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I have two suggestions that you can look into. Based on my own work in RL, I believe the first one will require less work to implement. If the observability of the environment is not an issue, then you could give the agent a relative measure (distance to the goal) as part of the observation to provide it with knowledge of how far away it is. You can also ...

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Predicting the correct amount of repetitions for an action sounds like a regression task. Turning it into a classification task using a model with n output nodes will lead to several drawbacks, the biggest ones being: Having to choose a priori a finite max amount of actions n Turning the data into really sparse vectors, especially for large n. So a better ...

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The expression "number of actions" is being used in the same way in both cases. In fact, the letter $m$ is used in both cases. The number of actions (in the state $s$) is the number of possible actions that you can take in the state $s$. So, here, $m$ does not refer to the dimensionality of an action, but to the size of the action set for a state. ...

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As I understand it, the Bellman equation assumes the setting to be deterministic, meaning that, if you're in state $s_t$ as you take action $a_1$, you should always reach the same $s_{t+1}$. This is not correct, which is a good thing for you. The Bellman equation for action values for an arbitrary policy \$\pi(a|s): \mathcal{S} \times \mathcal{A} \rightarrow ...

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