Mini-Batches in On- and Off-Policy Deep Reinforcement Learning

This lecture covers the importance of mini-batches in Deep Reinforcement Learning, explaining how to avoid data correlation by using replay buffers or multiple actors. It discusses on-policy and off-policy methods, such as Q-Learning and Advantage Actor-Critic, and the pros and cons of each approach.