- Bump catboost from 1.2.8 to 1.2.10
- Update google-api-python-client from 2.190.0 to 2.191.0
- Upgrade langchain from 1.2.0 to 1.2.10
- Update langchain-core from 1.2.16 to 1.2.17
- Upgrade langchain-huggingface from 1.2.0 to 1.2.1
- Bump marimo from 0.19.11 to 0.20.2
- Update matplotlib from 3.10.1 to 3.10.8
- Upgrade numpy from 2.2.5 to 2.4.2
- Update opencv-python from 4.11.0.86 to 4.13.0.92
- Bump pandas from 2.2.3 to 3.0.1
- Update plotly from 6.3.0 to 6.6.0
- Upgrade polars from 1.37.0 to 1.38.1
- Bump rasterio from 1.4.4 to 1.5.0
- Update scikit-learn from 1.6.1 to 1.8.0
- Upgrade scipy from 1.15.2 to 1.17.1
- Bump shap from 0.49.1 to 0.50.0
- Adjust isort section order for better readability
- Added Q-learning model checkpoint file (q_learning.pkl) to the checkpoints directory.
- Included training curves for Q-learning in the plots directory (Q-Learning_training_curves.png).
- Added a new checkpoint file for the SARSA algorithm at checkpoints/sarsa.pkl.
- Included training curves for the SARSA algorithm in plots/SARSA_training_curves.png.
- Introduced functions to create a Tennis environment and run matches between agents.
- Implemented a round-robin tournament format excluding random agents.
- Added win-rate matrix visualization and final ranking of agents based on performance.
- Updated imports to include necessary libraries for the new functionality.
