In contrast to the intense studies of deep Reinforcement Learning(RL) in games and simulations [1], employing deep RL to real world robots remains challenging, especially in high risk scenarios. Though there has been some progresses in RL based control in realistic robotics [2, 3, 4, 5], most of those previous works does not specifically deal with the safety concerns in the RL training process. For majority of high risk scenarios in real world, deep RL still suffer from bottlenecks both in cost and safety. As an example, collisions are extremely dangerous for UAV, while RL training requires thousands of times of collisions. Other works contributes to building simulation environments and bridging the gap between reality and simulation [4, 5]. However, building such simulation environment is arduous, not to mention that the gap can not be totally made up. To address the safety issue in real-world RL training, we present the Intervention Aided Reinforcement Learning (IARL) framework. Intervention is commonly used in many automatic control systems in real world for safety insurance. It is also regarded as an important evaluation criteria for autonomous navigation systems, e.g. the disengagement ratio in autonomous driving

Penalized likelihood methods are widely used for high-dimensional regression. Although many methods have been proposed and the associated theory is now well-developed, the relative efficacy of different methods in finite-sample settings, as encountered in practice, remains incompletely understood. There is therefore a need for empirical investigations in this area that can offer practical insight and guidance to users of these methods. In this paper we present a large-scale comparison of penalized regression methods. We distinguish between three related goals: prediction, variable selection and variable ranking. Our results span more than 1,800 data-generating scenarios, allowing us to systematically consider the influence of various factors (sample size, dimensionality, sparsity, signal strength and multicollinearity). We consider several widely-used methods (Lasso, Elastic Net, Ridge Regression, SCAD, the Dantzig Selector as well as Stability Selection). We find considerable variation in performance between methods, with results dependent on details of the data-generating scenario and the specific goal. Our results support a `no panacea' view, with no unambiguous winner across all scenarios, even in this restricted setting where all data align well with the assumptions underlying the methods. Lasso is well-behaved, performing competitively in many scenarios, while SCAD is highly variable. Substantial benefits from a Ridge-penalty are only seen in the most challenging scenarios with strong multi-collinearity. The results are supported by semi-synthetic analyzes using gene expression data from cancer samples. Our empirical results complement existing theory and provide a resource to compare methods across a range of scenarios and metrics.