The Fundamental Choice in Reinforcement Learning: On‑Policy vs. Off‑Policy

# The Two Ways AI Systems Learn From Trial and Error AI systems that learn by trying things and getting feedback face a basic choice: should they learn only from their own recent attempts, or also learn from past attempts (even failed ones)? This decision affects how safely the system explores new options, how much it costs to train, and how quickly it improves—making it one of the most important tradeoffs in building AI that learns on its own.