How does Q-Learning interact with non-convex function approximation landscapes?

Q-Learning with neural networks operates in a non-convex optimization landscape, leading to local minima and instability.

Deep Q-learning optimizes a non-convex loss surface, meaning gradient descent can converge to local minima or saddle points. Combined with non-stationary targets (bootstrapping), this makes optimization highly unstable. Techniques like target networks, replay buffers, and carefully tuned optimizers help mitigate these issues but do not eliminate non-convexity challenges.