# CERiL: Continuous Event-based Reinforcement Learning

arXiv: https://arxiv.org/abs/2302.07667 · HTML: https://mlsanity.com/paper/2302.07667/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 15 Feb 2023.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Model-Based Reinforcement Learning for Atari](https://mlsanity.com/paper/1903.00374/) (2019)
- [Stochastic Neural Networks for Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1704.03012/) (2017)
- [Event-Based Angular Velocity Regression with Spiking Networks](https://mlsanity.com/paper/2003.02790/) (2020)
- [Continuous-Time Model-Based Reinforcement Learning](https://mlsanity.com/paper/2102.04764/) (2021)
- [Representation Learning for Event-based Visuomotor Policies](https://mlsanity.com/paper/2103.00806/) (2021)
- [AirSim Drone Racing Lab](https://mlsanity.com/paper/2003.05654/) (2020)
- [Exploration of Reinforcement Learning for Event Camera using Car-like Robots](https://mlsanity.com/paper/2004.00801/) (2020)
- [EDeNN: Event Decay Neural Networks for low latency vision](https://mlsanity.com/paper/2209.04362/) (2022)

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

- [FLEET: Token-Based Feature Extraction for Event Camera-based Reinforcement Learning](https://mlsanity.com/paper/2608.16523/) (2026)

## Abstract (arXiv)

This paper explores the potential of event cameras to enable continuous time reinforcement learning. We formalise this problem where a continuous stream of unsynchronised observations is used to produce a corresponding stream of output actions for the environment. This lack of synchronisation enables greatly enhanced reactivity. We present a method to train on event streams derived from standard RL environments, thereby solving the proposed continuous time RL problem. The CERiL algorithm uses specialised network layers which operate directly on an event stream, rather than aggregating events into quantised image frames. We show the advantages of event streams over less-frequent RGB images. The proposed system outperforms networks typically used in RL, even succeeding at tasks which cannot be solved traditionally. We also demonstrate the value of our CERiL approach over a standard SNN baseline using event streams.