Strengths
- Consistent interface across algorithms simplifies usage and experimentation.
- High code coverage with automated unit tests ensures robustness.
- Benchmarking against reference implementations verifies algorithm performance.
- Extensive documentation and examples facilitate training, saving, and custom environment integration.
- Tensorboard support enables monitoring of training progress.
Limitations
- Requires careful handling of object shapes as broadcast errors may fail silently.
- Tuple observation spaces are not supported; only Dict spaces are supported for complex observations.
- Migration to Gymnasium backend in version 2.0+ may require updating existing Gym-based code.