Synthesizing optimal controllers for dynamical systems often involves solving
optimization problems with hard real-time constraints. These constraints
determine the class of numerical methods that can be applied: computationally
expensive but accurate numerical routines are replaced by fast and inaccurate
methods, trading inference time for solution accuracy. This paper provides
techniques to improve the quality of optimized control policies given a fixed
computational budget. We achieve the above via a hypersolvers approach, which
hybridizes a differential equation solver and a neural network. The performance
is evaluated in direct and receding-horizon optimal control tasks in both low
and high dimensions, where the proposed approach shows consistent Pareto
improvements in solution accuracy and control performance.

本文提出了一种通过超级求解器方法，将微分方程求解器和神经网络相混合的技术以提高在给定计算预算下优化控制策略的质量，并在低维和高维直接和远程视野最优控制任务中验证该方法的性能，结果表明提出的方法在解决精度和控制性能上具有一致的 Pareto 改进。

用于快速且准确的数值最优控制的神经求解器

Neural Solvers for Fast and Accurate Numerical Optimal Control

Designing optimal controllers continues to be challenging as systems are
becoming complex and are inherently nonlinear. The principal advantage of
reinforcement learning (RL) is its ability to learn from the interaction with
the environment and provide optimal control strategy. In this paper, RL is
explored in the context of control of the benchmark cartpole dynamical system
with no prior knowledge of the dynamics. RL algorithms such as
temporal-difference, policy gradient actor-critic, and value function
approximation are compared in this context with the standard LQR solution.
Further, we propose a novel approach to integrate RL and swing-up controllers.

本文研究了在没有先验动态知识的情况下，利用强化学习算法（如时间差分、策略梯度、价值函数逼近）对基准车杆动力系统进行控制的最优控制策略，进一步提出了将强化学习和摆起控制器相结合的新方法。