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A Novel Neuromorphic Processors Realization of Spiking Deep Reinforcement Learning for Portfolio Management

arXiv 2203.14159 ↗First posted 26 Mar 2022cs.LG cs.ARcited by 0

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Abstract (arXiv)

The process of continuously reallocating funds into financial assets, aiming to increase the expected return of investment and minimizing the risk, is known as portfolio management. Processing speed and energy consumption of portfolio management have become crucial as the complexity of their real-world applications increasingly involves high-dimensional observation and action spaces and environment uncertainty, which their limited onboard resources cannot offset. Emerging neuromorphic chips inspired by the human brain increase processing speed by up to 1000 times and reduce power consumption by several orders of magnitude. This paper proposes a spiking deep reinforcement learning (SDRL) algorithm that can predict financial markets based on unpredictable environments and achieve the defined portfolio management goal of profitability and risk reduction. This algorithm is optimized forIntel's Loihi neuromorphic processor and provides 186x and 516x energy consumption reduction is observed compared to the competitors, respectively. In addition, a 1.3x and 2.0x speed-up over the high-end processors and GPUs, respectively. The evaluations are performed on cryptocurrency market between 2016 and 2021 the benchmark.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 34 references that are in the map. First posted on arXiv 26 Mar 2022.

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References · 14

  1. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 399
  2. 2018SLAYER: Spike Layer Error Reassignment in Timecited by 257
  3. 2018Long short-term memory and learning-to-learn in networks of spiking neuronscited by 179
  4. 2017A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problemcited by 123
  5. 2019Deep Reinforcement Learning for Tradingcited by 73
  6. 2017More is Less: A More Complicated Network with Less Inference Complexitycited by 66
  7. 2020Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Controlcited by 44
  8. 2020Reinforcement co-Learning of Deep and Spiking Neural Networks for Energy-Efficient Mapless Navigation with Neuromorphic Hardwarecited by 32
  9. 2019Spiking Neural Network on Neuromorphic Hardware for Energy-Efficient Unidimensional SLAMcited by 19
  10. 2020Application of Deep Q-Network in Portfolio Managementcited by 11
  11. 2019Neural Population Coding for Effective Temporal Classificationcited by 10
  12. 2018Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradientscited by 4
2 more
  1. 2020A Spiking Neural Network Emulating the Structure of the Oculomotor System Requires No Learning to Control a Biomimetic Robotic Headcited by 4
  2. 2022Integrated Fault Diagnosis and Control Design for DER Inverters using Machine Learning Methodscited by 2

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