その イプシロン・グリーディ 戦略 is a fundamental approach in 強化学習 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) 現在最も高いと知られている行動を選択する代わりに reward （活用）。
実際的には、各 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.
この戦略は、最適な行動がすぐには明らかでない環境で特に有用であり、不確実な状況でより堅牢な学習を可能にします。ただし、εが小さすぎると、エージェントは早期にサブ最適な解に収束する可能性があり、一方で大きすぎると、既知の報酬を効果的に活用できなくなることがあります。