基于虚拟策略梯度的倒立摆平衡混合强化学习方法
A Hybrid Approach for Reinforcement Learning Using Virtual Policy Gradient for Balancing an Inverted Pendulum
摘要：
利用策略梯度算法，我们训练了一个单隐层神经网络来平衡一个物理上精确的模拟单级倒立摆。然后，训练好的权重和偏差可以转移到一个物理代理，在那里它们足够强大，可以平衡一个真正的倒立摆。这种训练模拟的混合方法可以比现实世界中更快地完成数千次试运行，从而大大减少训练时间和更多迭代，产生更强大的模型。与现有的强化学习方法相比，所得到的控制更平滑，学习速度更快，并且能够承受强制干扰。
英文摘要：
Using the policy gradient algorithm, we train a single-hidden-layer neural network to balance a physically accurate simulation of a single inverted pendulum. The trained weights and biases can then be transferred to a physical agent, where they are robust enough to to balance a real inverted pendulum. This hybrid approach of training a simulation allows thousands of trial runs to be completed orders of magnitude faster than would be possible in the real world, resulting in greatly reduced training time and more iterations, producing a more robust model. When compared with existing reinforcement learning methods, the resulting control is smoother, learned faster, and able to withstand forced disturbances.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
