An epsilon-greedy policy is a method used in reinforcement learning to balance exploration and exploitation when making decisions. In this approach, an agent, which learns to make decisions to maximize some reward, decides whether to explore new actions or exploit known ones based on a probability parameter represented by epsilon (ε). Specifically, with a probability of ε, the agent will choose a random action (exploration), while with a probability of 1 - ε, the agent will choose the best-known action based on its previous experiences (exploitation).
To clarify how epsilon-greedy works, consider a scenario where an agent must choose between several actions that may yield different rewards. If ε is set to 0.1, this indicates that 10% of the time, the agent will explore by selecting a random action, while the other 90% of the time, it will select the action that has previously provided the highest reward. This method allows the agent to discover potentially better actions while still leveraging the knowledge it has gained so far. Over time, adjusting ε can lead to improved learning outcomes as the agent may start with a higher ε to explore more and gradually shift to lower values to focus on exploiting the best-known actions.
Implementing an epsilon-greedy policy is straightforward. In a simple programming scenario, you would generate a random number between 0 and 1. If this number is less than ε, you choose a random action; otherwise, you select the action with the highest estimated value. This approach ensures that the agent does not get stuck in a local optimum and has the flexibility to discover new strategies that could provide better long-term rewards. Overall, the epsilon-greedy policy is a fundamental concept in reinforcement learning that helps agents learn effectively in uncertain environments.