El Exploración-Explotación Epsilon-Greedy Estrategia is a fundamental approach in aprendizaje por refuerzo that helps an agent decide whether to explore new actions or exploit known rewarding actions. The strategy incorporates a parameter known as epsilon (ε), which represents the probability of choosing a random action (exploration) en lugar de seleccionar la acción que actualmente se sabe que proporciona la mayor reward (explotación).
En términos prácticos, durante cada decision-making step, the agent will choose a random action with a probability of ε. For the remaining probability (1 – ε), the agent selects the action that has the highest estimated value based on prior experiences. This balance allows the agent to gather new information about the environment while still leveraging its existing knowledge to maximize rewards.
The value of epsilon is typically set to be small (e.g., 0.1 or 0.01), meaning that the agent will explore randomly 10% or 1% of the time, respectively. Epsilon can also be adjusted over time; for instance, it may start high to encourage exploration and gradually decrease to focus on exploitation as the agent gains confidence in its learned values.
Esta estrategia es particularmente útil en entornos donde las acciones óptimas no son inmediatamente claras, y permite un aprendizaje más robusto en situaciones de incertidumbre. Sin embargo, si ε es demasiado pequeño, el agente puede converger prematuramente en soluciones subóptimas, mientras que si es demasiado grande, el agente puede no explotar las recompensas conocidas de manera efectiva.