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I think I may be in position to answer my own question. The Bellman equation (for the optimal policy) for a MDP with $r(s,a,s')$ rewards would look like this: $$V(s) = \max_a \left\{ \sum_{s'} p(s'|s,a)(r(s,a,s') + \gamma V(s')) \right\} $$ $$V(s) = \max_a \left\{ \sum_{s'} p(s'|s,a) \cdot r(s,a,s') + \gamma \sum_{s'} p(s'|a,s) \cdot V(s') \right\} $$ Now, ...


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This depends on the complexity of the environment being learned, and the purpose for learning it. There is no general answer. For the simple environments used to teach reinforcement learning (RL), often the optimal solution is obvious, or can be calculated and proven optimal. For instance, any environment that can be solved using policy iteration will have ...


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shouldn't the expected return be calculated for some faraway time in the future (𝑡+𝑛) instead of current time $t$? This is partly a notation issue, but $G_t$ is already the future sum of rewards as seen by the first (and correct) equation in your question. You don't actually know the value of any individual return $g_t$* until after $t+n$. However, you ...


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I found the answer further into the paper! I'll post it here for everyone. Given any user, there is no pre-known targeted item in the KGRE-Rec (Knowledge Graph Reasoning for Explainable Recommendation) problem, so it is unfeasible to consider binary rewards indicating whether the agent has reached a target or not. Instead, the agent is encouraged to ...


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