| 总结: | <p>A multi-armed bandit is the simplest problem to study learning under uncertainty when decisions affect information. A standard approach to the multi-armed bandit often gives a heuristic construction of an algorithm and proves its regret bound. Following a constructive approach, it is often possible to find a scenario where following heuristic approaches gives a poor decision.</p>
<p>In this thesis, we consider solving the multi-armed bandit problem from first principles, in terms of stochastic control. We propose two novel approaches to address the multi-armed bandit problem. The first approach is to apply a relaxed control analogy to obtain a semi-closed form approximation to the optimal solution. The proposed model covers a wide range of bandit problems, and the proposed strategy can be computed with a low computational complexity with an empirically strong performance. The second approach focuses on bandits with independent arms and considers the interaction between two aspects of uncertainty: uncertainty aversion and learning. These aspects are in some sense opposite; one is pessimistic, but another is optimistic. To see this interaction, we consider a class of strategies that allows marginal projection on each arm and prove Gittins theorem under nonlinear expectation.</p>
<p>Overall, our proposed approaches provide an understanding of how to make decisions under uncertainty when our decisions determine future information. These results should be helpful as a foundation to combine stochastic control with more modern AI theories.</p>
|
|---|