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The bandit problem is an MDP. You can make the same argument about needing data to learn in the stateful MDP setting. The thing is, the data you need (the past rewards in this case) was drawn iid (conditioned on the arm) and is not actually a trajectory. For instance, once you learn an optimal policy, you no longer need to gather data and the sequence of ...


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Terminal state is always the same in the sense that it represents the same thing, that the episode is over. They don’t need to be the exact same state; for instance you could have an $n$ by $n$ grid world where the top right and bottom left states are terminal as when you reach these your agent dies. These are both terminal but not the same state. For chess ...


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Wow, that's a really confusing example, if I were you I would check out some other RL resources. I wouldn't consider h being the last step and h-1 being the previous step. In terms of steps of iterations of the dynamic programming algorithm, h is actually the first step, h-1 the next step and so on. Viewing it in these terms it makes sense that the Value of ...


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I believe the claim is true. Here is my attempt at a proof. Let us consider the optimal infinite horizon value function $V_\alpha^*$ of $\mathcal{M}_\alpha$ at an arbitrary state $s \in S$. The value $V_\alpha^*(s)$ is the expected sum of discounted rewards under an optimal policy $\pi_\alpha^*$, i.e., \begin{equation} V_\alpha^*(s) = \mathbb{E}_{\rho_\...


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Reinforcement Learning is really fun because the agent will find any bug in your implementation and will exploit it. >>> take_left(0) 0 >>> take_left(1) -4 The agent figured out your bug with negative values and exploits negative indexing to get to the target faster.


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