Q-learning

Proved the mathematical convergence of Q-learning to optimal action-values in discrete Markov Decision Processes without an environment transition model.

Serves as qualified theoretical and systems foundation for task-reinforcement-learning.

Assumptions

• Markovian state dynamics and stationary reward functions hold in target evaluation environments

Limitations

• Sample efficiency, exploration stability, and real-world sim-to-real transfer gaps require specialized tuning