rl4co 0.0.1

RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark

RL4CO

[Notion Page] [Sofware Practices]

Description

Code repository for RL4CO. Based on TorchRL and the Lightning-Hydra-Template best practices.

Getting started

Clone project and install project locally with pip:

git clone https://github.com/kaist-silab/rl4co && cd rl4co
pip install -e .

[Optional] Automatically install PyTorch with correct CUDA version

These commands will automatically install PyTorch with the right GPU version for your system:

pip install light-the-torch
python3 -m light_the_torch install -r  --upgrade torch

Note: conda is also a good candidate for hassle-free installation of PyTorch: check out the PyTorch website for more details.

To get started, we recommend checking out our quickstart notebook or the minimalistic example below.

Usage

Train model with default configuration (AM on TSP environment):

python run.py  

Change experiment

Train model with chosen experiment configuration from configs/experiment/ (e.g. tsp/am, and environment with 42 cities)

python run.py experiment=tsp/am env.num_loc=42 

Disable logging

python run.py experiment=test/am logger=none '~callbacks.learning_rate_monitor'

Note that ~ is used to disable a callback that would need a logger.

Create a sweep over hyperparameters (-m for multirun)

python run.py -m experiment=tsp/am  train.optimizer.lr=1e-3,1e-4,1e-5

Minimalistic Example

Here is a minimalistic example training the Attention Model with greedy rollout baseline on TSP in less than 50 lines of code:

from omegaconf import DictConfig
import lightning as L
from rl4co.envs import TSPEnv
from rl4co.models.zoo.am import AttentionModel
from rl4co.tasks.rl4co import RL4COLitModule

config = DictConfig(
    {"data": {
            "train_size": 100000,
            "val_size": 10000, 
            "batch_size": 512,
        },
    "optimizer": {"lr": 1e-4}}
)

# Environment, Model, and Lightning Module
env = TSPEnv(num_loc=20)
model = AttentionModel(env)
lit_module = RL4COLitModule(config, env, model)

# Trainer
trainer = L.Trainer(
    max_epochs=3, # only few epochs
    accelerator="gpu", # use GPU if available, else you can use others as "cpu"
    logger=None, # can replace with WandbLogger, TensorBoardLogger, etc.
    precision="16-mixed", # Lightning will handle faster training with mixed precision
    gradient_clip_val=1.0, # clip gradients to avoid exploding gradients
    reload_dataloaders_every_n_epochs=1, # necessary for sampling new data
)

# Fit the model
trainer.fit(lit_module)

Testing

Run tests with pytest from the root directory:

pytest tests

We will enable automated tests when we make the repo public.