Major release: v0.4.0 is here! 🚀

This release adds several new features and major refactorings in both modeling and environment sides!

Changelog

✨ Features

• DeepACO + ACO @Furffico @henry-yeh
• Non-autoregressive (NAR) models and NARGNN @Furffico @henry-yeh
• Add modular environment data generator with support to new distributions @cbhua
• New decoding techniques based on the decoding strategy class @LTluttmann
  • Top-p (nucleus sampling)
  • Top-k
• Select start nodes functions @LTluttmann

⚙️ Refactoring

• Major modeling refactoring (summarized here). Now we categorize NCO approaches (which are not necessarily trained with RL!) into the following: 1) constructive (AR and NAR), 2) improvement, 3) transductive. This translates into code, which is now fully customizable. For instance, in constructive methods, now encoders / decoders can be fully replaced or removed in an abstract way!
• Major environment refactoring (summarized here): we further modularize the environments into components (logic under env, data generation under generator, and so on), with several components moved inside the RL4COEnvBase. Importantly, we introduce data generators that can be customized!
• Use Abstract classes if class should not be @ngastzepeda

📝 Documentation

• Hydra documentation and tutorial @LTluttmann
• New modularized examples under examples/
• Updated RL4CO structure in ReadTheDocs
• Move to MIT license with AI4CO for inclusiveness
• New RL4CO / AI4CO swag. You may also find them here!

🐛 Bug Fixes

• MatNet and FFSP bugfix @LTluttmann
• Best solution gathering from POMO @ahottung
• Tests now passing on MPS; compatibility with TorchRL pytorch/rl#2125
• Miscellaneuous @LTluttmann , @bokveizen , @tycbony