Rl4co

Minor release with several QOL improvements

What's Changed
* Implement floyd on tmat_class atsp generation by abcdhhhh in https://github.com/ai4co/rl4co/pull/226
* Bump actions/download-artifact from 3 to 4.1.7 in /.github/workflows by dependabot in https://github.com/ai4co/rl4co/pull/210
* [DOCS] Modify url in example/1-quickstart.ipynb by falconlee236 in https://github.com/ai4co/rl4co/pull/215
* TSPLIB and CVRPLIB testing notebooks
* Automatically enable sampling if `num_samples`>1
* Update `Spec` names according to `torchrl>=0.6.0`
* Now actions are automatically returned by default, no need to specify `return_actions=True`
* Fix edge cases for `SDPA` 228
* PCTSP distribution problem fix

New Contributors
* falconlee236 made their first contribution in https://github.com/ai4co/rl4co/pull/215
* abcdhhhh made their first contribution in https://github.com/ai4co/rl4co/pull/226

**Full Changelog**: https://github.com/ai4co/rl4co/compare/v0.5.0...v0.5.1

Major release: v0.5.0 is finally here! :rocket:

We are proud to finally release our latest version, 0.5.0, after much work done for NeurIPS!
(Will [our paper](https://arxiv.org/abs/2306.17100) finally get accepted? :crossed_fingers:)


Changelog

:sparkles: Features

* New documentation released at rl4.co!
* Add SOTA FJSP environment LTluttmann
* Add Improvement methods and respective environments MDP yining043
* N2S
* DACT
* NeuOpt
* Ade HetGNN model for the JFSP LTluttmann
* Add L2D model LTluttmann
* Add Multi-task VRP (MTVRP) environment
* Add temperature in NARGNN policies Furffico
* Add multiple batch sizes for different dataset
* Local search support, DeepACO + Local search hyeok9855
* Add MTPOMO, MVMoE model RoyalSkye FeiLiu36
* Supporting the meta learning trainer jieyibi
* Supporting the improvement training yining043
* Add graph problems: MCP and FLP bokveizen
* New PPO versions:
* Stepwise LTluttmann
* Improvement yining043
* PolyNet support ahottung
* Different distributions support + MDPOMO jieyibi
* Add initial support for solvers API from RL4CO (MTVRP): PyVRP, OR-Tools, LKH3 N-Wouda leonlan
* Faster data logprobs collection: now we don't need to collect logprobs for unused trajectories, but we gather only logprobs for selected nodes by default, which decreases memory consumption
* Add Codecov to track the tests coverage

:gear: Refactoring

* [Environment] Supporting generator_params arguments for environments, more modularized and flexible.
* Modularization of the Attention Model decoder’s QKV calculation for more flexibility LTluttmann
* Refactor the MatNet encoder with the cross attention only needs to be calculated once LTluttmann

:memo: Documentation

* New documentation based on MkDocs
* Fast search
* Beautiful (we hope you'll like it!) new homepage
* New API reference, about section
* Ad-free website
* Light/Dark mode
* New about sections (licensing, citation)
* ...and more!
* New tutorial on data distributions
* Miscellaneous: fix Colab links wouterkool



:bug: Bug Fixes

* Fix the DeepACO’s log_heuristic calculation bug to raise the performance. Furffico henry-yeh
* Solve memory leakage during the autoregressive decoding LTluttmann
* Python versioning: remove Python 3.8, compatibility with Python 3.12, and poetry support ShuN6211
* Compatibility with tensordict>=0.5.0
* Memory leak in OP and PCTSP
* Fix A2C bug: optimize all parameters in module instead of only "policy" by default
* Fix double logging parameters, better logging in Wandb

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](https://github.com/ai4co/rl4co/pull/165)). 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](https://github.com/ai4co/rl4co/pull/169)): 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](https://github.com/ai4co/assets)!

🐛 Bug Fixes
- MatNet and FFSP bugfix LTluttmann
- Best solution gathering from POMO ahottung
- Tests now passing on MPS; compatibility with TorchRL https://github.com/pytorch/rl/pull/2125
- Miscellaneuous LTluttmann , bokveizen , tycbony

New Routing Envs and more :rocket:

Changelog

:sparkles: Features

* Add CVRPTW Environment ngastzepeda
* Add Solomon instance / solution loader via `vrplib`
* Add basic Skill-VRP (SVRP) ngastzepeda

:page_with_curl: Documentation

* [Minor] improve decoding strategies documentation

:bug: Bug Fixes

* Avoid `deepcopy` bug by not saving intermediate steps of decoding strategy 123
* Allow passing `select_start_nodes_fn` and other kwargs in decoding strategies

New Decoding Types and more :rocket:

Changelog

Features
- Beam Search 109 110 LTluttmann
- Decoding type class 109 110 LTluttmann

Documentation

- Add (simple , API work in progress!) tutorial notebooks for TSPLib and CVRPLib 84
- Add decoding strategies notebook LTluttmann + small fix Haimrich

Optimization

* `torch.no_grad` to `torch.inference_mode`
* Faster testing

Bug Fixes

* Batch size initialization ngastzepeda
* Bump up naming to align with `0.4.0` release of TorchRL
* MatNet bug fix 108

QoL and BugFixes 🚀

Changelog

- Better multi start decoding 102
- Add modular `select_start_nodes` function for POMO
- Improve efficiency of multistart function
- Add testing and selection function for more envs
- Fix OP selecting too far away nodes in POMO
- Automatic multistart, no need to manually choose beforehand when running POMO
- Fix CVRP capacity bug ngastzepeda 105
- Add critic init embedding support
- Fix data generation and add better docs 106
- Better dataset handling: add dataset choice; use low CPU usage dataset by default
- Better solution plotting and better quickstart notebook 103
- Library winter cleanup
- Miscellaneous minor fixes here and there