is a target network, having identical architecture as the $Q$ network, and whose parameter vector $\hat{\theta}$ is copied from the $Q$ network parameter $\theta$ every certain number of training iterations. The method makes use of Experience Replay, which is a storage buffer holding historical instances of the form $(s_t, a_t, r_t, s_{t+1})$ which are experienced by the agent during the episode. At the end of each training episode, a minibatch of such instances is randomly sampled from the buffer and uses for gradient computation, following .

Double DQN (DDQN [@van2016deep]) is an improvement of DQN, aiming to be less prone to overestimation of $Q$ values. It differs from DQN in that the evaluation of the Q values and the selection of the best action uses different parameter vectors: the selection is done using the online parameter $\theta$, while the evaluation uses the target parameter vector $\hat{\theta}$. $$L_{DDQN}(\theta) = \left( \left(\mathcal{R}(s, a) + \gamma  Q(s', \arg\max_a Q(s', a; \theta); \hat{\theta})\right) - Q(s, a; \theta)\right)^2. \label{eq:ddqn}$$

The Proposed Approach {#sec:proposed}
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In this section we describe our proposed approach for adaptive selection of small number of questions that will allow to accurately predict the outcome variable.

### Rationale {#sec:rationale}

Our questionnaire dataset 