This lecture discusses the scaling problem of Temporal Difference (TD) algorithms and introduces n-step TD methods as a solution. It covers the concept of n-step SARSA and n-step expected SARSA, explaining how they improve the flow of information in reinforcement learning. The lecture also explores the use of n-step TD methods for estimating both action values and state values, providing a comprehensive overview of their implementation and benefits.