# A Dual-Memory Architecture for Reinforcement Learning on Neuromorphic Platforms

arXiv: https://arxiv.org/abs/2103.04780 · HTML: https://mlsanity.com/paper/2103.04780/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 49 references that are in the map.
- First posted on arXiv 5 Mar 2021.

## Builds on (most cited first, 12 of 12)

- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Deep Learning in Spiking Neural Networks](https://mlsanity.com/paper/1804.08150/) (2018)
- [A Survey of Neuromorphic Computing and Neural Networks in Hardware](https://mlsanity.com/paper/1705.06963/) (2017)
- [A comparison of Vector Symbolic Architectures](https://mlsanity.com/paper/2001.11797/) (2020)
- [Applications of Deep Learning and Reinforcement Learning to Biological Data](https://mlsanity.com/paper/1711.03985/) (2017)
- [Online Few-shot Gesture Learning on a Neuromorphic Processor](https://mlsanity.com/paper/2008.01151/) (2020)
- [A Complementary Learning Systems Approach to Temporal Difference Learning](https://mlsanity.com/paper/1905.02636/) (2019)
- [Winner-Take-All Computation in Spiking Neural Networks](https://mlsanity.com/paper/1904.12591/) (2019)
- [Resonator networks for factoring distributed representations of data structures](https://mlsanity.com/paper/2007.03748/) (2020)
- [Reward-based learning under hardware constraints - Using a RISC processor embedded in a neuromorphic substrate](https://mlsanity.com/paper/1303.6708/) (2013)

## Built on by (most cited first, 1 of 1)

- [AI/ML Algorithms and Applications in VLSI Design and Technology](https://mlsanity.com/paper/2202.10015/) (2022)

## Abstract (arXiv)

Reinforcement learning (RL) is a foundation of learning in biological systems and provides a framework to address numerous challenges with real-world artificial intelligence applications. Efficient implementations of RL techniques could allow for agents deployed in edge-use cases to gain novel abilities, such as improved navigation, understanding complex situations and critical decision making. Towards this goal, we describe a flexible architecture to carry out reinforcement learning on neuromorphic platforms. This architecture was implemented using an Intel neuromorphic processor and demonstrated solving a variety of tasks using spiking dynamics. Our study proposes a usable energy efficient solution for real-world RL applications and demonstrates applicability of the neuromorphic platforms for RL problems.