Reinforcement Learning Algorithms with Python

Multi-armed bandit

The multi-armed bandit problem is the classic RL problem that is used to illustrate the exploration-exploitation trade-off dilemma. In the dilemma, an agent has to choose from a fixed set of resources, in order to maximize the expected reward. The name multi-armed bandit comes from a gambler that is playing multiple slot machines, each with a stochastic reward from a different probability distribution. The gambler has to learn the best strategy in order to achieve the highest long-term reward.

This situation is illustrated in the following diagram. In this particular example, the gambler (the ghost) has to choose one of the five slot machines, all with different and unknown reward probabilities, in order to win the highest ...