How does Q-Learning deal with reward noise in stochastic environments?

Q-Learning handles reward noise by averaging over many samples, but high noise slows convergence.

In stochastic environments, rewards are random variables rather than deterministic values. Q-Learning estimates expected returns by averaging over many experiences. However, high variance in rewards increases uncertainty in Q-updates, requiring more samples for stable convergence. Techniques like reward smoothing, normalization, and larger replay buffers help mitigate noise impact.