Nos derniers articles

"We develop an approach for solving time-consistent risk-sensitive stochastic optimization problems using model-free reinforcement learning (RL). Specifically, we assume agents assess the risk of a sequence of random variables using dynamic convex risk measures. We employ a time-consistent dynamic programming principle to determine the value of a particular policy, and develop policy gradient update rules. We further develop an actor-critic style algorithm using neural networks to optimize over policies. Finally, we demonstrate the performance and flexibility of our approach by applying it to optimization problems in statistical arbitrage trading and obstacle avoidance robot control."

"... insurance carriers should internally organize key stakeholders related to AI strategy and development to collaboratively evaluate how they define and develop AI projects and models. If carriers have not yet established broad life cycle governance or risk management practices unique to their AI/machine learning systems, they should begin that journey with haste."