Reinforcement Learning and Deep RL Python Theory and Projects - Off Policy Versus On Policy

The video tutorial introduces two key terminologies in reinforcement learning: off-policy and on-policy. It explains that Q-learning follows an off-policy approach, where the learning agent derives the value function from another policy. In contrast, Sarsa uses an on-policy approach, learning from its current policy. The tutorial briefly touches on the mathematical equations for both methods but focuses on explaining the differences through Python code. The main distinction is that Q-learning seeks the maximum value from a new state, while Sarsa uses the value of a new action from its policy.

How does the Q learning equation differ from the Sarsa equation?

In the context of Q learning, what is meant by 'maximum value of new state'?

What steps does Sarsa take to determine the value of a new action?