ε-greedy strategy

The ε-greedy strategy is a method used in decision-making, especially in reinforcement learning, to balance exploring new options and exploiting known successful ones. It involves choosing the best-known option most of the time, but occasionally trying a random different choice with a small probability ε. This approach helps discover potentially better options while still prioritizing actions that have worked well before. Over time, ε can decrease, allowing the system to increasingly focus on the best-known choices, improving performance while still maintaining some exploration to avoid missing better alternatives.