论文标题
自适应风险敏感模型通过随机搜索进行预测性控制
Adaptive Risk Sensitive Model Predictive Control with Stochastic Search
论文作者
论文摘要
我们提出了一个通用框架，用于使用随机搜索优化动态系统的条件价值风险。该框架能够从模型中的初始条件，随机动力学和不确定参数中处理不确定性。将算法与对风险敏感的分布增强学习框架进行比较，并在带有随机动力学的钟摆和卡特柱上表现出色。我们还通过优化粒子过滤器在仿真中的摆，cartpole和Quadcopter上优化框架对机器人技术作为自适应风险敏感控制器的适用性。
We present a general framework for optimizing the Conditional Value-at-Risk for dynamical systems using stochastic search. The framework is capable of handling the uncertainty from the initial condition, stochastic dynamics, and uncertain parameters in the model. The algorithm is compared against a risk-sensitive distributional reinforcement learning framework and demonstrates outperformance on a pendulum and cartpole with stochastic dynamics. We also showcase the applicability of the framework to robotics as an adaptive risk-sensitive controller by optimizing with respect to the fully nonlinear belief provided by a particle filter on a pendulum, cartpole, and quadcopter in simulation.
