https://arshadhs.github.io/tags/mdp/Recent content in MDP on Arshad SiddiquiHugoen-ushttps://arshadhs.github.io/docs/ai/045-deep-reinforcement-learning/040-rewards-returns-policies-and-value-functions/Mon, 01 Jan 0001 00:00:00 +0000https://arshadhs.github.io/docs/ai/045-deep-reinforcement-learning/040-rewards-returns-policies-and-value-functions/<h1 id="rewards-returns-policies-and-value-functions"> Rewards, Returns, Policies and Value Functions <a class="anchor" href="#rewards-returns-policies-and-value-functions">#</a> </h1> <p>An MDP describes how states, actions, rewards and transitions fit together. The next task is to evaluate behaviour: what should the agent try to achieve, how should future rewards be counted, and how good is a state or action over the long term?</p> <p>Rewards define the objective, returns combine rewards across time, a policy describes behaviour, and value functions predict the long-term quality of that behaviour.</p>