The MATLAB code for the memristor Q-network based reinforcement learning, for Wang, Z. et al. Reinforcement learning with analogue memristor arrays, Nature Electronics (https://dx.doi.org/10.1038/s41928-019-0221-6)

Authors(Alphabetic) : Daniel Belkin, Can Li, Wenhao Song, Zhongrui Wang

Advisors: Prof. J. Joshua Yang (Email: jjyang at umass dot edu, Website: http://www.ecs.umass.edu/ece/jjyang/); Prof. Qiangfei Xia (Email: qxia at umass dot edu, Website: http://nano.ecs.umass.edu);

The codes have been tested on Mathworks Matlab R2017b.

Run the script "main_cartpole.m" for the cart-pole problem. Run the script "main_mountaincar.m" for the mountain car problem. The codes that interface the hardware is not provided.

The real_array2 backend are dependent on the measurement system and hardware. The demo runs a multilayer perceptron Q-network. For construction of other advanced Q-network, please use the framework https://github.com/zhongruiwang/memristorCNN. The experimental data will be provided upon reasonable request.

Copyright (c) 2019 Department of Electrical and Computer Engineering, University of Massachusetts Amherst

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The MIT License (MIT)

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