State and Action Values in a Grid World: A Policy for a Reinforcement Learning Agent

Описание к видео State and Action Values in a Grid World: A Policy for a Reinforcement Learning Agent

** Apologies for the low volume. Just turn it up **
This video uses a grid world example to set up the idea of an agent following a policy and receiving rewards in a sequential decision making task, also known as a Reinforcement Learning problem. Although there is no learning agent yet in this video, the concepts of state values (utility) and Q-values are discussed, which are vital components of many RL algorithms. The grid world formulation comes from the book Artificial Intelligence: A Modern Approach, by Russell and Norvig.

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