Constrained Attractor Selection Using Deep Reinforcement Learning
This work addresses attractor control in nonlinear systems, which is incremental as it applies existing RL methods to a known problem with constraints.
The paper tackled the problem of attractor selection in nonlinear dynamical systems with constrained actuation, using deep reinforcement learning methods (CEM and DDPG) on a Duffing oscillator, and found that both methods achieved attractor selection under constraints with nearly identical success rates, while DDPG offered advantages like high learning rate and low variance.
This paper describes an approach for attractor selection (or multi-stability control) in nonlinear dynamical systems with constrained actuation. Attractor selection is obtained using two different deep reinforcement learning methods: 1) the cross-entropy method (CEM) and 2) the deep deterministic policy gradient (DDPG) method. The framework and algorithms for applying these control methods are presented. Experiments were performed on a Duffing oscillator, as it is a classic nonlinear dynamical system with multiple attractors. Both methods achieve attractor selection under various control constraints. While these methods have nearly identical success rates, the DDPG method has the advantages of a high learning rate, low performance variance, and a smooth control approach. This study demonstrates the ability of two reinforcement learning approaches to achieve constrained attractor selection.