Robotic Tracking Control with Kernel Trick-based Reinforcement Learning
In recent years, reinforcement learning has been developed dramatically and is widely used to solve control problems, e.g., playing games. However, there are still some problems for reinforcement learning to perform robotic control tasks. Fortunately, the kernel trick-based methods provide a chance to deal with those challenges. This work aims at developing a kernel trick-based learning control method to carry out robotic tracking control tasks. A reward system, in this work, is presented in order to speed up the learning processes. And then, a kernel trick-based reinforcement learning tracking controller is presented to perform tracking control tasks on a robotic manipulator system. To evaluate the policy and assist the reward system to accelerate the speed of finding the optimal control policy, a critic system is introduced. Finally, from the comparison with the benchmark, the simulation results illustrate that our algorithm has faster convergence rate and can execute tracking control tasks effectively, the reward function and the critic system proposed in this work is efficient.
Code (0)
등록된 구현이 없습니다.
Tasks
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
Reinforcement Learning Tracking Control for Robotic Manipulator With Kernel-Based Dynamic Model
Reinforcement learning (RL) is an efficient learning approach to solving control problems for a robot by interacting with the environment to acquire the optimal control policy. However, there are many challenges for R…
continuous-controlContinuous Controlreinforcement-learningReinforcement Learning+1Data Informed Residual Reinforcement Learning for High-Dimensional Robotic Tracking Control
The learning inefficiency of reinforcement learning (RL) from scratch hinders its practical application towards continuous robotic tracking control, especially for high-dimensional robots. This work proposes a data-infor…
reinforcement-learningReinforcement Learning (RL)Influence of Radial Basis Activation Functions on Intelligent Controller for Robotic Manipulators
This paper presents an intelligent control framework for trajectory tracking of robotic manipulators using radial basis function (RBF) neural networks for online disturbance estimation. The proposed control structure com…
Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning
We investigate the effect of including domain knowledge about a robotic system's causal relations when generating explanations. To this end, we compare two methods from explainable artificial intelligence, the popular Ke…
Deep Reinforcement LearningExplainable artificial intelligenceReinforcement Learning (RL)Leveraging Symmetry to Accelerate Learning of Trajectory Tracking Controllers for Free-Flying Robotic Systems
Tracking controllers enable robotic systems to accurately follow planned reference trajectories. In particular, reinforcement learning (RL) has shown promise in the synthesis of controllers for systems with complex dynam…
Reinforcement Learning (RL)