# Deep Binary Reinforcement Learning for Scalable Verification

arXiv: https://arxiv.org/abs/2203.05704 · HTML: https://mlsanity.com/paper/2203.05704/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 11 Mar 2022.

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

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks](https://mlsanity.com/paper/1702.01135/) (2017)
- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [Algorithms for Verifying Deep Neural Networks](https://mlsanity.com/paper/1903.06758/) (2019)
- [Binary Neural Networks: A Survey](https://mlsanity.com/paper/2004.03333/) (2020)
- [Verifying Properties of Binarized Deep Neural Networks](https://mlsanity.com/paper/1709.06662/) (2017)
- [Deep Neural Network Compression for Aircraft Collision Avoidance Systems](https://mlsanity.com/paper/1810.04240/) (2018)
- [An SMT-Based Approach for Verifying Binarized Neural Networks](https://mlsanity.com/paper/2011.02948/) (2020)
- [Efficient Exact Verification of Binarized Neural Networks](https://mlsanity.com/paper/2005.03597/) (2020)
- [Verification for Machine Learning, Autonomy, and Neural Networks Survey](https://mlsanity.com/paper/1810.01989/) (2018)
- [Reinforcement Learning with Iterative Reasoning for Merging in Dense Traffic](https://mlsanity.com/paper/2005.11895/) (2020)

## Built on by (most cited first, 2 of 2)

- [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)

## Abstract (arXiv)

The use of neural networks as function approximators has enabled many advances in reinforcement learning (RL). The generalization power of neural networks combined with advances in RL algorithms has reignited the field of artificial intelligence. Despite their power, neural networks are considered black boxes, and their use in safety-critical settings remains a challenge. Recently, neural network verification has emerged as a way to certify safety properties of networks. Verification is a hard problem, and it is difficult to scale to large networks such as the ones used in deep reinforcement learning. We provide an approach to train RL policies that are more easily verifiable. We use binarized neural networks (BNNs), a type of network with mostly binary parameters. We present an RL algorithm tailored specifically for BNNs. After training BNNs for the Atari environments, we verify robustness properties.