add contrib guide wip

CONTRIBUTING.md

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+# CORL Contribution Guidelines
+
+We welcome:
+
+- Bug reports
+- Pull requests for bug fixes
+- Logs and documentation improvements
+- New algorithms and datasets
+- Better hyperparameters (but with proofs)
+
+## Contributing to the codebase
+
+Contributing code is done through standard github methods:
+
+```commandline
+git clone [email protected]:tinkoff-ai/CORL.git
+cd CORL
+pip install -r requirements/requirements_dev.txt
+```
+
+1. Fork this repo
+2. Make a change and commit your code
+3. Submit a pull request. It will be reviewed by maintainers and they'll give feedback or make requests as applicable
+
+### Code style
+
+The CI will run several checks on the new code pushed to the CORL repository. 
+These checks can also be run locally without waiting for the CI by following the steps below:
+1. [install `pre-commit`](https://pre-commit.com/#install),
+2. install the Git hooks by running `pre-commit install`.
+
+Once those two steps are done, the Git hooks will be run automatically at every new commit. The Git hooks can also be run manually with `pre-commit run --all-files`, and if needed they can be skipped (not recommended) with `git commit --no-verify`. **Note:** you may have to run `pre-commit run --all-files` manually a couple of times to make it pass when you commit, as each formatting tool will first format the code and fail the first time but should pass the second time.
+
+We use [Ruff](https://github.com/astral-sh/ruff) as our main linter. If you want to see possible problems before pre-commit, you can run `ruff check --diff .` to see exact linter suggestions and future fixes.
+
+## Adding new algorithms
+
+All new algorithms should go to the `algorithms/contrib`. 
+We as a team try to keep the core as reliable and reproducible as possible, 
+but we may not have the resources to support all future algorithms. 
+Therefore, this separation is necessary, as we cannot guarantee that all 
+algorithms from `algorithms/contrib` exactly reproduce the results of their original publications.
+
+Make sure your new code is properly documented and all references to the original implementations and papers are present (for example as in [Decision Transformer](algorithms/offline/dt.py)). 
+Please, *explain all the tricks and possible differences from the original implementation in as much detail as possible*. 
+Keep in mind that this code may be used by other researchers. Make their lives easier!
+
+### Considerations
+While we welcome any algorithms, it is better to open an issue with the proposal before 
+so we can discuss the details. Unfortunately, not all algorithms are equally 
+easy to understand and reproduce. We may be able to give a couple of advices to you,
+or on the contrary warn you that this particular algorithm will require too much 
+computational resources to fully reproduce the results and it is better to do something else.
+
+### Running benchmarks
+
+Although you will have to do a hyperparameter search while reproducing the algorithm, 
+in the end we expect to see final configs in `configs/contrib/<algo_name>/<dataset_name>.yaml` with the best hyperparameters for all calculated 
+datasets. The configs should be in yaml format, containing all parameters sorted 
+in alphabetical order (see existing configs for an inspiration).
+
+Use this conventions to name your runs in the configs:
+1. `name: <algo_name>`
+2. `group: <algo_name>-<dataset_name>-multiseed-v0`. Increment version if needed
+3. use our [\_\_post_init\_\_](https://github.com/tinkoff-ai/CORL/blob/962688b405f579a1ce6ec1b57e6369aaf76f9e69/algorithms/offline/awac.py#L48) implementation in your config dataclass
+
+Since we are releasing wandb logs for all algorithms, you will need to submit multiseed (4 seeds) 
+training runs the `CORL` project in the wandb [corl-team](https://wandb.ai/corl-team) organization. We'll invite you there when the time will come.
+
+We usually use wandb sweeps for this. You can use this example config (it will work with pyrallis as it expects `config_path` cli argument):
+```yaml
+# sweep_config.yaml
+entity: corl-team
+project: CORL
+program: algorithms/contrib/<algo_name>.py
+method: grid
+parameters:
+  config_path:
+    values: [
+        "configs/contrib/<algo_name>/<dataset_name_1>.yaml",
+        "configs/contrib/<algo_name>/<dataset_name_2>.yaml",
+        "configs/contrib/<algo_name>/<dataset_name_3>.yaml",
+    ]
+  train_seed:
+    values: [0, 1, 2, 3]
+```
+
+Then proceed as usual. Create wandb sweep with `wandb sweep sweep_config.yaml`, then run agents with `wandb agent <agent_id>`.
+
+### Checklist
+
+- [ ] Issue about new algorithm is open
+- [ ] Single-file implementation is added to the `algorithms/contrib`
+- [ ] PR has passed all the tests
+- [ ] Evidence that implementation reproduces original results is provided
+- [ ] Configs with best hyperparameters for all datasets are added to the `configs/contrib`
+- [ ] Logs for best hyperparameters are submitted to the our wandb organization