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In a Markov Decision Process (MDP) model, we define a set of states ($S$), a set of actions ($A$), the rewards ($R$), and the transition probabilities $P(s' \mid s, a)$. The goal is to figure out the best action to take in each of the states, i.e. the policy $\pi$. Policy To calculate the policy we make use of the Bellman equation: $$V_{i+1}(s)=R(s)+\gamma \...


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In the reinforcement learning setting, an agent interacts with an environment in (discrete) time steps, which are incremented after the agent takes an action, receives a reward and the "system" (the environment and the agent) moves to a new state. More precisely, at time step $t=0$ (the first time step), the environment (including the agent) is in some ...


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