Reinforcement learning control of nonlinear multi-link system
In this paper, the effects of basic parameters in reinforcement learning control such as eligibility, action and critic network constrained weights, system nonlinearities, gradient information, state-space partitioning, variance of exploration are studied in detail. It is attempted to increase feasibility for practical applications, implementation, learning efficiency, and enhance performance. Also, a novel adaptive grid algorithm is proposed to overcome the difficulty in partitioning the input space to achieve better performance. Reinforcement learning is applied for control of a nonlinear one and two-link robots. This problem dictates that the learning is performed on-line, based on a binary or real-valued reinforcement signal from a critic network, without knowing the system model or nonlinearity.
reinforcement learning learning control robotics nonlinear control learning algorithm