Nos derniers articles

This paper discusses the role of public policy in #regulating the development of #ai, #ml, and #robotics, and the potential #risks of different approaches to #governance. It explores the tension between precautionary principles that prioritize risk avoidance and permissionless innovation that encourages entrepreneurship, and advocates for a more flexible, #bottomup governance approach that can address risks without hindering innovation.

"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."