Games & virtual worlds · PROJECT NOTES
RLCard
A card-game environment toolkit exposing observations, legal actions and trajectories for reinforcement learning and search under imperfect information.
What can you do with it?
Train or compare card-game policies with explicit legal-action handling and reproducible seeded episodes.
What should you know before using it?
Game-specific rules and action abstractions affect comparability. The default package contains environments; learning algorithms and graphical demos have separate dependencies/projects. PyPI lists 1.2.0 while the latest GitHub release is older. Repository code is MIT; no model or game interaction was run.
What is verified here?
This entry was reviewed against original project material. We have not independently run the software or reproduced its results. AI-development claims are author-reported; check upstream for current versions, setup requirements, and licenses.
- Resource type
- Environment framework
- Provenance
- Development provenance not established
- Reviewed
- Platforms
- Consult upstream documentation