# General policy mapping: online continual reinforcement learning inspired on the insect brain

arXiv: https://arxiv.org/abs/2211.16759 · HTML: https://mlsanity.com/paper/2211.16759/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 30 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Towards Continual Reinforcement Learning: A Review and Perspectives](https://mlsanity.com/paper/2012.13490/) (2020)
- [Optimizing for the Future in Non-Stationary MDPs](https://mlsanity.com/paper/2005.08158/) (2020)
- [Learning Shared Representations in Multi-task Reinforcement Learning](https://mlsanity.com/paper/1603.02041/) (2016)

## Abstract (arXiv)

We have developed a model for online continual or lifelong reinforcement learning (RL) inspired on the insect brain. Our model leverages the offline training of a feature extraction and a common general policy layer to enable the convergence of RL algorithms in online settings. Sharing a common policy layer across tasks leads to positive backward transfer, where the agent continuously improved in older tasks sharing the same underlying general policy. Biologically inspired restrictions to the agent's network are key for the convergence of RL algorithms. This provides a pathway towards efficient online RL in resource-constrained scenarios.