# What is the difference between bootstrapping and sampling in reinforcement learning?

I have come across a David Silver's slide which contains both the terms "bootstrapping" and "sampling". Is there any realistic example which helps me to understand the concepts better.

• You may need to reproduce and/or paraphrase some of the text from the slide to give enough context to answer. In RL, it is possible to have both bootstrapping and sampling occurring within a single update rule, and it is not clear whether you need the distinction made in that context or whether the context is e.g. Monte Carlo vs TD learning. Apr 24, 2018 at 7:22
• I just wanted to know the distinction between them in a general sense. @NeilSlater Apr 24, 2018 at 20:13

• Bootstrapping: When you estimate something based on another estimation. In the case of Q-learning for example this is what is happening when you modify your current reward estimation $r_t$ by adding the correction term $\max_a' Q(s',a')$ which is the maximum of the action value over all actions of the next state. Essentially you are estimating your current action value Q by using an estimation of the future Q. Neil has answered that in detail here.