What does the epsilon-greedy algorithm accomplish in Reinforcement Learning?

The epsilon-greedy algorithm in Reinforcement Learning is designed to balance the trade-off between exploration and exploitation. By setting a probability threshold (epsilon), the algorithm allows for both exploration of new actions and exploitation of known actions.

Specifically, with a probability of epsilon, the agent chooses an action at random, thereby exploring less-frequent actions that may lead to higher rewards. Conversely, with a probability of (1 - epsilon), the agent selects the best-known action based on current value estimates, which is the exploitation aspect. This balance is crucial because it enables the agent to discover potentially better actions while still leveraging what it has learned from past interactions to maximize its rewards.

This method is effective in various scenarios and is one of the foundational strategies used in reinforcement learning algorithms to ensure that the agent does not become overly reliant on a limited set of actions, which might lead to suboptimal performance.