This relaxed program is differentiable and can be trained end-to-end, and the resulting training loss is an approximately admissible heuristic that can guide the combinatorial search.

This work addresses the problem of regret minimization in non-stochastic multi-armed bandit problems, focusing on performance guarantees that hold with high probability. Such results are rather scarce in the literature since proving them requires a large deal of technical effort and significant modifications to the standard, more intuitive algorithms that come only with guarantees that hold on expectation. One of these modifications is forcing the learner to sample arms from the uniform distribution at least Ω( T) times over T rounds, which can adversely affect performance if many of the arms are suboptimal. While it is widely conjectured that this property is essential for proving high-probability regret bounds, we show in this paper that it is possible to achieve such strong results without this undesirable exploration component. Our result relies on a simple and intuitive loss-estimation strategy called Implicit eXploration (IX) that allows a remarkably clean analysis. To demonstrate the flexibility of our technique, we derive several improved high-probability bounds for various extensions of the standard multi-armed bandit framework. Finally, we conduct a simple experiment that illustrates the robustness of our implicit exploration technique.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Multi-task learning is a very challenging problem in reinforcement learning.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

The ability to quickly adapt to changing environments is an important quality of intelligent systems. For such quick adaptation, it is important to be able to identify, memorise, and recall useful past experiences when acquiring new ones.

Neural Information Processing Systems http://nips.cc/