… the inventory level is low, which is commonly the case with a risk-… Zhang, “Deep reinforcement
learning for resource … for humanlevel agents using deep reinforcement learning: a survey…

… However, neither bi-level optimization nor reinforcement learning … deep deterministic policy
gradient reinforcement learning algorithm, which is based on a combination of reinforcement …

… However, the adoption of deep neural networks makes the … downstream interpretations
to extra adversarial risk. In spite of the … and the threat model, at high level, we formulate the …

… We provide two base portfolio allocation methods for investors with different risk levels. The
first one is using meanvariance optimization to allocate the stocks, this method is suitable for …

… using expected returns and risk levels, which are … Reinforcement training and deep
neural network training can help investors identify patterns of market behavior and develop risk …

… Given that robots allocate different levels of attention to the pedestrians in their vicinity, we
define the RH-Attention mechanism to capture the interactions between robots and humans. …

Deep reinforcement learning (DRL) has numerous applications in the real world, thanks to
its ability to achieve high performance in a range of environments with little manual oversight. …

… However, it is also critical to consider risk factors in designing an AI … In this study, we consider
a safety level in the sense of how … a certain level of safety in AI systems using deep RL. The …

… BIM based tools to perform further risk analysis (although due to … deep reinforcement
learning [64]: a top level q value function learns a policy over intrinsic goals, while a lower level …

… To evaluate and mitigate the security risks of DRL models in power systems, we propose a
vulnerability assessment method for such DRL models under noisy data and cyber-attack. In …