Summary
2023
Session Number:C1L-3
Session:
Number:C1L-33
Reservoir Reinforcement Learning with Chaotic Boltzmann Machines Implemented on an FPGA
Sakino Yamato, Tamukoh Hakaru, Morie Takashi, Katori Yuichi,
pp.422-425
Publication Date:2023-09-21
Online ISSN:2188-5079
Summary:
Reinforcement learning has led to advances in areas such as attitude control in robotics and action planning in autonomous driving systems. However, reinforcement learning algorithms often face computational bottlenecks that limit their application to edge devices. In recent years, reservoir computing has emerged as a potential solution to this problem. One of these models updates the weight matrix using Q-learning, a popular reinforcement learning algorithm. This paper introduces reservoir reinforcement learning using chaotic Boltzmann machines on a field programmable gate array. We demonstrate the efficient implementation of reservoir computing with chaotic Boltzmann machines in hardware, achieving low computational costs. To demonstrate the effectiveness of this approach, we validate the proposed model using an action planning task in a two-dimensional environment consisting of nine rooms. The result shows that the model can be implemented in digital circuits with low power consumption and hardware resource savings while maintaining problem-solving capabilities. This result suggests efficient machine learning hardware in action planning could be applied to real-world scenarios.