Epsilon greedy policy: Revision history

18 March 2023

• curprev 21:5521:55, 18 March 2023‎ Walle talk contribs‎ 3,508 bytes +3,508‎ Created page with "{{see also|Machine learning terms}} ==Introduction== The '''Epsilon-Greedy Policy''' is a widely used exploration-exploitation strategy in Reinforcement Learning (RL) algorithms. It helps balance the decision-making process between exploring new actions and exploiting the knowledge acquired thus far in order to maximize the expected cumulative rewards. ==Exploration and Exploitation Dilemma== In the context of RL, an agent interacts with an environment and learns an..."