domains
-- **Common interfaces** for creating datasets, statistical models, and optimization algorithms
-- **Ready-to-use DFL policies** compatible with [InferOpt.jl](https://github.com/JuliaDecisionFocusedLearning/InferOpt.jl) and the whole [JuliaDecisionFocusedLearning](https://github.com/JuliaDecisionFocusedLearning) ecosystem
-- **Evaluation tools** for comparing algorithm performance
-
-## Benchmark Categories
-
-The package organizes benchmarks into three main categories based on their problem structure:
-
-### Static Benchmarks (`AbstractBenchmark`)
-Single-stage optimization problems with no randomness involved:
-- [`ArgmaxBenchmark`](@ref): argmax toy problem
-- [`Argmax2DBenchmark`](@ref): 2D argmax toy problem
-- [`RankingBenchmark`](@ref): ranking problem
-- [`SubsetSelectionBenchmark`](@ref): select optimal subset of items
-- [`PortfolioOptimizationBenchmark`](@ref): portfolio optimization problem
-- [`FixedSizeShortestPathBenchmark`](@ref): find shortest path on grid graphs with fixed size
-- [`WarcraftBenchmark`](@ref): shortest path on image maps
-
-### Stochastic Benchmarks (`AbstractStochasticBenchmark`)  
-Single-stage optimization problems under uncertainty:
-- [`StochasticVehicleSchedulingBenchmark`](@ref): stochastic vehicle scheduling under delay uncertainty
-
-### Dynamic Benchmarks (`AbstractDynamicBenchmark`)
-Multi-stage sequential decision-making problems:
-- [`DynamicVehicleSchedulingBenchmark`](@ref): multi-stage vehicle scheduling under customer uncertainty
-- [`DynamicAssortmentBenchmark`](@ref): sequential product assortment selection with endogenous uncertainty
-
-## Getting Started
-
-In a few lines of code, you can create benchmark instances, generate datasets, initialize learning components, and evaluate performance, using the same syntax across all benchmarks:
+**DecisionFocusedLearningBenchmarks.jl** provides a collection of benchmark problems for evaluating [Decision-Focused Learning](https://JuliaDecisionFocusedLearning.github.io/DecisionFocusedLearningBenchmarks.jl/stable/) algorithms, spanning static, stochastic, and dynamic settings.
+Each benchmark provides a dataset generator, a statistical model architecture, and a combinatorial oracle, ready to plug into any DFL training algorithm:
 
 ```julia
 using DecisionFocusedLearningBenchmarks
 
-# Create a benchmark instance for the argmax problem
-benchmark = ArgmaxBenchmark()
-
-# Generate training data
-dataset = generate_dataset(benchmark, 100)
-
-# Initialize policy components
-model = generate_statistical_model(benchmark)
-maximizer = generate_maximizer(benchmark)
-
-# Training algorithm you want to use
-# ... your training code here ...
-
-# Evaluate performance
-gap = compute_gap(benchmark, dataset, model, maximizer)
+bench = ArgmaxBenchmark()
+dataset = generate_dataset(bench, 100)
+model = generate_statistical_model(bench)
+maximizer = generate_maximizer(bench)
 ```
 
-The only component you need to customize is the training algorithm itself.
+For the full list of benchmarks, the common interface, and detailed usage examples, refer to the [documentation](https://JuliaDecisionFocusedLearning.github.io/DecisionFocusedLearningBenchmarks.jl/stable/).
 
 ## Related Packages