What is minimax loss?
A loss function used in machine learning to train models, especially in game-playing AI and reinforcement learning
minimax loss explained in plain English
Analogy
Imagine you're playing a game of chess. The minimax loss is like thinking about the best move you can make to win, while also considering the worst move your opponent could make to beat you. You're trying to minimize the loss of the game by thinking about the maximum possible loss.
Example
AlphaGo, a computer program that plays Go, used minimax loss to train its model and defeat a human world champion. The model considered all possible moves and their outcomes, and adjusted its strategy to minimize the maximum loss.
How is minimax loss used?
Common misconceptions about minimax loss
Some people think that minimax loss is only used in game-playing AI, but it's also used in other areas, such as finance and robotics, where models need to make decisions under uncertainty.
History
The concept of minimax loss originated in the 1920s in the field of game theory, and was later adopted in machine learning and artificial intelligence.
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