Reinforcement Learning and Deep RL Python Theory and Projects - Policy and Plan

The video tutorial introduces the concept of policy in agent strategy, explaining how policies guide agents to achieve goals. It discusses three types of policies: random, careful, and reinforcement learning. The random policy involves arbitrary actions, while the careful policy is more strategic but not optimal. Reinforcement learning policy is highlighted as a method for agents to learn the shortest path to a goal. The tutorial also covers how states generate actions and introduces the concept of LAN as a collection of policies.

Explain the concept of reinforcement learning policy as described in the text.

What role does the state play in determining the action suggested by a policy?

Define what a LAN is in the context of policies and their collection.