[RLlib contrib] add contributing.md to RLlib contrib (ray-project#35346)

Signed-off-by: Avnish <avnishnarayan@gmail.com>
ShuN6211 · May 18, 2023 · db97dda · db97dda
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+# Contributing Guidelines
+
+Any issues that are filed in `rllib_contrib` will be solved best-effort by the community and there is no expectation of maintenance by the RLlib team. 
+
+**The api surface between algorithms in rllib_contrib and current versions of ray / rllib is not guaranteed. This means that any apis that are used in rllib_contrib could potentially become modified/removed in newer version of ray/rllib. You should check the version of ray that an algorithm is using before making any modifications, and refer to that documentation / release on github.** 
+
+We will generally accept contributions to this repo that meet any of the following criteria:
+
+1. Updating dependencies.
+2. Submitting community contributed algorithms that have been tested and are ready for use.
+3. Enabling algorithms to be run in different environments (ex. adding support for a new type of gym environment).
+4. Updating algorithms for use with the newer RLlib APIs.
+5. General bug fixes.
+
+We will not accept contributions that generally add a significant maintenance burden. In this case users should instead make their own repo with their contribution, **using the same guidelines as this repo**, and the RLlib team can help to market/promote it in the ray docs.
+
+## Contributing new algorithms
+
+If users would like to contribute a new algorithm tor rllib_contrib, they should follow these steps:
+1. Create a new directory with the same structure as the other algorithms.
+2. Add a `README.md` file that describes the algorithm and its usecases.
+3. Create unit tests/shorter learning tests and long learning tests for the algorithm.
+4. Submit a PR, add the tag `rllib_contrib`, and then a RLlib maintainer will review it and help you set up your testing to integrate with the CI of this repo.
+
+Regarding unit tests and long running tests:
+
+- Unit tests are any tests that tests a sub component of an algorithm. For example tests that check the value of a loss function given some inputs.
+- Short learning tests should run an algorithm on an easy to learn environment for a short amount of time (e.g. ~3 minutes) and check that the algorithm is achieving some learning threshold (e.g. reward mean or loss).
+- Long learning tests should run an algorithm on a hard to learn environment (e.g.) for a long amount of time (e.g. ~1 hour) and check that the algorithm is achieving some learning threshold (e.g. reward mean or loss).