Reward-risk ratio (RR) is a very important stock market definition. In order to capture the situation that the investor does not have complete information on the distribution of the underlying uncertainty, people extend RR model to distributionally robust reward-risk ratio (DRR) model. In this paper, we study the DRR problem where the ambiguity on the distributions is defined through Wassertein...

Agents interacting in a dynamically changing spatial environment often need to access the same spatial resources. A typical example is given by moving vehicles that meet at an intersection in a street network. In such situations right-of-way rules regulate the actions the vehicles involved may perform. For this application scenario we show how the Golog framework for reasoning about action and ...

Article history: Available online 3 April 2010

We consider the problem of reinforcement learning in a controlled Markov environment with multiple objective functions of the long-term average reward type. The environment is initially unknown, and furthermore may be affected by the actions of other agents, actions that are observed but cannot be predicted beforehand. We capture this situation using a stochastic game model, where the learning ...

The aim of this study was to test whether honeybees develop reward expectations. In our experiment, bees first learned to associate colors with a sugar reward in a setting closely resembling a natural foraging situation. We then evaluated whether and how the sequence of the animals' experiences with different reward magnitudes changed their later behavior in the absence of reinforcement and wit...

Learning to fear and avoid life-threatening stimuli are critical survival skills but are maladaptive when they persist in the absence of a direct threat. Thus, it is important to detect when a situation is safe and to increase behaviors leading to naturally rewarding actions, such as feeding and mating. It is unclear how the brain distinguishes between dangerous and safe situations. Here, we pr...