Constrained Attractor Selection Using Deep Reinforcement Learning
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This paper describes an approach for attractor selection in nonlinear dynamical systems with constrained actuation. Attractor selection is achieved 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 a high learning rate, low performance variance, and offers a smooth control approach. This experiment demonstrates the applicability of reinforcement learning to constrained attractor selection problems.
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