# Binarized P-Network: Deep Reinforcement Learning of Robot Control from Raw Images on FPGA

arXiv: https://arxiv.org/abs/2109.04966 · HTML: https://mlsanity.com/paper/2109.04966/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2021.

## Built on by (most cited first, 3 of 3)

- [Learning Quantized Continuous Controllers for Integer Hardware](https://mlsanity.com/paper/2511.07046/) (2025)
- [Differentiable Weightless Controllers: Learning Logic Circuits for Continuous Control](https://mlsanity.com/paper/2512.01467/) (2025)
- [Robust Iterative Value Conversion: Deep Reinforcement Learning for Neurochip-driven Edge Robots](https://mlsanity.com/paper/2408.13018/) (2024)

## Abstract (arXiv)

This paper explores a Deep Reinforcement Learning (DRL) approach for designing image-based control for edge robots to be implemented on Field Programmable Gate Arrays (FPGAs). Although FPGAs are more power-efficient than CPUs and GPUs, a typical DRL method cannot be applied since they are composed of many Logic Blocks (LBs) for high-speed logical operations but low-speed real-number operations. To cope with this problem, we propose a novel DRL algorithm called Binarized P-Network (BPN), which learns image-input control policies using Binarized Convolutional Neural Networks (BCNNs). To alleviate the instability of reinforcement learning caused by a BCNN with low function approximation accuracy, our BPN adopts a robust value update scheme called Conservative Value Iteration, which is tolerant of function approximation errors. We confirmed the BPN's effectiveness through applications to a visual tracking task in simulation and real-robot experiments with FPGA.