text: Mobile Context-Aware Recommender Systems can be naturally modelled as an exploration exploitation trade-off (exr exp) problem, where the system has to choose between maximizing its expected rewards dealing with its current knowledge (exploitation) and learning more about the unknown user's preferences to improve its knowledge (exploration). This problem has been addressed by the reinforcement learning community but they do not consider the risk level of the current user's situation, where it may be dangerous to recommend items the user may not desire in her current situation if the risk level is high. We introduce in this paper an algorithm named R-UCB that considers the risk level of the user's situation to adaptively balance between exr and exp. The detailed analysis of the experimental results reveals several important discoveries in the exr exp behaviour.
  @cite_5: Abstract With regard to future service robots, unsafe exceptional circumstances can occur in complex systems that are hardly to foresee. In this paper, the assumption of having no knowledge about the environment is investigated using reinforcement learning as an option for learning behavior by trial-and-error. In such a scenario, action-selection decisions are made based on future reward predictions for minimizing costs in reaching a goal. It is shown that the selection of safety-critical actions leading to highly negative costs from the environment is directly related to the exploration exploitation dilemma in temporal-difference learning. For this, several exploration policies are investigated with regard to worst- and best-case performance in a dynamic environment. Our results show that in contrast to established exploration policies like e-Greedy and Softmax, the recently proposed VDBE-Softmax policy seems to be more appropriate for such applications due to its robustness of the exploration parameter for unexpected situations.
summary: A recent work, @cite_5 , treated the risk and proposed the VDBE algorithm to extend @math -greedy by introducing a state-dependent exploration probability, instead of hand-tuning a global parameter. The system makes exploration in situations when the knowledge about the environment is uncertain, which is indicated by fluctuating action values during learning. In contrast, the amount of exploration is reduced as far as the system's knowledge becomes certain, which is indicated by very small or no value differences.