Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Despite an ever growing literature on reinforcement learning algorithms and applications, much less is known about their statistical inference.
In this paper, we investigate the large-sample behaviors of the Q-value estimates with closed-form characterizations of the asymptotic variances.
This allows us to efficiently construct confidence regions for Q-value and optimal value functions, and to develop policies to minimize their estimation errors.
This also leads to a policy exploration strategy that relies on estimating the relative discrepancies among the Q estimates.
Numerical experiments show superior performances of our exploration strategy than other benchmark approaches.
We consider the classical reinforcement learning (RL) problem where the agent interacts with a random environment and aims to maximize the accumulated discounted reward over time.
The environment is formulated as a Markov decision process (MDP) and the agent is uncertain about the true dynamics to start with.
As the agent interacts with the environment, data about the system dynamics are collected and the agent becomes increasingly confident about her decision.
With finite data, however, the potential reward from each decision is estimated with errors and the agent may be led to a suboptimal decision.
Our focus in this paper is on statistically efficient methodologies to quantify these errors and uncertainties, and to demonstrate their use in obtaining better policies.
More precisely, we investigate the large-sample behaviors of estimated Q-value, optimal value function, and their associated policies.
Our results are in the form of asymptotic convergence to an explicitly identified and computable Gaussian (or other) distribution, as the collected data sizes increase.
The motivation of our investigation is three-fold.
First, these precise asymptotic statements allow us to construct accurate confidence regions for quantities related to the optimal policy, and, like classical statistical inference, they can assess the reliability of the current estimates with respect to the data noises.
Second, our results complement some finite-sample error bounds developed in the literature (Kearns & Singh, 1998; Kakade, 2003; Munos & Szepesvári, 2008) , by supplementing a closed-form asymptotic variance that often shows up in the first-order terms in these bounds.
Our third and most important motivation is to design good exploration policies by directly using our tight error estimates.
Motivated by recent autonomous-driving and other applications (e.g., Kalashnikov et al. (2018) ), we consider the pure exploration setting where an agent is first assigned an initial period to collect as much experience as possible, and then, with the optimal policy trained offline, starts deployment to gain reward.
We propose an efficient strategy to explore by optimizing the worst-case estimated relative discrepancy among the Q-values (ratio of mean squared difference to variance), which provides a proxy for the probability of selecting the best policy.
Similar criteria have appeared in the so-called optimal computing budget allocation (OCBA) procedure in simulation-based optimization (Chen & Lee, 2011 ) (a problem closely related to best-arm identification (Audibert & Bubeck, 2010) in online learning).
In this approach, one divides computation (or observation) budget into stages in which one sequentially updates mean and variance estimates, and optimizes next-stage budget allocations according to the worst-case relative discrepancy criterion.
Our proposed procedure, which we term Q-OCBA, follows this idea with a crucial use of our Q-value estimates and randomized policies to achieve the optimal allocation.
We demonstrate how this idea consistently outperforms other benchmark exploration policies, both in terms of the probability in selecting the best policy and generating the tightest confidence bounds for value estimates at the end of the exploration period.
Regarding the problem of constructing tight error estimates in RL, the closest work to ours is Mannor et al. (2004; 2007) , which studies the bias and variance in value function estimates with a fixed policy.
Our technique resolves a main technical challenge in Mannor et al. (2004; 2007) , which allows us to substantially generalize their variance results to Q-values, optimal value functions and asymptotic distributional statements.
The derivation in Mannor et al. (2004; 2007) hinges on an expansion of the value function in terms of the perturbation of the transition matrix, which (as pointed out by the authors) is not easily extendable from a fixed-policy to the optimal value function.
In contrast, our results utilize an implicit function theorem applied to the Bellman equation that can be verified to be sufficiently smooth.
This idea turns out to allow us to obtain gradients for Q-values, translate to the optimal value function, and furthermore generalize to similar results for constrained MDP and approximate value iterations.
We also relate our work to the line of studies on dynamic treatment regimes (DTR) (Laber et al., 2014) applied commonly in medical decision-making, which focuses on the statistical properties of polices on finite horizon (such as two-period).
Our infinite-horizon results on the optimal value and Q-value distinguishes our developments from the DTR literature.
Moreover, our result on the non-unique policy case can be demonstrated to correspond to the "non-regularity" concept in DTR, where the true parameters are very close to the decision "boundaries" that switch the optimal policy (motivated by situations of small treatment effects), thus making the obtained policy highly sensitive to estimation noises.
In the rest of this paper, we first describe our MDP setup and notations (Section 2).
Then we present our results on large-sample behaviors (Section 3), demonstrate their use in exploration strategies (Section 4), and finally substantiate our findings with experimental results (Section 5).
In the Appendix, we first present generalizations of our theoretical results to constrained MDP (A.1) and problems using approximate value iteration (A.2).
Then we include more numerical experiments (B), followed by all the proofs (C).
We investigate the large-sample behaviors of the Q-value estimates and proposed an efficient exploration strategy that relies on estimating the relative discrepancies among the Q estimates.