Abstract
Reinforcement learning has been intensively investigated and developed in artificial intelligence in the absence of training data, such as autonomous driving vehicles, robot control, and internet advertising. However, the computational cost of reinforcement learning with deep neural networks is extremely high, and reducing the learning cost is a challenging issue. We propose a photonic on-line implementation of reinforcement learning using optoelectronic delay-based reservoir computing, both experimentally and numerically. In the proposed scheme, we accelerate reinforcement learning at a rate of several megahertz because there is no required learning process for the internal connection weights in reservoir computing. We perform two benchmark tasks, CartPole-v0 and MountanCar-v0 tasks, to evaluate the proposed scheme. Our results represent the first hardware implementation of reinforcement learning based on photonic reservoir computing and paves the way for fast and efficient reinforcement learning as a novel photonic accelerator.
Industrial Robot Control by Means of Gestures and Voice Commands in Off-Line and On-Line Mode