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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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Since my question arose from my incomprehension of $v(S_{t + 1})$ and since I got clarifications on it by Neil Slater, I thought I'd go back to this question and try to answer it again. So I'm assuming that $v(S_{t + 1})$ is a random variable made by the composition of the state-value function $v$ and the random variable $S_{t + 1}$. Since $v(s) = \mathbb{E}[...


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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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