Lecture
This lecture discusses the exploration-exploitation dilemma in reinforcement learning, where the challenge lies in balancing the need to explore new possibilities to find optimal actions with the desire to exploit known rewarding actions. It covers the issues of correct Q values estimation, the drawbacks of a greedy strategy, and practical approaches like epsilon-greedy methods. Through examples and simulations, the instructor illustrates how different strategies impact decision-making and performance in reinforcement learning tasks.