Predictive Analytics with TensorFlow 11.1: Reinforcement Learning

The video tutorial introduces reinforcement learning (RL) as a middle ground between supervised and unsupervised learning. It explains how RL involves an agent making decisions to maximize long-term rewards by balancing exploration and exploitation. Key concepts such as value function, policy, utility, and Q function are discussed, highlighting their roles in determining optimal actions. The tutorial also outlines the basic steps of RL algorithms: infer, do, and learn. Examples and diagrams illustrate these concepts, emphasizing their application in areas like robotics.

What distinguishes reinforcement learning from supervised and unsupervised learning?

What is the role of the value function in reinforcement learning?

Describe the process an RL agent follows during the learning phase.

Explain the concept of exploration versus exploitation in reinforcement learning.

What is the optimal policy in reinforcement learning?

How does the utility function relate to the expected rewards in reinforcement learning?

Summarize the three main steps that most reinforcement learning algorithms follow.