RL4CO: RL for Combinatorial Optimization

• RL4CO is an open-source library that standardizes reinforcement learning benchmarks for combinatorial optimization, integrating 27 diverse CO environments and 23 baseline algorithms.
• It employs a modular PyTorch-based architecture with Hydra configuration to streamline experiment setup, reduce engineering overhead, and ensure reproducibility.
• The library facilitates direct comparisons of RL methodologies on NP-hard problems in routing, scheduling, and electronic design automation through transparent benchmarking.

RL4CO is an open-source unified benchmark and research library focused on advancing reinforcement learning (RL) methodologies for combinatorial optimization (CO) problems. By consolidating 27 CO environments and 23 state-of-the-art baselines within a modular, reproducible framework, RL4CO addresses inconsistencies in prior benchmarks, reduces engineering overhead, and enables direct comparative evaluation of neural approaches to classic NP-hard problems in fields such as routing, scheduling, facility location, and electronic design automation (Berto et al., 2023). The library is implemented in PyTorch, leverages best practices in scalable RL pipelines, and provides extensive configuration management and documentation to facilitate experimentation across diverse CO tasks.

1. Motivation and Design Rationale

RL4CO is constructed in response to multiple persistent challenges in RL-based CO research. Traditional CO solutions—such as branch-and-bound solvers, mixed-integer programming, and expert-designed heuristics—are robust but often scale poorly and require intricate domain knowledge. Recent progress in deep RL has demonstrated the feasibility of learning-based algorithms on CO instances, but lack of standardized benchmarks has led to inconsistent evaluations and reproducibility issues.

RL4CO resolves these obstacles by:

• Centralizing 27 CO environments, spanning routing, scheduling, graph-based, and EDA problems.
• Implementing 23 baselines with rigorous adherence to literature specifications.
• Decoupling algorithmic prototyping from low-level engineering via modular code and configuration management.
• Streamlining reproducibility to support fair cross-algorithm comparisons and accelerating method development for new entrants.

2. Problem and Baseline Coverage

The RL4CO library provides a broad taxonomy of combinatorial optimization environments and solution methods relevant to both academic research and industrial applications.

CO Problem Environments

RL4CO’s environment suite includes:

• Routing: Traveling Salesman Problem (TSP), Capacitated VRP (CVRP), Orienteering, Pickup & Delivery, and multi-task VRP variants.
• Scheduling: Job Shop, Flexible Job Shop, Flexible Flow Shop.
• Electronic Design Automation: Decap placement problems.
• Graph-Based CO: Facility Location, Maximum Coverage.

Each environment adheres to a standardized interface, enabling consistent state representation and batch processing, which supports GPU acceleration through PyTorch.

RL Baselines

The library’s baselines comprise:

• Attention Model (AM), Pointer Networks, POMO, PolyNet, Sym-NCO: Autoregressive and non-autoregressive architectures for sequential decision-making.
• RL Algorithms: REINFORCE, A2C, PPO.
• Hybrid methods: GLOP, which combines non-autoregressive and autoregressive agents.

Table 1 summarizes the scope of problems and baseline methods.

Category	Example Problems/Methods	Count
Environments	TSP, CVRP, Job Shop, FacilityLoc	27
Baselines	AM, POMO, REINFORCE, GLOP	23

The modular inheritance from metaclasses for each baseline allows extensibility and recombination of model and training components.

3. Modular Architecture and Experimentation Framework

RL4CO is implemented with a focus on modularity and efficient configuration:

• Environment, Policy, and Algorithm Modules: Code is structured so that environments, neural policies (autoregressive, non-autoregressive, improvement-based), and RL algorithms are independently defined and can be recombined with minimal refactoring.
• Configuration Management: Hydra facilitates hierarchical YAML configuration, supporting specification of problem instances, architectural details (e.g., number of encoder layers, attention heads), and hyperparameters. This enables reproducibility and transparent sharing of experimental protocols.
• Scalability: RL4CO leverages PyTorch Lightning to abstract boilerplate code, while TorchRL and TensorDict provide for efficient batch handling and GPU acceleration in both training and evaluation. Environments are stateless and accept TensorDicts as inputs; step functions are optimized for memory efficiency and batch throughput, reducing evaluation step latency by up to 50% and lowering memory footprint.

4. Implementation of Neural RL for CO

Central to RL4CO’s technical framework is the implementation of neural RL models specifically tailored for CO:

• Attention-Based Models: Many baselines, such as the AM and its derivatives, utilize multi-head self-attention for sequential construction of solutions. Key equations—such as scaled dot-product attention, tanh clipping, and softmax transformation—are implemented following standard formulations. RL4CO supports computational accelerations, including FlashAttention when permissible by the problem’s causal structure.
• Policy Gradient Training: Policy gradient formulations, including REINFORCE and actor-critic methods, are used in both constructive (sequential decision) and improvement (local search) models. All algorithmic variants are implemented following the conventions delineated in literature baselines.
• Flexible Decoding and Sampling: Decoding strategies (greedy, sampling, top-k) are modular and can be interchanged to paper their effects on solution diversity and performance.

Minimal example scripts and code snippets allow a complete training pipeline to be launched in under 20 lines of Python, with problem instances, model hyperparameters, and runtime settings fully defined via YAML.

5. Benchmarking, Reproducibility, and Community

RL4CO enforces standardized, transparent benchmarking to strengthen result validity:

• Benchmark Studies: Comprehensive evaluations span all 23 baseline algorithms over the 27 CO environments, tracking sample efficiency, out-of-distribution generalization, and scaling up to thousands of nodes in routing tasks.
• Logging and Experiment Tracking: Integrates with experiment managers such as Hydra, enabling logging of not only evaluation metrics but trial configurations and code snapshots. This infrastructure supports community-driven reproducibility and critical evaluation of method performance.
• Open Source Community: RL4CO is distributed under the MIT license, and the AI4CO community includes over 250 researchers. Regular contributions, bug reports, and feature suggestions drive rapid development and adoption.

6. Future Directions and Extensions

Several future trajectories are apparent:

• Foundation Models for CO: There is an explicit direction towards developing generalist neural CO models (“foundation models”) capable of representing and solving families of combinatorial problems—potentially obviating narrow, problem-specific retraining.
• Beyond RL: While RL comprises the current focus, hybridization with supervised or hybrid learning techniques is anticipated.
• Advanced Sampling and Hybrid Methods: Planned enhancements include more efficient decoding strategies (e.g., advanced nucleus sampling) and end-to-end hybrid models that combine constructive and local improvement routines within a unified framework.
• Expanding Domains: RL4CO is designed for extension into areas such as multi-objective optimization, non-Euclidean topologies, and multi-agent combinatorial settings.
• Further Optimization: Ongoing hardware-dependent code improvements (such as expanded FlashAttention support and reduced inter-process overhead) are targeted to further reduce training and inference wallclock time.

7. Relationship to Adjacent Tools and Research

RL4CO differentiates itself by providing a dedicated, scalable, and highly modular toolkit for RL-based combinatorial optimization, in contrast to general RL libraries or CO benchmarks. Its design methodology shares principles with other modern RL ecosystems, such as Hydra-based configuration and PyTorch Lightning training orchestration, but uniquely supports the breadth and extensibility required for CO research. The library’s community focus and explicit benchmarking philosophy align it with recent calls for greater transparency and reproducibility in deep learning for operational research (Berto et al., 2023).

In summary, RL4CO establishes a comprehensive infrastructural and benchmarking standard for RL methodologies in combinatorial optimization, lowering the entry barrier for method development, ensuring consistent comparison across tasks and algorithms, and enabling rapid iteration in this rapidly advancing intersection of machine learning and operational research.