An educational reinforcement-learning project that uses a game environment to make policy learning and agent behavior observable. The repository is intended for experimentation with state representation, actions, rewards, and iterative policy improvement.
- Environment-state modeling and action selection.
- Reward design and feedback loops.
- Training and evaluation of an agent in a controlled game setting.
- Visualization of behavior for easier debugging and learning.
Follow the notebook or Python entry point included in the repository and install the dependencies listed there. Keep generated model artifacts and experiment outputs separate from source code when extending the project.
Python, reinforcement learning, game simulation, numerical experimentation, and visualization.