Epsilon Greedy Exploration

$\epsilon$-Greedy Exploration is an exploration strategy in reinforcement learning that takes an exploratory action with probability $\epsilon$ and a greedy action with probability $1-\epsilon$. It tackles the exploration-exploitation tradeoff with reinforcement learning algorithms: the desire to explore the state space with the desire to seek an optimal policy. Despite its simplicity, it is still commonly used as an behaviour policy $\pi$ in several state-of-the-art reinforcement learning models.

Image Credit: Robin van Embden