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1

This is the original Q-Learning paper by Watkins, though you may need to pay for access to this. This is the Nature paper that introduced the DQN.

3

Scale your neural network inputs. The raw observations are in range $[0,89]$, and neural networks will cope badly with that used as inputs. The ideal case for NN for each input feature is a gaussian distribution with mean 0, standard deviation 1. You don't need that to be perfect, though. A simple scale - divide each element by $30$ and subtract $1.5$ - will ...

3

I'll assume Q-player is being trained with Q learning (note, Q tables can be useful in other algorithms too, like SARSA). Q learning is an off policy algorithm, meaning that the Q values can be learned regardless of the policy used to collect data. So the Q player can be following a random policy, or even a fixed pre defined policy if you want. Usually, ...

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I am going to stick with Q learning here to keep things simple. Most value-based reinforcement learning used for optimal control will have some statement similar to: Choose $a$ from $s$ using policy derived from $Q$ First, yes this is always the current Q function or Q table, evaluated for the state of interest. When you are choosing the agent's best guess ...

2

In addition to the RF [*], you also need to define an exploratory policy (an example is the $\epsilon$-greedy), which allows you to explore the environment and learn the state-action value function $\hat{q}$. Moreover, although you don't need to know the details (i.e. the specific probabilities of transitioning from one state to the other) of the transition ...

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