What is entropy in reinforcement learning and how does it relate to Q-learning exploration?

Entropy measures randomness in action selection and encourages exploration.

Higher entropy policies explore more diverse actions, preventing premature convergence. While classic Q-learning uses epsilon-greedy exploration, entropy-based methods (more common in policy gradients) can also be integrated to guide exploration in deep RL systems.